scientific research and design thinking

Princeton Correspondents on Undergraduate Research

Design Thinking in Research

I remember it like it was just yesterday. The steps to the scientific method: Question. Research. Hypothesis. Experiment. Analysis. Conclusion. I can actually still hear the monotonous voices of my classmates reciting the six steps to the content of the middle school science fair judges.

For our middle school science fair, I had created a web-based calculator that could output the carbon footprint of an individual based on a variety of overlooked environmental factors like food consumption and public transportation usage. Having worked on the project for several months, I was quite content when I walked into our gym and stood proudly next to my display board. Moments later the first judge approached my table. Without even introducing himself, he glanced at my board and asked me, W here’s your hypothesis? Given the fact that my project involved creating a new tool rather than exploring a scientific cause-effect relationship, I told him that I didn’t think a hypothesis would make sense for my project. To my dismay, he told me that a lack of hypothesis was a clear violation of the scientific method, and consequently my project would not be considered.

This was quite disheartening to me, especially because I was a sixth grader taking on my very first attempt at scientific research. But at the same time, I was confident that the scientific method wasn’t this unadaptable set of principles that all of scientific research aligned to. A few years later, my suspicions were justified when my dad recommended I read a book called Design Thinking  by Peter Rowe. While the novel pertains primarily to building design, the ideas presented in the book are very applicable in the field of engineering research, where researchers don’t necessarily have hypotheses but rather have envisioned final products. Formally, design thinking is a 5-7 step process:

• Empathize – observing the world, understanding the need for research in one’s field
• Define – defining one particular way in which people’s lives could be improved by research
• Ideate – relentless brainstorming of ideas without judgment or overanalysis
• Prototype – sketching, modeling, and outlining the implementation of potential solutions
• Choose – choosing the solutions that provide the highest level of impact without jeopardizing feasibility
• Implement – creating reality out of an idea
• Learn – reflecting on the results and rethinking the process for endless improvement

But more generally, advocates of design thinking call it a “method of creative action”. In design thinking, researchers are not concerned about solving a particular problem, but are looking more broadly at a general solution. In fact, design thinkers don’t even necessarily identify a problem or question (as outlined in the scientific method); they are more concerned about reaching a particular goal that improves society.

This view of research is particularly insightful especially in disciplines beyond the scientific realm. One aspect that particularly appeals to me is the relative importance placed on the solution’s impact. In design thinking, researchers empathize. They understand at a personal level the limitations of current solutions. And once they implement their solutions, they learn from the results and dive right back into the entire process. Societal impact is their overall goal – an idea that carries over into humanities and social science research.

The most important aspect, in my opinion, is the freedom of design thinking. In design thinking, the ‘brainstorming’ process and the solution are given the most attention. Design thinkers are primarily concerned with the overall effectiveness of potential solutions, worrying about the individual details afterwards. This inherently promotes a creative and entrepreneurial research process. Combined with the methodology and analysis components of the scientific method, the principles of design thinking help research ideas blossom into realities. In a sense, design thinking repackages the scientific method to create a general research process in non-scientific fields. Artists, fashion designers, and novelists all use design thinking when creating their products.

So while I certainly didn’t impress the judges that day at the science fair, I did learn something far more resourceful than a display board could teach. In order to complete a satisfying research project, one doesn’t need to rigorously follow a well-outlined protocol. Often, all one needs is the drive to design creative and impactful solutions.

— Kavi Jain, Engineering Correspondent

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Integrating design thinking with sustainability science: a Research through Design approach

1 School of Earth and Environmental Science, The University of Queensland, Brisbane, QLD Australia

2 Centre for Policy Futures, The University of Queensland, Brisbane, QLD Australia

Melanie Maher

3 Visual Communication Design, Brisbane, Australia

Samuel Mann

4 Otago Polytechnic, Dunedin, New Zealand

Clive A. McAlpine

Associated data.

Design disciplines have a long history of creating well-integrated solutions to challenges which are complex, uncertain and contested by multiple stakeholders. Society faces similar challenges in implementing the Sustainable Development Goals, so design methods hold much potential. While principles of good design are well established, there has been limited integration of design thinking with sustainability science. To advance this integration, we examine the process of designing MetaMAP: an interactive graphic tool for collaborating to understand social–ecological systems and design well-integrated solutions. MetaMAP was created using Research through Design methods which integrate creative and scientific thinking. By applying design thinking, researchers and practitioners from different backgrounds undertook multiple cycles of problem framing, solution development, testing and reflection. The testing was highly collaborative involving over 150 people from diverse disciplines in workshops, case studies, interviews and critique. Reflecting on this process, we discuss design principles and opportunities for integrating design thinking with sustainability science to help achieve Sustainable Development Goals.

Electronic supplementary material

The online version of this article (10.1007/s11625-018-0618-6) contains supplementary material, which is available to authorized users.

Introduction

This paper describes the application of design methodology to sustainability goals. The United Nations’ Sustainable Development Goals (SDGs) provide a common direction (Griggs et al. 2013 ) for guiding collaborating towards a sustainable future. However, several conceptual, institutional, and communication barriers restrict our progress in achieving SDGs in a cohesive fashion (Maher et al. 2018b ). Integrating design-based approaches with sustainability science may help to overcome many of the current challenges limiting progress towards SDGs. We propose that Research through Design (Zimmerman et al. 2010 ) is well suited to achieve sustainability goals by applying design approaches in a research context. Research through Design solves complex and contested challenges by taking a holistic approach and developing ideas through many iterations of proposition and critical reflection (Glanville 2007 ). To give this design approach some context, we describe the process of designing MetaMAP as a case study.

Case study introduced

MetaMAP is an interactive graphical tool which supports collaborative investigation and design for achieving SDGs. It helps diverse users to integrate their thinking, understand sustainability challenges holistically, and develop well-integrated solutions (Fig.  1 ). A detailed description with worked examples and further applications can be found in Maher et al. ( 2018a ). In brief, the structure and application of the MetaMAP framework help users gain insight by seeing relationships among parts of the natural environment, built environment and society across multiple spatial and temporal scales. It provides an inclusive framework to help people from different backgrounds integrate their diverse perspectives on sustainability issues into a common understanding. Armed with this holistic perspective, guided process help users to identify points of leverage and design well-integrated sustainability initiatives.

MetaMAP framework notated to identify core elements. The intent of this paper is not to describe MetaMAP in detail, rather to use its derivation as a case study in the application of the design thinking approach in addressing Sustainable Development Goals

Global society faces substantial challenges in transforming our relationship with the natural environment. To support this transformation, the United Nation’s Sustainable Development Goals (SDGs) provide a common direction (Griggs et al. 2013 ). However, our traditional approach for building knowledge and solving problems is poorly suited to the unique nature of sustainability challenges (Maher et al. 2018a ; Sterling 2009 ). Sustainability problems are complex and contentious and transcend the boundaries of disciplines and nations (Brandt et al. 2013 ). Many current institutional structures (e.g. strict hierarchies) and thinking paradigms (e.g. reductionist thinking and reliance on single metrics) lead us to look at problems in isolation (Siebert 2011 ). This causes many sustainability initiatives conceived in theory to fail in practice due to issues outside the scope of consideration. Sustainability initiatives designed in isolation lack synergy, so advances in one area may setback others. This siloed approach makes it difficult to build wide support for initiatives which only address narrow interests.

Growing concern with the isolated approaches often used for achieving SDGs is prompting calls for more integrated approaches. The necessary integration takes several forms, including understanding the context of sustainability challenges more holistically, connecting people and ideas across social and institutional divides (Khalili et al. 2017 ), and understanding interactions among SDGs (Stafford-Smith et al. 2017 ). The aim of such an approach is to produce sustainability initiatives which are more effective, more efficient and better aligned with diverse interests.

To support this, researchers have developed more integrated conceptual frameworks for holistically understanding sustainability challenges and their context. By organising concepts meaningfully, these frameworks help to guide enquiry and provide a foundation for stronger collaboration across disciplines (Heemskerk et al. 2003 ). Several well-established frameworks have become a foundation of research and education in sustainability science including resilience (Berkes and Ross 2013 ; Folke 2006 ), planetary boundaries (Rockström et al. 2009 ) and ecosystem services (Abson et al. 2014 ; Bennett et al. 2009 ). Partelow ( 2015 ) calls for a more thorough and deliberate integration of Ostrom’s social–ecological systems framework (Ostrom 2009 ) with sustainability science. In contrast to other highly technical and rigid frameworks, Hall et al. ( 2017 ) translate Luhmann’s social system theory to the development of sustainability solutions. Of particular interest is the concept of resonance—the “sweet spot…[where] all the factors…come together beautifully” (Hall et al. 2017 ). It represents a convergence of mutually reinforcing feedback loops across multiple sectors of society. This can be applied strategically to align the interests of diverse stakeholders which is critical for long-term success of sustainability initiatives. We examine several of these frameworks, their benefits and limitations elsewhere (Maher et al. 2018a ).

Building on these frameworks, many tools have been developed to support decision making for sustainability. Recent developments in this area include the SDG Interlinkages Framework (Nilsson et al. 2016 ) which helps to understand typical interactions among selected SDGs at a general level. This supports the argument for achieving SDGs in an integrated fashion. However, it may be of less value for informing specific sustainability initiatives where actual conflicts and Synergy depend on unique social, ecological and political circumstances. The System Dynamics-based iSDG family of models is tools for comparing and choosing among competing sustainability initiatives (Collste et al. 2017 ). This is valuable for maximising multiple known sustainability outcomes in alignment with stakeholder demands. However, the initiatives being assessed must be predetermined using other means. The graphical multi-agent decision-making model (GMADM) (Khalili et al. 2017 ) also depends on assumed plans or portfolios. While the tool is well formulated, focusing on comparative analysis has limited value for challenging assumptions or generating new ideas on which to build creative and innovative solutions. Tools that prioritise analysis restrict lateral thinking—our ability to perceive and re-conceptualise things in fundamentally new ways (de Bono 1970 ). This means outcomes tend to optimise and reinforce existing ways of being rather than transformation. In short, these are tools for analysis, not design. The demands on tools for addressing SDGs are tremendous. They must: aggregate knowledge across disciplines (Wiek et al. 2012 ; Partelow 2015 ), educate users in systems thinking (Stafford-Smith et al. 2017 ), bridge across geographical and political scales (Collste Collste et al. 2017 ), link theory with policy (Khalili et al. 2017 ), incorporate social–ecological systems with ecosystems services (Partelow 2015 ), link theory with real-world projects (Lang et al. 2017 ), connect intellectual concepts with shared social values (Lang et al. 2017 ; Hall et al. 2017 ) and promote new ways of thinking (Hall et al. 2017 ). To be widely used, tools for addressing SDGs must be intuitive, practical and suitable for a wide diversity of users with limited specialist training. Underlying these requirements is a need for tools which support the “creative coordination of resources, capacities, and information into new ways of seeing the system which are useful for designing strategic interventions in the setting” (Hall et al. 2017 ). While new tools are of value for strategic decision making, they do little to support the creativity and design required.

The need to advance design approaches for achieving sustainability goals

Integrating design-based approaches with sustainability science may help to overcome many of the current challenges limiting progress towards SDGs. While these types of ‘wicked’ challenges (Bojórquez-Tapia et al. 2017 ) are relatively new to science, other disciplines face similar types of challenges and have well-established methods for doing so. Design disciplines, especially architecture, commonly face “…incalculably complex (and ambiguously defined) problems, bringing them to simple resolution: designers typically make one object that satisfies a myriad of often contradictory and ill-defined requirements” (Glanville 2007 ). Architects have highly developed techniques for making sense of complex situations, generating innovative strategies for solving problems, integrating multiple perspectives and achieving many goals simultaneously (Dorst 2011 ; Rodgers and Yee 2014 ). These both complement and are supported by recent advances in sustainability science (Nassauer and Opdam 2008 ). Integrating design approaches with sustainability science offers substantial opportunities for achieving SDGs efficiently and effectively amid real-world complexity.

Despite calls for more design approaches to sustainability research and practice (Future Earth 2014 ; Kolko 2009 ), design approaches are rare among research on solutions for the SDGs. This is likely because design is seen as mysterious, even ‘magical’ (Glanville 2007 ) by those who are unfamiliar with its methods. It also follows a different logic and methodology to the sciences which have dominated our progress in sustainability. However, design and sustainability science are highly compatible and integrating them can produce sustainability initiatives which are effective, transformational, and well integrated into their unique social–ecological context. The contribution of this paper is to go beyond that magic by introducing the attitudes, processes and principles that make design work.

To advance this integration, sustainability researchers and practitioners need a better understanding of design principles, design methods, and their value for supporting sustainability science. In this paper, we provide an overview of Research through Design methodology, its value for achieving SDGs and its compatibility with sustainability science. We then examine a case study of a Research through Design project which develops new tools for achieving SDGs. We then unpack the design process for easier comprehension and integration with sustainability science. We describe five stages of design, each a cycle of: (a) problem framing, (b) solution development, (c) testing and (d) critical reflection. We then briefly describe the primary research outcome: MetaMAP—a graphical tool for collaborating to understand social–ecological systems holistically and design well-integrated initiatives (described in detail in Maher et al. 2018a ). Reflecting, we distil some core design principles applied in the case study and discuss implications for their wider application to achieve SDGs. Finally, a brief conclusion identifies limits and opportunities to extend both Research through Design methodology and MetaMAP.

Research through design methodology

Research through Design (RtD) translates methods and mental processes from design practice to a research environment (Zimmerman et al. 2010 ). Design is a process of producing simple and effective responses to complex and vague problems that span across disciplines and stakeholder groups. Design has been described as a process of “…reflection-in-action” (Kennedy-Clark 2013 ), and as “…organizing complexity or finding clarity in chaos…” (Kolko 2009 ). It takes a holistic perspective, drawing together different perspectives on problems and their context, technology, human needs, empathy with users and stakeholders to create aesthetic artefacts, which can be rich in meaning.

Research through Design offers many advantages for research on sustainability and achieving SDGs. By taking a holistic perspective and expanding the framing of the context of the problem, RtD methods can create better integrated sustainability initiatives—where components work in harmony with each other and their context. Developing a proposal through multiple iterations can help a single initiative to achieve multiple goals simultaneously. By focusing on synthesis over analysis, RtD can create usable artifacts, fit for a specific time and place in the real world. Engaging diverse stakeholders in a project enriches outcomes and helps to secure wider support for its findings. Each of these approaches helps to address critical limits to progress on the SDGs. These principles are demonstrated in practice through the case study of MetaMAP. At the end of this article, we discuss some fundamental design principles of particular relevance to achieving SDGs. More detail can be found elsewhere (Faste and Faste 2012 ; Kennedy-Clark 2013 ; Moloney 2015 ; Rodgers and Yee 2014 ; Zimmerman et al. 2007 , 2010 ), but usually without specific reference to sustainability.

Table  1 provides a summary of some typical differences between the focus of traditional scientific and design approaches to research (Hes and Du Plessis 2014 ; Sterling 2009 ). Readers will see that sustainability science is shifting towards the right column. We will demonstrate that this evolution can be accelerated by integrating design and sustainability science.

Table 1

Differences between the typical focus of traditional mechanistic and design approaches to research

Design principles

There are several design principles which help designers to solve wicked problems and can help to achieve SDGs. These principles act as rules of thumb and attitudes which help to guide design processes towards innovative, valuable, and well-integrated outcomes (e.g. Rodgers and Yee 2014 ). Five design principles of particular value to achieving SDGs are: broad problem framing supporting multiple goals; maximise synergy, minimise compromise; integrating diverse perspectives; thinking visually; and multiple feedback loops. These are demonstrated throughout the case study below and expanded in detail following it.

Case study: designing MetaMAP using a research through design approach

We now examine a case study, which applies Research through Design methods to develop MetaMAP: a graphical tool for achieving SDGs. This begins with an overview and structure of the Research through Design process. We then follow the narrative of designing MetaMAP through five stages, each containing four types of design activity. Throughout this process, we recorded how the design developed, feedback from collaborative testing and our own critical reflections on the process. We end this section with an overview of MetaMAP, who it is for and how it works. In total, this provides a successful demonstration of design methods for research supporting SDGs and a vehicle to discuss methodological development and broader applications.

MetaMAP aims

It is not possible to strictly predefine the aims of an RtD project as aims continuously evolve in response to new understandings gained as the design advances. Initially, we aimed to design a digital platform for integrating and sharing an ecosystem of knowledge and action for sustainability. As the design process uncovered new opportunities and needs, our aims shifted to creating graphical tools for collaborating to understand and visualise social–ecological systems and design well-integrated initiatives to achieve SDGs. This reframing is evident in the narrative below.

Overview and structure of the research through design process

Designing MetaMAP was a significant undertaking which addressed uncertain, vaguely defined and conflicting goals and a scope which transcended academic disciplines. To synthesise this complexity into harmonious resolution, we developed MetaMap using Research through Design methodology. The design was advanced through several stages, each building on the previous. The stages were advanced simultaneously so that progress in one aspect could inform others (Fig.  2 ). This approach is a common design strategy and was considered superior to sequential stages which remove the possibility of feedback loops (Moloney 2015 ). We have described the process as a linear flow due to the limits of text. However, the process is non-linear and the reader should be aware of the integration and interaction of these stages.

Five parallel stages of design with multiple avenues of feedback

Each stage can be considered as a cycle of four design activities: (a) problem/opportunity framing, (b) solution development, (c) testing and (d) critical reflection as shown in Fig.  3 . Each cycle helps to “…re-define the problems, possible solutions, and the principles that might best address them” (Amiel and Reeves 2008 ). In practice, these design activities were not strictly predefined in order to take advantage of inspiration and opportunities for critique as they arose. This agile process involving many layers of feedback loops is a fundamental principle of design for addressing wicked problems (Faste and Faste 2012 ). It has significant value for achieving SDGs amid real-world complexity. We now introduce the four types of design activities in each cycle.

Each stage can be considered a cycle of four design activities

Activity type a: problem/opportunity framing

The first design activity in each cycle involved framing the problem, identifying opportunities and (re)defining limits. The designers considered questions such as:

• What issues should we be considering?
• How might they relate?
• How have similar challenges been addressed elsewhere?
• Where are the synergies trade-offs and priorities in this unique case?
• Who are the stakeholders (in the broadest sense)?
• What other benefits might we gain beyond our initial objectives?
• What other disciplines might provide guidance?

The specific methods used in this design activity varied in each stage. Common activities included: reviewing literature, semi-structured interviews, analysing precedents, concept mapping, pin boards and writing design briefs.

Activity type b: solution development

Based on framing of the problem/opportunity space, we designed possible solutions. We represented concepts visually through sketches, diagrams, paper-based prototypes and digital mockups. Methods for generating and developing innovative ideas varied greatly throughout the project. Brainstorming was also used to produce many concepts rapidly without prejudging them. When design ideas became stagnant or lacked originality, we applied lateral thinking approaches to reinvigorate the process (de Bono 1970 ). This involved browsing diverse collections of semi-related images and adapting ideas from existing precedents to new applications. Concept mapping helped us to see existing and potential relationships between different objectives and strategies. The images we created helped to visualise possible implications of our ideas and to communicate ideas to others for testing, application and critique. Figure  4 provides an overview of how MetaMAP evolved across five stages of design.

Overview of design concepts in the evolution of MetaMAP from sketch to designed digital interface (details of each stage given in text and figures in following sections)

Activity type c: testing

One purpose for testing design prototypes was to understand which aspects of the tool were effective, which were not, and to identify fruitful opportunities for further development. Even more important was to discover the unknown unknowns—the problems and opportunities we did not know to look for. For this reason, it was important not to restrict the type of feedback we could receive from the testing process.

Most of the testing activities were collaborative involving workshops, case studies and semi-structured interviews. In total, these involved over 150 people from diverse disciplines including: Ecology, Human geography, Earth Sciences, Architecture, Environmental management, Educational psychology, Participatory GIS, Business, Human–Computer Interaction (HCI), Sustainable practice and Conservation Biology. In these tests, users applied the MetaMAP prototype in different ways to sustainability projects which varied greatly in scale and type. We observed the activities and took notes during and immediately afterwards. We also invited workshop participants to comment directly on the MetaMAP framework and its application.

Activity type d: critical reflection

Collaborative testing helped to validate our assumptions about how potential users would use MetaMAP. Following the tests we asked: How did participants respond to the framework? Where were they uncertain about its use? What did they find most valuable? What insights into their own work did it help them to gain? Sometimes it became clear that a previously rejected option would have performed better.

By reflecting critically on the tests, we learned about the problems and opportunities which helped to refine our judgement in making design decisions. Skilful and informed judgement is an essential part of the process. As such, design is a subjective, not objective undertaking—it involves pursuing a deliberate intention to shape the future based on a set of values. The designer is part of the process and not distinct from it.

Case study narrative: five stages of design

We now describe each stage of design in turn. For each we outline: (a) how the problem and opportunities were framed at that time; (b) how the design evolved in response; (c) how we tested it, including main objectives and who was involved in the task undertaken; and (d) critical reflections on the design and testing.

Stage (1) Design sketching

(1a) problem/opportunity framing.

Creating the right circumstances to foster inspiration is an important part of creative endeavours, including science (Scheffer 2014 ). MetaMAP began when I (First Author), as a recent graduate confronted by the potential and challenges of the SDGs, went for a long walk alone thinking about the future. I wanted to find the best way I could to help build a sustainable future. I knew of many sustainability challenges and opportunities to contribute, but I did not know how they were related or where I could have the biggest impact. I thought that many others must be having similar challenges at different scales, whether planning a career, designing multinational policy or just choosing which product to buy. In response, I imagined a digital world which showed how all parts of our social–ecological system were related like a giant network—constellations of ideas which I could explore to find where I could have greatest influence over a sustainable future. If we could build this digital world, what would it contain, how could it be navigated, and who would use it?

(1b) Solution development

In its first conception, the design took the form of a colour-coded system model which showed relationships among diverse content on sustainability (Fig.  5 ). Content was to be added by users and organised by how it is acted upon: learn, act, collaborate and donate. The systems model was considered as an interactive interface which would reorganise itself as users explore chains of interaction among issues. Selecting a topic would access detailed information contributed by an online community. Many of these fundamental characteristics remain in MetaMAP despite evolution of the design through multiple stages with diverse collaborators.

The first concept sketch

(1c) Testing

Throughout this early stage, it was important to identify and test a wide range of possible approaches before committing to one. This involved creating many hundreds of drawings exploring different ideas (Fig.  6 ). Sketching helped us to develop and critique ideas rapidly through many iterations. Representing ideas visually allowed us to see potential consequences which we may have otherwise overlooked. This testing often involved considering the challenge from one perspective, drawing ideas which came to mind, and then critiquing them from several other perspectives. This is commonly known as a ‘conversation with the self via the pen’ (e.g. Kennedy-Clark 2013 ). The growing collection of drawings provided a point of comparison for later developments. These methods continued to be used throughout the entire design process for developing and testing ideas.

Collection of concept sketches developing ideas for MetaMAP. Recurring themes included social–ecological systems, exploration of different hierarchies among parts, landscape metaphors, graphic means of managing complexity and tools to help users navigate an ecosystem of knowledge

(1d) Reflections

This stage also provided insight on issues which would shape future developments. There were considerable challenges in representing many abstract sustainability concepts vividly. As such, the designs constantly shifted between highly abstract representations of sustainability ideas and more tangible ‘landscapes’ which expressed sustainability issues through metaphor. The metaphorical landscapes were based on familiar elements of built and natural environments and fostered rapid understanding.

We found that systems approaches were much better suited to synthesising and understanding sustainability issues than hierarchical frameworks. Systems models are valuable for understanding relationships, but without an underlying structure, they can be very difficult to navigate. As such, sustainability scientists and practitioners may benefit greatly from organising systems models, but how? An organising framework should help users to understand them rapidly, build familiarity over time and inspire insight into higher order phenomenon. On the other hand, any such underlying framework would risk excluding ideas which are important for sustainability. Different disciplines and cultures have different ways of understanding the relationship between people and nature. We realised that to bring these together into a common framework would require input from people of different backgrounds.

Stage (2) Student workshop

(2a) problem/opportunity framing.

This stage focused on developing the conceptual framework used to organise and navigate the envisaged ‘digital world’ of sustainability ideas and action. Following Stage 1 and an extensive review of literature and precedents, we developed a manifesto which set out the long-term ambitions of the project (See Text Box  1 ). This informed a design brief for the project (not shown) which set objectives for several aspects of MetaMAP including: knowledge transfer and development, conceptual frameworks and concepts, users and how they interact, social empowerment, content, communication, interface, governance and management, aesthetic and technical requirements and fostering social-environmental impact beyond the platform. It was continuously refined following insight gained in later stages.

(2b) Solution development

Here, we focused on refining the framework used to give structure to systems models. It needed to be intuitive, and encompass a wide diversity of perspectives on sustainability issues. We developed a circular framework which was overlaid by concept maps of sustainability challenges. The circle was divided into three primary realms: the natural environment the built environment and society. These were further subdivided into categories as presented in Fig.  7 . These realms and categories helped to locate particular issues within a concept map.

MetaMAP circular prototype

(2c) Testing

We ran a 2 h workshop to test the effectiveness of the framework for learning and guiding exploration of sustainability ideas. We investigated if participants could understand the framework. Did it help them to think in systems? Did it aid collaboration? Did it facilitate insight? The workshop involved 104 s year Architecture students with no prior education in systems theory, and no prior tertiary education in sustainability. Participants selected a familiar building and considered the impacts that it had on its broader social–ecological context. They represented each impact with a labelled arrow connecting the building in the centre to the category it affected in the edge. Facilitators noted the students’ insight of how the building influenced the social–ecological system and compared it with student insight from previous activities without the framework.

(2d) Reflections

We found that the structured categories and realms were especially valuable in helping participants tap to into their existing general knowledge and consider issues they may have otherwise overlooked. By working together on a single framework, participants could build on each other’s ideas and contribute to a greater shared understanding of the topic. The structured framework and semi-guided process also helped participants to gain insight on higher order properties of the system. For example, one group drew a circle around the entire system and said “When you think deeply, everything is impacting on each other!”—a potentially transformational paradigm shift in young architects. It also helped them to apply systems thinking rapidly without prior knowledge.

The arrows showing relations were useful to guide thinking during the task, but were difficult to translate later if not well noted. A more intuitive system would allow faster understanding by a broader audience, easier application, and more developed high order thinking in less time. We realised that improving the intuitiveness of the framework should be a priority throughout the project.

Stage (3) Ecovillage case study

(3a) problem/opportunity framing.

Next, we broadened the literature review and conducted a number of expert interviews which highlighted the importance of scale when addressing sustainability challenges (Wu 2013 ). It is often necessary to examine relationships among issues occurring at different physical scales, i.e. ‘think globally, act locally’. In addition, purely objective approaches to sustainability separate people from the systems they influence. In contrast, other approaches which synthesize objective (user outside the system) and subjective (user within the system) perspectives can be more comprehensive and empowering (e.g. Integral theory) (Brown 2007 ). This would be a valuable addition to a framework for organising diverse content on sustainability. We also continued to increase the intuitiveness of the framework by representing abstract ideas through familiar visual metaphors.

(3b) Solution development

We advanced the design substantially in response to our growing understanding of the challenge and potential solutions as in Fig.  8 . A physical scale was introduced ranging from personal up to global. To aid understanding, the framework was represented as a portion of a globe with smaller scales in the foreground and larger scales on the distant horizon. The horizon edge was visualised as a stylised landscape ranging from natural through industrial to urban. This reinforced both the physical scale and different elements of the social–ecological system. The individual user was represented in the foreground from which point connections could be made to the system they are examining. The MetaMAP framework is intended to support the SDGs, but at this point we chose not to include the 17 goals explicitly to reduce complexity for unfamiliar users.

MetaMAP prototype

(3c) Testing

The next cycle of testing sought to understand how the framework might help to guide learning and exploration of a complex sustainability-driven initiative. Could it help people unfamiliar with systems approaches to visualise interactions across scales and sectors? Over 6 weeks, 15 Masters of Architecture students used the MetaMAP framework to conduct a holistic case study of Currumbin Ecovillage: a community in the Gold Coast hinterland seeking to live sustainably and regenerate the local ecosystems (O’Callaghan et al. 2012 ). These students had extensive design training but no prior education in systems approaches and little sustainability education. Each student selected a different aspect of the Ecovillage to examine over the 6 weeks period. For example, some studied water or energy systems while others considered construction materials or community culture. Students visited the site, conducted independent research and collaborative sessions and an oral presentation. Throughout the project, the MetaMAP framework was used to describe relationships between the Ecovillage and different aspects of our social–ecological system across scales (e.g. Fig.  9 ). In a final workshop, the class reflected on the project and critiqued the framework.

Page from student assignment applying MetaMAP prototype to understand how water and waste systems within the Ecovillage shape the broader social–ecological system across scales. Each page of the assignment highlighted a subset of issues (crimson on the left) and described how they relate (text on right).

Source: Rehn 2016 (with permission)

(3d) Reflections

As a whole, students took several sessions to understand the MetaMAP framework and systems thinking approach. Once familiar, however, all 15 students applied the framework with great enthusiasm and intellectual rigour. All students identified various paths through which the Ecovillage shaped the natural environment, the built environment and society. This helped them to examine details of the project in the context of the broader social–ecological system. All identified cross-scale interactions and came to appreciate how their role as designers contributed to global challenges. One said that without the framework “I wouldn’t have known where to start”. Several students identified feedback loops in their system without being introduced to the concept. Students used the MetaMAP framework in their presentations which proved valuable in communicating clear narratives through complex systems—they mapped the path of their argument visually. Many students developed unique visual methods for describing phenomena they identified in their system (e.g. ‘ripple effects across scale, sub systems, overall impacts, effects of time). Despite this, the sheer complexity of the systems led to concept maps which were difficult for others to follow. This visual complexity needs to be managed carefully. These tests revealed opportunities to develop guided processes to help unfamiliar users create concept maps. They also reinforced that understanding the specific context of use is essential for designing sustainability initiatives.

Stage (4) Framework comparative analysis

(4a) problem/opportunity framing.

MetaMAP had so far proved highly effective in sustainability education. However, by focusing on parts of the social–ecological system and how they relate, some important emergent properties were left out of the framework. These included concepts such as carrying capacity, resilience and ecosystem services. Analysis of the existing conceptual frameworks of sustainability identified other important perspectives on sustainability which we then aspired to incorporate into MetaMAP.

(4b) Solution development

During this time, our understanding of MetaMAP expanded into the digital environment. We now envisaged three nested elements: (1) the underlying conceptual framework and concept maps developed through previous stages; (2) a digital interface through which users navigated content visually; and (3) a diverse community of sustainability ambassadors collaborating through online networks. Figure  10 describes these nested elements and several academic and practical disciplines which contribute theory to the design.

Nested elements of MetaMAP and the disciplines which informed their design

(4c) Testing

Seeking greater comprehensiveness, we next compared MetaMAP with existing conceptual frameworks related to sustainability to identify important concepts which it so far neglected. This exercise was carried out visually as diagrams can be an effective way of expressing paradigms of thought and the concepts of which they are composed. First, we compiled a collection of leading conceptual frameworks. We sourced these from prominent institutions (e.g. Millennium Ecosystem Assessment 2005 ; Rockström et al. 2009 ) and Sustainable Lens (Mann 2011 ). We then attempted to map each framework onto MetaMAP as shown in the examples in Fig.  11 .

Examples of MetaMAP expressing existing conceptual frameworks of sustainability

(4d) Reflections

The majority of frameworks were easily transcribed onto the MetaMAP framework. These included Planetary Boundaries, the United Nations Sustainable Development Goals (SDGs), Ecosystem Services, People-Profit-Planet (Sosik and Jung 2011 ) and Complex Systems (van Kerkhoff 2014 ) among many others. Other Frameworks were able to be mapped only by developing the design of MetaMAP. For example, Social–Ecological Fit examines how relationships between people correlate with relationships among the elements of the social–ecological system which they manage (Guerrero et al. 2015 ). We were able to support the concept of Social–Ecological Fit in MetaMAP by adding a new ‘layer’ for people overlaying the social–ecological system (Fig.  11 , bottom left). The concept of time remained important yet challenging to include in MetaMAP. Eventually we solved this by ‘tagging’ each relationship link in a concept map with the duration it takes to unfold.

Stage (5) Researcher workshops

We then sought to test MetaMAP with a more experienced user group and greater diversity of sustainability projects.

(5a) Problem/opportunity framing

MetaMAP had proved to be quite comprehensive in the previous stage of testing, and able to synthesise most of the frameworks and concepts tested. Most of the issues it failed to include were social processes which are important for sustainability rather than elements of the social–ecological system. For this next stage, the underlying conceptual framework remained unchanged, but we developed guided processes to help new users apply it effectively.

(5b) Solution development

We expanded the previous model of MetaMAP which included three nested elements to include a fourth element: guided process of mapping sustainability issues. We conceived of this process as a combination of workshop facilitation and strategic design methods. This allowed us to incorporate a suite of important sustainability theory related to social processes for achieving sustainability goals which, until now, had been overlooked.

(5c) Testing

We then sought to test MetaMAP with a more experienced user group and greater diversity of sustainability projects. As before, we aimed to identify unforeseen problems but also had guiding questions: could we increase the speed at which new users could understand and apply MetaMAP? Could it cope with a wide variety of applications? Could it help users to position their work in the context of broader sustainability initiatives such as the United Nations Sustainable Development Goals?

To examine these issues, we ran a series of three, 2 h workshops with a total of 26 participants. To ensure that MetaMAP could support a wide diversity of perspectives on sustainability we engaged participants from many different disciplines with experience ranging from undergraduate to experienced researchers. Workshops began with a brief introduction to the MetaMAP framework. The session leader projected the framework onto a whiteboard and drew an example concept map with input from participants. Each participant then mapped their current sustainability project on a sheet of transparent paper overlaying an A3 print of MetaMAP. The projects were diverse including: impacts of farming on creeks in Otago (Fig.  12 ), improving bicycle networks in Dunedin, social housing policy for children’s wellbeing and water scarcity as a factor in intrastate conflict. The mapping loosely followed a guided process shown in Text Box  2 . Participants then discussed their work in groups of two or three. They overlaid their transparent sheets to compare models and identify issues of common interest. For example, farming practices in Otago and community forest regeneration schemes can both support the health of local creeks. Finally, participants placed their conceptual model over a version of MetaMAP which had the 17 United Nations Sustainable Development Goals (SDGs) mapped on it. This helped them to identify which SDGs their project might contribute to and how.

Workshop application of MetaMAP showing point of leverage over food system and implications for the SDGs

(5d) Reflections

The diversity of the three workshops provided a rich foundation for reflective learning. By introducing principles of design thinking, the guided process helped turn a previously descriptive or reactive activity into a proactive one. Instead of merely describing a system disconnected from the participants or identifying problems, MetaMAP helped users to see themselves in the system. Many participants identified strategies within their power which could help to transform the system towards greater sustainability. When a framework was not used, participants had difficulty organising concepts in a meaningful way. This limited higher-level insight.

Many participants found MetaMAP useful for self-reflection as it helped them to “…analyse our own views points and show relationships between them.” Some expressed that the exercise helped them to frame their research in a broader context. Another “…found it valuable to conceptualise how different issues interact and the relationships between them, however, it is difficult to map the full complexity of this.” The visual space consumed to represent an idea limited the number of ideas which could be seen (and hence considered) together. A digital environment would allow for a greater density of information with easily collapsible and expandable content. This would facilitate more complex and subtle thinking necessary for addressing sustainability issues. The large projector screen used in the introduction proved especially valuable for collaboration which highlights opportunities for educational, commercial and institutional applications.

By connecting their own projects with the SDGs, some participants (especially younger ones) were greatly empowered—one was visibly moved. However, many had a limited understanding of their own opportunities in driving systemic change. The geographic scale within the MetaMAP framework proved insufficient for many disciplines. For example, some from social sciences and humanities backgrounds requested scales of human interaction such as individual, family, community, society, and civilisation. Some participants found that within the system they studied, stakeholders were disconnected from the issues they manage—a critical challenge for sustainability practitioners (Guerrero et al. 2015 ). This led us to develop methods for improving ‘Social–Ecological fit’ in later iterations. Opportunities remain for developing more guided processes based on collaborative design and facilitation methods.

Outcomes of the design process

We now provide a brief overview of MetaMAP in its current state and its value for achieving SDGs (Fig.  1 ). A detailed description with worked examples and further applications can be found in (Maher et al. 2018a ). A blank version of the MetaMAP framework is provided as an appendix for use by readers. MetaMAP is a graphical tool which supports collaborative investigation and design for achieving SDGs. It helps diverse users to integrate their thinking, understand sustainability challenges holistically, and develop well-integrated solutions. MetaMAP is based on a new high-level conceptual framework which gives structure to social–ecological systems built collaboratively by interdisciplinary teams. The framework synthesises important concepts drawn from multiple schools of thought including: Social–Ecological systems (e.g. Partelow 2015 ), Planetary Boundaries (e.g. Rockström et al. 2009 ), Design thinking (e.g. Glanville 2007 ), Integral theory (e.g. Brown 2007 ) and Ecosystem Services (e.g. Abson et al. 2014 ) among others. This underlying framework is represented visually as a ‘landscape of ideas’. Over this ‘landscape’, users add concepts—icons which represent important components of the social–ecological system being investigated. The ‘landscape’ locates concepts based on their scale (the y -axis ranging from personal to universal) and how they may be categorised in the social–ecological system ( x -axis grouped into three realms: the natural Environment, the built environment and society). Users link concepts using lines and arrows to represent how they relate (e.g. increases, decreases, restricts, etc.). As interdisciplinary teams contribute concepts and links, they build up a conceptual model of the social–ecological system being investigated. The framework helps users to consider a wide variety of issues and gain insight into patterns and trends in the system. These big picture issues can be represented as notations and groups. Common high-order concepts (e.g. resilience and synergy) are located in the ‘emergent properties’ and ‘guiding principles’ boxes above the landscape. The SDGs (or other guiding frameworks) can be located on the landscape to help users see how the system they influence may support or compromise the SDGs.

We have designed a digital platform which uses the MetaMAP framework to organise and navigate diverse content contributed by a community of users from around the globe. We are currently seeking collaboration to support its development.

Applications and benefits for different users

MetaMAP helps users to achieve sustainability goals in a number of complementary ways. The structure and application of the MetaMAP framework help users gain insight by seeing relationships among parts of the natural environment, built environment and society across multiple spatial and temporal scales. It provides an inclusive framework to help people from different backgrounds integrate their diverse perspectives on sustainability issues into a common understanding. Decision makers can use MetaMAP to help understand complex challenges, identify strategies with synergy and design well-integrated solutions. Researchers can use MetaMAP to identify gaps in knowledge and communicate the broader implications of their research. Learners can use MetaMAP to explore diverse content on sustainability and understand connections between seemingly isolated issues.

Design principles for achieving SDGs

Examining the process of designing MetaMAP sheds light on important design principles which help to solve wicked problems and achieve SDGs. Design brings with it a particular way of understanding; “…a way of looking at the world and reshaping it, a way of generating knowledge through creation” (Overbeeke and Wensveen 2003 ). To support this, design approaches can help people to understand complex situations in new ways which generate new types of solutions. This is of critical value for transforming our social–ecological system to be more sustainable (Westley et al. 2011 ). When used alone, traditional methods which focus on optimising existing circumstances will remain unable to generate the degree of change needed to achieve sustainability goals. We now discuss some design principles used in this project and their broader implications for achieving SDGs (Table  2 ).

Table 2

Design principles, examples from the design of MetaMAP, and how MetaMAP helps users to apply the principle in practice

Broad problem framing supporting multiple goals

Whereas traditional approaches to research narrow their focus towards strictly defined objectives, design approaches often lead to a broader understanding of problems and potential solutions (Faste and Faste 2012 ). This helps design researchers to challenge assumptions and to remain open to shifting their objectives in the light of new understanding. Achieving SDGs routinely involves conflicts among stakeholders with different values and goals. Framing sustainability projects narrowly puts different goals into opposition and increases these conflicts. Alternatively, design approaches which broaden their problem framing can help to find a ‘higher common purpose’ among stakeholders and reduce conflict (Patel 2005 ). More inclusive conceptual frameworks can also help sustainability researchers and practitioners to take a broader perspective.

Maximise synergy, minimise compromise

In order to create well-integrated results, designers seek to identify strategies with synergy—where a single approach can help to achieve multiple contradictory goals simultaneously (Glanville 2007 ). In architecture for example, a line of columns in a building might simultaneously (1) support a roof, (2) define a path, (3) provide privacy, (4) form a space, (5) embellish a façade, and (6) express wealth and power. Where this is successful, the whole becomes much more than the sum of its parts. Any attempt to quantify its value or judge it by a single predetermined metric undervalues it and distorts reality. This highlights the risk of relying on many common tools and approaches to achieving SDGs. Building on synergy can also help to minimise conflict. For example, if two goals, stakeholders, or SDGs are typically in conflict, a design approach would be to seek atypical situations with the potential to minimise/avoid/reverse the conflict. However, developing synergetic strategies is challenging, requiring designers to constantly shift their own perspective. Each new way of looking at a particular design proposal provides new insight on its shortcomings and opportunities for its development.

Integrating diverse perspectives

Achieving sustainability goals entails transforming our social–ecological system so that many parts work in harmony. This is challenging, however, as the perspective of any one individual or discipline focuses on some issues while neglecting others. As such, no one perspective or discipline working in isolation can be expected to develop well-integrated sustainability initiatives. Failing to consider an important perspective may lead to blind spots in a proposal which become likely points of failure. To avoid this narrow mindedness, a core question of design thinking is ‘what am I missing?’.

When making decisions, many people employ a number of techniques to avoid the problems of single perspectives, such as listing pros and cons or Edward de Bono’s ‘six thinking hats’ (De Bono 2017 ). Designers have other methods. The RtD case study above introduced the perspectives of different disciplines through collaborative activities such as interviews and workshops. Visual methods can also help people take on multiple perspectives by representing the same idea in different ways (Agrawala et al. 2011 ).

In designing MetaMAP, this deliberate shifting of perspective took many forms. Sometimes it involved focusing on one objective, then another. Roleplaying helped us to imagine how different potential users might respond to some aspect of the design. Sometimes we extended this by imagining entire scenarios involving particular users in a particular context using MetaMAP to pursue their own unique interests. This helped us to tailor the current prototype to specific context and applications. Knowing when to apply each approach for maximum benefit requires constant self-reflection and is among the most difficult design skills to learn.

This approach has many benefits for achieving SDGs as each new way of looking at a project provides new insight into challenges and opportunities. A problem which seems impossible from one perspective may be solved easily from another. Sustainability initiatives developed by integrating diverse perspectives can also expect to benefit from wider support among stakeholders.

Thinking visually

Many sustainability challenges are too complex to hold it in one’s mind, so they must be visualised to be considered holistically. Diagrams can help sustainability researchers to identify existing and possible relationships between parts and to “…understand the research in totality and…freely manipulate and associate individual pieces of data” (Kolko 2009 ). Similarly, holistic frameworks can help decision makers to see the details of a project in a wider context. That way, decisions at a small scale can contribute positively to the project at large. Achieving SDGs involves complex relationships within social–ecological systems. Visual methods can represent these non-linear circumstances far more clearly than words. Communication is a critical limit in developing interdisciplinary sustainability initiatives (Godemann and Michelsen 2011 ) and visual methods can help to bridge disciplinary silos by providing a common language.

Many disciplines contributing to sustainability use specialist visual tools such as geographic maps and data visualisation. Like design tools, these visuals help researchers to gain insight into complex issues. However, to aid design thinking, visual tools should allow users to represent diverse ideas and manipulate them freely. Despite their value in sustainability research, many academic publishers restrict the use of visuals to the minimum required to support the text. Instead, publishers should seek the optimal number and type to communicate complex issues vividly to a wide audience.

Multiple feedback loops

In sustainability as in other wicked problems, it is rarely possible to see a direct path to a successful outcome. The many unknowns and unforeseen opportunities mean that any approach to creating a solution must be adjusted along the way. The more cycles of action and reflection, the faster an effective strategy can be identified. To achieve this, design approaches employ a fractal of feedback loops—often many thousands in a single project. At the largest scale, lessons learned from each project inform the next. Within a project, reflecting on progress in each stage shapes the activities of the next. When testing ideas collaboratively, each person’s response may guide future design. Each new perspective a designer takes on provokes the next idea. Seeing each new drawing informs the next, and even within a single sketch, drawing and seeing each line helps guide the next. Each feedback loop is a new opportunity to steer the project towards outcomes which will be effective in the complexities of the real world. Applying a fractal of feedback loops to the pursuit of sustainability goals delivers initiatives with greater synergy, fewer objections, providing more benefits and better adapted to their unique contexts.

The scientific method requires that each step must be justified objectively and to the satisfaction of external reviewers. However in design, and when seeking transformational change generally, it is essential to suspend judgement (de Bono 1970 ) so that new strategies can be tested despite initial uncertainty and limitations. Reliability in design is generated not through concrete incremental advances, but by insightful leaps refined through numerous cycles of critique, editing and development (Dorst 2011 ).

Future work

The SDG tools compendium (Asian Development Bank 2018 ) introduces 134 tools for helping to address environmental SDGs in Asia. These are sorted into 17 different categories and support critical endeavours including analysis, budgeting, stakeholder engagement, building scenarios and developing measurements. However, none of the 134 tools are explicitly for designing sustainability initiatives using established design principles and methods. Only one provides an excellent yet brief and general introduction to design thinking (Elmansy 2017 ), and it does not provide tools tailored for designing sustainability initiatives.

MetaMAP can help users to apply design approaches to achieve SDGs in a wide variety of contexts. We presented MetaMAP in Stockholm at Resilience 2017 (Maher 2017b ) and the International Conference on Sustainability Science (ICSS) (Maher 2017a ). Afterwards, we were approached by people from academia, Non-Government Organisations and governance backgrounds seeking to apply MetaMAP in diverse contexts including:

• Resilience planning in South and Southeast Asia
• Multiple ecosystem services and urbanisation around Shanghai
• Air pollution in China
• Facilitating Academic‐Industry collaboration in sustainable agriculture
• Community development planning in Guyana
• Cross‐scale issues in health systems
• Organisational strategy for sustainability
• Communicating how SDGs are being addressed in India
• Sustainability education

Requests for these broad application domains demonstrate both the flexibility of the MetaMAP system and strong demand for the benefits it provides. Applying MetaMAP in contexts such as the above is required to enhance it and develop a digital platform. This response also demonstrates the potential value of the RtD process that created it.

In this paper, we discussed the value and limitations of new tools for achieving SDGs. This identified a pressing need for: (1) a stronger integration of design approaches with sustainability science; and (2) tools which help researchers and practitioners apply design thinking to develop sustainability solutions. We provided an overview of Research through Design methodology then examined a case study of its application: the process of designing MetaMAP. This involved five stages, each including: (re)framing the problem/opportunities, designing possible solutions, testing them collaboratively and reflecting critically. We concluded the case study by providing an overview of MetaMAP—a graphical tool for collaborating to understand social–ecological systems holistically and design well-integrated sustainability initiatives. Reflecting on this case study, we presented some fundamental design principles which were demonstrated through the RtD case study and their value for achieving SDGs.

Integrating design and sustainability science hold much value for transforming our social–ecological system to achieve SDGs. However, there are some significant challenges in doing so. Many aspects of a Research through Design project cannot be predetermined (Moloney 2015 ) which provides challenges for traditional research grants. There is a critical shortage of literature and guidance on design approaches for sustainability research. Design approaches to achieving SDGs can be advanced by (1) collaborating and learning from those experienced in creative design methods; (2) expanding opportunities for publishing creative explorations and visioning (Wiek and Iwaniec 2014 ); and (3) applying design methods to SDGs and sharing the results and process.

More specifically, MetaMAP can help researchers, practitioners and educators to understand the context of sustainability challenges more holistically and design initiatives. MetaMAP necessarily contains some compromises including: the categories, scales and guided process may be unsuited to some applications; and the apparent complexity of the framework may require practice and/or training to apply. Refining the usability and effectiveness of MetaMAP requires further application by people seeking to achieve SDGs in diverse settings. We are pursuing to develop MetaMAP into a digital platform for collaborating across disciplines and borders to achieve SDGs. This requires substantial testing, expertise and resources for which we are currently seeking collaboration. Combining the methods of science and design can help to develop more innovative, better integrated and truly transformational initiatives for sustainability. We encourage readers to apply design approaches and the MetaMAP graphic tools to their own unique sustainability projects and share their findings.

Below is the link to the electronic supplementary material.

Handled by Thomas Elmqvist, Stockholm Resilience Centre, Sweden.

The original version of this article was revised. The Figures 1 and 11 are corrected in this version.

Change history

In original publication of the article, the Figure 1 and Figure 11 are published incorrectly. The correct figures should be as follows.

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35 Scientific Thinking and Reasoning

Kevin N. Dunbar, Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD

David Klahr, Department of Psychology, Carnegie Mellon University, Pittsburgh, PA

• Published: 21 November 2012
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Scientific thinking refers to both thinking about the content of science and the set of reasoning processes that permeate the field of science: induction, deduction, experimental design, causal reasoning, concept formation, hypothesis testing, and so on. Here we cover both the history of research on scientific thinking and the different approaches that have been used, highlighting common themes that have emerged over the past 50 years of research. Future research will focus on the collaborative aspects of scientific thinking, on effective methods for teaching science, and on the neural underpinnings of the scientific mind.

There is no unitary activity called “scientific discovery”; there are activities of designing experiments, gathering data, inventing and developing observational instruments, formulating and modifying theories, deducing consequences from theories, making predictions from theories, testing theories, inducing regularities and invariants from data, discovering theoretical constructs, and others. — Simon, Langley, & Bradshaw, 1981 , p. 2

What Is Scientific Thinking and Reasoning?

There are two kinds of thinking we call “scientific.” The first, and most obvious, is thinking about the content of science. People are engaged in scientific thinking when they are reasoning about such entities and processes as force, mass, energy, equilibrium, magnetism, atoms, photosynthesis, radiation, geology, or astrophysics (and, of course, cognitive psychology!). The second kind of scientific thinking includes the set of reasoning processes that permeate the field of science: induction, deduction, experimental design, causal reasoning, concept formation, hypothesis testing, and so on. However, these reasoning processes are not unique to scientific thinking: They are the very same processes involved in everyday thinking. As Einstein put it:

The scientific way of forming concepts differs from that which we use in our daily life, not basically, but merely in the more precise definition of concepts and conclusions; more painstaking and systematic choice of experimental material, and greater logical economy. (The Common Language of Science, 1941, reprinted in Einstein, 1950 , p. 98)

Nearly 40 years after Einstein's remarkably insightful statement, Francis Crick offered a similar perspective: that great discoveries in science result not from extraordinary mental processes, but rather from rather common ones. The greatness of the discovery lies in the thing discovered.

I think what needs to be emphasized about the discovery of the double helix is that the path to it was, scientifically speaking, fairly commonplace. What was important was not the way it was discovered , but the object discovered—the structure of DNA itself. (Crick, 1988 , p. 67; emphasis added)

Under this view, scientific thinking involves the same general-purpose cognitive processes—such as induction, deduction, analogy, problem solving, and causal reasoning—that humans apply in nonscientific domains. These processes are covered in several different chapters of this handbook: Rips, Smith, & Medin, Chapter 11 on induction; Evans, Chapter 8 on deduction; Holyoak, Chapter 13 on analogy; Bassok & Novick, Chapter 21 on problem solving; and Cheng & Buehner, Chapter 12 on causality. One might question the claim that the highly specialized procedures associated with doing science in the “real world” can be understood by investigating the thinking processes used in laboratory studies of the sort described in this volume. However, when the focus is on major scientific breakthroughs, rather than on the more routine, incremental progress in a field, the psychology of problem solving provides a rich source of ideas about how such discoveries might occur. As Simon and his colleagues put it:

It is understandable, if ironic, that ‘normal’ science fits … the description of expert problem solving, while ‘revolutionary’ science fits the description of problem solving by novices. It is understandable because scientific activity, particularly at the revolutionary end of the continuum, is concerned with the discovery of new truths, not with the application of truths that are already well-known … it is basically a journey into unmapped terrain. Consequently, it is mainly characterized, as is novice problem solving, by trial-and-error search. The search may be highly selective—but it reaches its goal only after many halts, turnings, and back-trackings. (Simon, Langley, & Bradshaw, 1981 , p. 5)

The research literature on scientific thinking can be roughly categorized according to the two types of scientific thinking listed in the opening paragraph of this chapter: (1) One category focuses on thinking that directly involves scientific content . Such research ranges from studies of young children reasoning about the sun-moon-earth system (Vosniadou & Brewer, 1992 ) to college students reasoning about chemical equilibrium (Davenport, Yaron, Klahr, & Koedinger, 2008 ), to research that investigates collaborative problem solving by world-class researchers in real-world molecular biology labs (Dunbar, 1995 ). (2) The other category focuses on “general” cognitive processes, but it tends to do so by analyzing people's problem-solving behavior when they are presented with relatively complex situations that involve the integration and coordination of several different types of processes, and that are designed to capture some essential features of “real-world” science in the psychology laboratory (Bruner, Goodnow, & Austin, 1956 ; Klahr & Dunbar, 1988 ; Mynatt, Doherty, & Tweney, 1977 ).

There are a number of overlapping research traditions that have been used to investigate scientific thinking. We will cover both the history of research on scientific thinking and the different approaches that have been used, highlighting common themes that have emerged over the past 50 years of research.

A Brief History of Research on Scientific Thinking

Science is often considered one of the hallmarks of the human species, along with art and literature. Illuminating the thought processes used in science thus reveal key aspects of the human mind. The thought processes underlying scientific thinking have fascinated both scientists and nonscientists because the products of science have transformed our world and because the process of discovery is shrouded in mystery. Scientists talk of the chance discovery, the flash of insight, the years of perspiration, and the voyage of discovery. These images of science have helped make the mental processes underlying the discovery process intriguing to cognitive scientists as they attempt to uncover what really goes on inside the scientific mind and how scientists really think. Furthermore, the possibilities that scientists can be taught to think better by avoiding mistakes that have been clearly identified in research on scientific thinking, and that their scientific process could be partially automated, makes scientific thinking a topic of enduring interest.

The cognitive processes underlying scientific discovery and day-to-day scientific thinking have been a topic of intense scrutiny and speculation for almost 400 years (e.g., Bacon, 1620 ; Galilei 1638 ; Klahr 2000 ; Tweney, Doherty, & Mynatt, 1981 ). Understanding the nature of scientific thinking has been a central issue not only for our understanding of science but also for our understating of what it is to be human. Bacon's Novumm Organum in 1620 sketched out some of the key features of the ways that experiments are designed and data interpreted. Over the ensuing 400 years philosophers and scientists vigorously debated about the appropriate methods that scientists should use (see Giere, 1993 ). These debates over the appropriate methods for science typically resulted in the espousal of a particular type of reasoning method, such as induction or deduction. It was not until the Gestalt psychologists began working on the nature of human problem solving, during the 1940s, that experimental psychologists began to investigate the cognitive processes underlying scientific thinking and reasoning.

The Gestalt psychologist Max Wertheimer pioneered the investigation of scientific thinking (of the first type described earlier: thinking about scientific content ) in his landmark book Productive Thinking (Wertheimer, 1945 ). Wertheimer spent a considerable amount of time corresponding with Albert Einstein, attempting to discover how Einstein generated the concept of relativity. Wertheimer argued that Einstein had to overcome the structure of Newtonian physics at each step in his theorizing, and the ways that Einstein actually achieved this restructuring were articulated in terms of Gestalt theories. (For a recent and different account of how Einstein made his discovery, see Galison, 2003 .) We will see later how this process of overcoming alternative theories is an obstacle that both scientists and nonscientists need to deal with when evaluating and theorizing about the world.

One of the first investigations of scientific thinking of the second type (i.e., collections of general-purpose processes operating on complex, abstract, components of scientific thought) was carried out by Jerome Bruner and his colleagues at Harvard (Bruner et al., 1956 ). They argued that a key activity engaged in by scientists is to determine whether a particular instance is a member of a category. For example, a scientist might want to discover which substances undergo fission when bombarded by neutrons and which substances do not. Here, scientists have to discover the attributes that make a substance undergo fission. Bruner et al. saw scientific thinking as the testing of hypotheses and the collecting of data with the end goal of determining whether something is a member of a category. They invented a paradigm where people were required to formulate hypotheses and collect data that test their hypotheses. In one type of experiment, the participants were shown a card such as one with two borders and three green triangles. The participants were asked to determine the concept that this card represented by choosing other cards and getting feedback from the experimenter as to whether the chosen card was an example of the concept. In this case the participant may have thought that the concept was green and chosen a card with two green squares and one border. If the underlying concept was green, then the experimenter would say that the card was an example of the concept. In terms of scientific thinking, choosing a new card is akin to conducting an experiment, and the feedback from the experimenter is similar to knowing whether a hypothesis is confirmed or disconfirmed. Using this approach, Bruner et al. identified a number of strategies that people use to formulate and test hypotheses. They found that a key factor determining which hypothesis-testing strategy that people use is the amount of memory capacity that the strategy takes up (see also Morrison & Knowlton, Chapter 6 ; Medin et al., Chapter 11 ). Another key factor that they discovered was that it was much more difficult for people to discover negative concepts (e.g., not blue) than positive concepts (e.g., blue). Although Bruner et al.'s research is most commonly viewed as work on concepts, they saw their work as uncovering a key component of scientific thinking.

A second early line of research on scientific thinking was developed by Peter Wason and his colleagues (Wason, 1968 ). Like Bruner et al., Wason saw a key component of scientific thinking as being the testing of hypotheses. Whereas Bruner et al. focused on the different types of strategies that people use to formulate hypotheses, Wason focused on whether people adopt a strategy of trying to confirm or disconfirm their hypotheses. Using Popper's ( 1959 ) theory that scientists should try and falsify rather than confirm their hypotheses, Wason devised a deceptively simple task in which participants were given three numbers, such as 2-4-6, and were asked to discover the rule underlying the three numbers. Participants were asked to generate other triads of numbers and the experimenter would tell the participant whether the triad was consistent or inconsistent with the rule. They were told that when they were sure they knew what the rule was they should state it. Most participants began the experiment by thinking that the rule was even numbers increasing by 2. They then attempted to confirm their hypothesis by generating a triad like 8-10-12, then 14-16-18. These triads are consistent with the rule and the participants were told yes, that the triads were indeed consistent with the rule. However, when they proposed the rule—even numbers increasing by 2—they were told that the rule was incorrect. The correct rule was numbers of increasing magnitude! From this research, Wason concluded that people try to confirm their hypotheses, whereas normatively speaking, they should try to disconfirm their hypotheses. One implication of this research is that confirmation bias is not just restricted to scientists but is a general human tendency.

It was not until the 1970s that a general account of scientific reasoning was proposed. Herbert Simon, often in collaboration with Allan Newell, proposed that scientific thinking is a form of problem solving. He proposed that problem solving is a search in a problem space. Newell and Simon's theory of problem solving is discussed in many places in this handbook, usually in the context of specific problems (see especially Bassok & Novick, Chapter 21 ). Herbert Simon, however, devoted considerable time to understanding many different scientific discoveries and scientific reasoning processes. The common thread in his research was that scientific thinking and discovery is not a mysterious magical process but a process of problem solving in which clear heuristics are used. Simon's goal was to articulate the heuristics that scientists use in their research at a fine-grained level. By constructing computer programs that simulated the process of several major scientific discoveries, Simon and colleagues were able to articulate the specific computations that scientists could have used in making those discoveries (Langley, Simon, Bradshaw, & Zytkow, 1987 ; see section on “Computational Approaches to Scientific Thinking”). Particularly influential was Simon and Lea's ( 1974 ) work demonstrating that concept formation and induction consist of a search in two problem spaces: a space of instances and a space of rules. This idea has influenced problem-solving accounts of scientific thinking that will be discussed in the next section.

Overall, the work of Bruner, Wason, and Simon laid the foundations for contemporary research on scientific thinking. Early research on scientific thinking is summarized in Tweney, Doherty and Mynatt's 1981 book On Scientific Thinking , where they sketched out many of the themes that have dominated research on scientific thinking over the past few decades. Other more recent books such as Cognitive Models of Science (Giere, 1993 ), Exploring Science (Klahr, 2000 ), Cognitive Basis of Science (Carruthers, Stich, & Siegal, 2002 ), and New Directions in Scientific and Technical Thinking (Gorman, Kincannon, Gooding, & Tweney, 2004 ) provide detailed analyses of different aspects of scientific discovery. Another important collection is Vosnadiau's handbook on conceptual change research (Vosniadou, 2008 ). In this chapter, we discuss the main approaches that have been used to investigate scientific thinking.

How does one go about investigating the many different aspects of scientific thinking? One common approach to the study of the scientific mind has been to investigate several key aspects of scientific thinking using abstract tasks designed to mimic some essential characteristics of “real-world” science. There have been numerous methodologies that have been used to analyze the genesis of scientific concepts, theories, hypotheses, and experiments. Researchers have used experiments, verbal protocols, computer programs, and analyzed particular scientific discoveries. A more recent development has been to increase the ecological validity of such research by investigating scientists as they reason “live” (in vivo studies of scientific thinking) in their own laboratories (Dunbar, 1995 , 2002 ). From a “Thinking and Reasoning” standpoint the major aspects of scientific thinking that have been most actively investigated are problem solving, analogical reasoning, hypothesis testing, conceptual change, collaborative reasoning, inductive reasoning, and deductive reasoning.

Scientific Thinking as Problem Solving

One of the primary goals of accounts of scientific thinking has been to provide an overarching framework to understand the scientific mind. One framework that has had a great influence in cognitive science is that scientific thinking and scientific discovery can be conceived as a form of problem solving. As noted in the opening section of this chapter, Simon ( 1977 ; Simon, Langley, & Bradshaw, 1981 ) argued that both scientific thinking in general and problem solving in particular could be thought of as a search in a problem space. A problem space consists of all the possible states of a problem and all the operations that a problem solver can use to get from one state to the next. According to this view, by characterizing the types of representations and procedures that people use to get from one state to another it is possible to understand scientific thinking. Thus, scientific thinking can be characterized as a search in various problem spaces (Simon, 1977 ). Simon investigated a number of scientific discoveries by bringing participants into the laboratory, providing the participants with the data that a scientist had access to, and getting the participants to reason about the data and rediscover a scientific concept. He then analyzed the verbal protocols that participants generated and mapped out the types of problem spaces that the participants search in (e.g., Qin & Simon, 1990 ). Kulkarni and Simon ( 1988 ) used a more historical approach to uncover the problem-solving heuristics that Krebs used in his discovery of the urea cycle. Kulkarni and Simon analyzed Krebs's diaries and proposed a set of problem-solving heuristics that he used in his research. They then built a computer program incorporating the heuristics and biological knowledge that Krebs had before he made his discoveries. Of particular importance are the search heuristics that the program uses, which include experimental proposal heuristics and data interpretation heuristics. A key heuristic was an unusualness heuristic that focused on unusual findings, which guided search through a space of theories and a space of experiments.

Klahr and Dunbar ( 1988 ) extended the search in a problem space approach and proposed that scientific thinking can be thought of as a search through two related spaces: an hypothesis space and an experiment space. Each problem space that a scientist uses will have its own types of representations and operators used to change the representations. Search in the hypothesis space constrains search in the experiment space. Klahr and Dunbar found that some participants move from the hypothesis space to the experiment space, whereas others move from the experiment space to the hypothesis space. These different types of searches lead to the proposal of different types of hypotheses and experiments. More recent work has extended the dual-space approach to include alternative problem-solving spaces, including those for data, instrumentation, and domain-specific knowledge (Klahr & Simon, 1999 ; Schunn & Klahr, 1995 , 1996 ).

Scientific Thinking as Hypothesis Testing

Many researchers have regarded testing specific hypotheses predicted by theories as one of the key attributes of scientific thinking. Hypothesis testing is the process of evaluating a proposition by collecting evidence regarding its truth. Experimental cognitive research on scientific thinking that specifically examines this issue has tended to fall into two broad classes of investigations. The first class is concerned with the types of reasoning that lead scientists astray, thus blocking scientific ingenuity. A large amount of research has been conducted on the potentially faulty reasoning strategies that both participants in experiments and scientists use, such as considering only one favored hypothesis at a time and how this prevents the scientists from making discoveries. The second class is concerned with uncovering the mental processes underlying the generation of new scientific hypotheses and concepts. This research has tended to focus on the use of analogy and imagery in science, as well as the use of specific types of problem-solving heuristics.

Turning first to investigations of what diminishes scientific creativity, philosophers, historians, and experimental psychologists have devoted a considerable amount of research to “confirmation bias.” This occurs when scientists only consider one hypothesis (typically the favored hypothesis) and ignore other alternative hypotheses or potentially relevant hypotheses. This important phenomenon can distort the design of experiments, formulation of theories, and interpretation of data. Beginning with the work of Wason ( 1968 ) and as discussed earlier, researchers have repeatedly shown that when participants are asked to design an experiment to test a hypothesis they will predominantly design experiments that they think will yield results consistent with the hypothesis. Using the 2-4-6 task mentioned earlier, Klayman and Ha ( 1987 ) showed that in situations where one's hypothesis is likely to be confirmed, seeking confirmation is a normatively incorrect strategy, whereas when the probability of confirming one's hypothesis is low, then attempting to confirm one's hypothesis can be an appropriate strategy. Historical analyses by Tweney ( 1989 ), concerning the way that Faraday made his discoveries, and experiments investigating people testing hypotheses, have revealed that people use a confirm early, disconfirm late strategy: When people initially generate or are given hypotheses, they try and gather evidence that is consistent with the hypothesis. Once enough evidence has been gathered, then people attempt to find the boundaries of their hypothesis and often try to disconfirm their hypotheses.

In an interesting variant on the confirmation bias paradigm, Gorman ( 1989 ) showed that when participants are told that there is the possibility of error in the data that they receive, participants assume that any data that are inconsistent with their favored hypothesis are due to error. Thus, the possibility of error “insulates” hypotheses against disconfirmation. This intriguing hypothesis has not been confirmed by other researchers (Penner & Klahr, 1996 ), but it is an intriguing hypothesis that warrants further investigation.

Confirmation bias is very difficult to overcome. Even when participants are asked to consider alternate hypotheses, they will often fail to conduct experiments that could potentially disconfirm their hypothesis. Tweney and his colleagues provide an excellent overview of this phenomenon in their classic monograph On Scientific Thinking (1981). The precise reasons for this type of block are still widely debated. Researchers such as Michael Doherty have argued that working memory limitations make it difficult for people to consider more than one hypothesis. Consistent with this view, Dunbar and Sussman ( 1995 ) have shown that when participants are asked to hold irrelevant items in working memory while testing hypotheses, the participants will be unable to switch hypotheses in the face of inconsistent evidence. While working memory limitations are involved in the phenomenon of confirmation bias, even groups of scientists can also display confirmation bias. For example, the controversy over cold fusion is an example of confirmation bias. Here, large groups of scientists had other hypotheses available to explain their data yet maintained their hypotheses in the face of other more standard alternative hypotheses. Mitroff ( 1974 ) provides some interesting examples of NASA scientists demonstrating confirmation bias, which highlight the roles of commitment and motivation in this process. See also MacPherson and Stanovich ( 2007 ) for specific strategies that can be used to overcome confirmation bias.

Causal Thinking in Science

Much of scientific thinking and scientific theory building pertains to the development of causal models between variables of interest. For example, do vaccines cause illnesses? Do carbon dioxide emissions cause global warming? Does water on a planet indicate that there is life on the planet? Scientists and nonscientists alike are constantly bombarded with statements regarding the causal relationship between such variables. How does one evaluate the status of such claims? What kinds of data are informative? How do scientists and nonscientists deal with data that are inconsistent with their theory?

A central issue in the causal reasoning literature, one that is directly relevant to scientific thinking, is the extent to which scientists and nonscientists alike are governed by the search for causal mechanisms (i.e., how a variable works) versus the search for statistical data (i.e., how often variables co-occur). This dichotomy can be boiled down to the search for qualitative versus quantitative information about the paradigm the scientist is investigating. Researchers from a number of cognitive psychology laboratories have found that people prefer to gather more information about an underlying mechanism than covariation between a cause and an effect (e.g., Ahn, Kalish, Medin, & Gelman, 1995 ). That is, the predominant strategy that students in simulations of scientific thinking use is to gather as much information as possible about how the objects under investigation work, rather than collecting large amounts of quantitative data to determine whether the observations hold across multiple samples. These findings suggest that a central component of scientific thinking may be to formulate explicit mechanistic causal models of scientific events.

One type of situation in which causal reasoning has been observed extensively is when scientists obtain unexpected findings. Both historical and naturalistic research has revealed that reasoning causally about unexpected findings plays a central role in science. Indeed, scientists themselves frequently state that a finding was due to chance or was unexpected. Given that claims of unexpected findings are such a frequent component of scientists' autobiographies and interviews in the media, Dunbar ( 1995 , 1997 , 1999 ; Dunbar & Fugelsang, 2005 ; Fugelsang, Stein, Green, & Dunbar, 2004 ) decided to investigate the ways that scientists deal with unexpected findings. In 1991–1992 Dunbar spent 1 year in three molecular biology laboratories and one immunology laboratory at a prestigious U.S. university. He used the weekly laboratory meeting as a source of data on scientific discovery and scientific reasoning. (He termed this type of study “in vivo” cognition.) When he looked at the types of findings that the scientists made, he found that over 50% of the findings were unexpected and that these scientists had evolved a number of effective strategies for dealing with such findings. One clear strategy was to reason causally about the findings: Scientists attempted to build causal models of their unexpected findings. This causal model building results in the extensive use of collaborative reasoning, analogical reasoning, and problem-solving heuristics (Dunbar, 1997 , 2001 ).

Many of the key unexpected findings that scientists reasoned about in the in vivo studies of scientific thinking were inconsistent with the scientists' preexisting causal models. A laboratory equivalent of the biology labs involved creating a situation in which students obtained unexpected findings that were inconsistent with their preexisting theories. Dunbar and Fugelsang ( 2005 ) examined this issue by creating a scientific causal thinking simulation where experimental outcomes were either expected or unexpected. Dunbar ( 1995 ) has called the study of people reasoning in a cognitive laboratory “in vitro” cognition. These investigators found that students spent considerably more time reasoning about unexpected findings than expected findings. In addition, when assessing the overall degree to which their hypothesis was supported or refuted, participants spent the majority of their time considering unexpected findings. An analysis of participants' verbal protocols indicates that much of this extra time was spent formulating causal models for the unexpected findings. Similarly, scientists spend more time considering unexpected than expected findings, and this time is devoted to building causal models (Dunbar & Fugelsang, 2004 ).

Scientists know that unexpected findings occur often, and they have developed many strategies to take advantage of their unexpected findings. One of the most important places that they anticipate the unexpected is in designing experiments (Baker & Dunbar, 2000 ). They build different causal models of their experiments incorporating many conditions and controls. These multiple conditions and controls allow unknown mechanisms to manifest themselves. Thus, rather than being the victims of the unexpected, they create opportunities for unexpected events to occur, and once these events do occur, they have causal models that allow them to determine exactly where in the causal chain their unexpected finding arose. The results of these in vivo and in vitro studies all point to a more complex and nuanced account of how scientists and nonscientists alike test and evaluate hypotheses about theories.

The Roles of Inductive, Abductive, and Deductive Thinking in Science

One of the most basic characteristics of science is that scientists assume that the universe that we live in follows predictable rules. Scientists reason using a variety of different strategies to make new scientific discoveries. Three frequently used types of reasoning strategies that scientists use are inductive, abductive, and deductive reasoning. In the case of inductive reasoning, a scientist may observe a series of events and try to discover a rule that governs the event. Once a rule is discovered, scientists can extrapolate from the rule to formulate theories of observed and yet-to-be-observed phenomena. One example is the discovery using inductive reasoning that a certain type of bacterium is a cause of many ulcers (Thagard, 1999 ). In a fascinating series of articles, Thagard documented the reasoning processes that Marshall and Warren went through in proposing this novel hypothesis. One key reasoning process was the use of induction by generalization. Marshall and Warren noted that almost all patients with gastric entritis had a spiral bacterium in their stomachs, and he formed the generalization that this bacterium is the cause of stomach ulcers. There are numerous other examples of induction by generalization in science, such as Tycho De Brea's induction about the motion of planets from his observations, Dalton's use of induction in chemistry, and the discovery of prions as the source of mad cow disease. Many theories of induction have used scientific discovery and reasoning as examples of this important reasoning process.

Another common type of inductive reasoning is to map a feature of one member of a category to another member of a category. This is called categorical induction. This type of induction is a way of projecting a known property of one item onto another item that is from the same category. Thus, knowing that the Rous Sarcoma virus is a retrovirus that uses RNA rather than DNA, a biologist might assume that another virus that is thought to be a retrovirus also uses RNA rather than DNA. While research on this type of induction typically has not been discussed in accounts of scientific thinking, this type of induction is common in science. For an influential contribution to this literature, see Smith, Shafir, and Osherson ( 1993 ), and for reviews of this literature see Heit ( 2000 ) and Medin et al. (Chapter 11 ).

While less commonly mentioned than inductive reasoning, abductive reasoning is an important form of reasoning that scientists use when they are seeking to propose explanations for events such as unexpected findings (see Lombrozo, Chapter 14 ; Magnani, et al., 2010 ). In Figure 35.1 , taken from King ( 2011 ), the differences between inductive, abductive, and deductive thinking are highlighted. In the case of abduction, the reasoner attempts to generate explanations of the form “if situation X had occurred, could it have produced the current evidence I am attempting to interpret?” (For an interesting of analysis of abductive reasoning see the brief paper by Klahr & Masnick, 2001 ). Of course, as in classical induction, such reasoning may produce a plausible account that is still not the correct one. However, abduction does involve the generation of new knowledge, and is thus also related to research on creativity.

The different processes underlying inductive, abductive, and deductive reasoning in science. (Figure reproduced from King 2011 ).)

Turning now to deductive thinking, many thinking processes that scientists adhere to follow traditional rules of deductive logic. These processes correspond to those conditions in which a hypothesis may lead to, or is deducible to, a conclusion. Though they are not always phrased in syllogistic form, deductive arguments can be phrased as “syllogisms,” or as brief, mathematical statements in which the premises lead to the conclusion. Deductive reasoning is an extremely important aspect of scientific thinking because it underlies a large component of how scientists conduct their research. By looking at many scientific discoveries, we can often see that deductive reasoning is at work. Deductive reasoning statements all contain information or rules that state an assumption about how the world works, as well as a conclusion that would necessarily follow from the rule. Numerous discoveries in physics such as the discovery of dark matter by Vera Rubin are based on deductions. In the dark matter case, Rubin measured galactic rotation curves and based on the differences between the predicted and observed angular motions of galaxies she deduced that the structure of the universe was uneven. This led her to propose that dark matter existed. In contemporary physics the CERN Large Hadron Collider is being used to search for the Higgs Boson. The Higgs Boson is a deductive prediction from contemporary physics. If the Higgs Boson is not found, it may lead to a radical revision of the nature of physics and a new understanding of mass (Hecht, 2011 ).

The Roles of Analogy in Scientific Thinking

One of the most widely mentioned reasoning processes used in science is analogy. Scientists use analogies to form a bridge between what they already know and what they are trying to explain, understand, or discover. In fact, many scientists have claimed that the making of certain analogies was instrumental in their making a scientific discovery, and almost all scientific autobiographies and biographies feature one particular analogy that is discussed in depth. Coupled with the fact that there has been an enormous research program on analogical thinking and reasoning (see Holyoak, Chapter 13 ), we now have a number of models and theories of analogical reasoning that suggest how analogy can play a role in scientific discovery (see Gentner, Holyoak, & Kokinov, 2001 ). By analyzing several major discoveries in the history of science, Thagard and Croft ( 1999 ), Nersessian ( 1999 , 2008 ), and Gentner and Jeziorski ( 1993 ) have all shown that analogical reasoning is a key aspect of scientific discovery.

Traditional accounts of analogy distinguish between two components of analogical reasoning: the target and the source (Holyoak, Chapter 13 ; Gentner 2010 ). The target is the concept or problem that a scientist is attempting to explain or solve. The source is another piece of knowledge that the scientist uses to understand the target or to explain the target to others. What the scientist does when he or she makes an analogy is to map features of the source onto features of the target. By mapping the features of the source onto the target, new features of the target may be discovered, or the features of the target may be rearranged so that a new concept is invented and a scientific discovery is made. For example, a common analogy that is used with computers is to describe a harmful piece of software as a computer virus. Once a piece of software is called a virus, people can map features of biological viruses, such as that it is small, spreads easily, self-replicates using a host, and causes damage. People not only map individual features of the source onto the target but also the systems of relations. For example, if a computer virus is similar to a biological virus, then an immune system can be created on computers that can protect computers from future variants of a virus. One of the reasons that scientific analogy is so powerful is that it can generate new knowledge, such as the creation of a computational immune system having many of the features of a real biological immune system. This analogy also leads to predictions that there will be newer computer viruses that are the computational equivalent of retroviruses, lacking DNA, or standard instructions, that will elude the computational immune system.

The process of making an analogy involves a number of key steps: retrieval of a source from memory, aligning the features of the source with those of the target, mapping features of the source onto those of the target, and possibly making new inferences about the target. Scientific discoveries are made when the source highlights a hitherto unknown feature of the target or restructures the target into a new set of relations. Interestingly, research on analogy has shown that participants do not easily use remote analogies (see Gentner et al., 1997 ; Holyoak & Thagard 1995 ). Participants in experiments tend to focus on the sharing of a superficial feature between the source and the target, rather than the relations among features. In his in vivo studies of science, Dunbar ( 1995 , 2001 , 2002 ) investigated the ways that scientists use analogies while they are conducting their research and found that scientists use both relational and superficial features when they make analogies. Whether they use superficial or relational features depends on their goals. If their goal is to fix a problem in an experiment, their analogies are based upon superficial features. However, if their goal is to formulate hypotheses, they focus on analogies based upon sets of relations. One important difference between scientists and participants in experiments is that the scientists have deep relational knowledge of the processes that they are investigating and can hence use this relational knowledge to make analogies (see Holyoak, Chapter 13 for a thorough review of analogical reasoning).

Are scientific analogies always useful? Sometimes analogies can lead scientists and students astray. For example, Evelyn Fox-Keller ( 1985 ) shows how an analogy between the pulsing of a lighthouse and the activity of the slime mold dictyostelium led researchers astray for a number of years. Likewise, the analogy between the solar system (the source) and the structure of the atom (the target) has been shown to be potentially misleading to students taking more advanced courses in physics or chemistry. The solar system analogy has a number of misalignments to the structure of the atom, such as electrons being repelled from each other rather than attracted; moreover, electrons do not have individual orbits like planets but have orbit clouds of electron density. Furthermore, students have serious misconceptions about the nature of the solar system, which can compound their misunderstanding of the nature of the atom (Fischler & Lichtfeld, 1992 ). While analogy is a powerful tool in science, like all forms of induction, incorrect conclusions can be reached.

Conceptual Change in Science

Scientific knowledge continually accumulates as scientists gather evidence about the natural world. Over extended time, this knowledge accumulation leads to major revisions, extensions, and new organizational forms for expressing what is known about nature. Indeed, these changes are so substantial that philosophers of science speak of “revolutions” in a variety of scientific domains (Kuhn, 1962 ). The psychological literature that explores the idea of revolutionary conceptual change can be roughly divided into (a) investigations of how scientists actually make discoveries and integrate those discoveries into existing scientific contexts, and (b) investigations of nonscientists ranging from infants, to children, to students in science classes. In this section we summarize the adult studies of conceptual change, and in the next section we look at its developmental aspects.

Scientific concepts, like all concepts, can be characterized as containing a variety of “knowledge elements”: representations of words, thoughts, actions, objects, and processes. At certain points in the history of science, the accumulated evidence has demanded major shifts in the way these collections of knowledge elements are organized. This “radical conceptual change” process (see Keil, 1999 ; Nersessian 1998 , 2002 ; Thagard, 1992 ; Vosniadou 1998, for reviews) requires the formation of a new conceptual system that organizes knowledge in new ways, adds new knowledge, and results in a very different conceptual structure. For more recent research on conceptual change, The International Handbook of Research on Conceptual Change (Vosniadou, 2008 ) provides a detailed compendium of theories and controversies within the field.

While conceptual change in science is usually characterized by large-scale changes in concepts that occur over extensive periods of time, it has been possible to observe conceptual change using in vivo methodologies. Dunbar ( 1995 ) reported a major conceptual shift that occurred in immunologists, where they obtained a series of unexpected findings that forced the scientists to propose a new concept in immunology that in turn forced the change in other concepts. The drive behind this conceptual change was the discovery of a series of different unexpected findings or anomalies that required the scientists to both revise and reorganize their conceptual knowledge. Interestingly, this conceptual change was achieved by a group of scientists reasoning collaboratively, rather than by a scientist working alone. Different scientists tend to work on different aspects of concepts, and also different concepts, that when put together lead to a rapid change in entire conceptual structures.

Overall, accounts of conceptual change in individuals indicate that it is indeed similar to that of conceptual change in entire scientific fields. Individuals need to be confronted with anomalies that their preexisting theories cannot explain before entire conceptual structures are overthrown. However, replacement conceptual structures have to be generated before the old conceptual structure can be discarded. Sometimes, people do not overthrow their original conceptual theories and through their lives maintain their original views of many fundamental scientific concepts. Whether people actively possess naive theories, or whether they appear to have a naive theory because of the demand characteristics of the testing context, is a lively source of debate within the science education community (see Gupta, Hammer, & Redish, 2010 ).

Scientific Thinking in Children

Well before their first birthday, children appear to know several fundamental facts about the physical world. For example, studies with infants show that they behave as if they understand that solid objects endure over time (e.g., they don't just disappear and reappear, they cannot move through each other, and they move as a result of collisions with other solid objects or the force of gravity (Baillargeon, 2004 ; Carey 1985 ; Cohen & Cashon, 2006 ; Duschl, Schweingruber, & Shouse, 2007 ; Gelman & Baillargeon, 1983 ; Gelman & Kalish, 2006 ; Mandler, 2004 ; Metz 1995 ; Munakata, Casey, & Diamond, 2004 ). And even 6-month-olds are able to predict the future location of a moving object that they are attempting to grasp (Von Hofsten, 1980 ; Von Hofsten, Feng, & Spelke, 2000 ). In addition, they appear to be able to make nontrivial inferences about causes and their effects (Gopnik et al., 2004 ).

The similarities between children's thinking and scientists' thinking have an inherent allure and an internal contradiction. The allure resides in the enthusiastic wonder and openness with which both children and scientists approach the world around them. The paradox comes from the fact that different investigators of children's thinking have reached diametrically opposing conclusions about just how “scientific” children's thinking really is. Some claim support for the “child as a scientist” position (Brewer & Samarapungavan, 1991 ; Gelman & Wellman, 1991 ; Gopnik, Meltzoff, & Kuhl, 1999 ; Karmiloff-Smith 1988 ; Sodian, Zaitchik, & Carey, 1991 ; Samarapungavan 1992 ), while others offer serious challenges to the view (Fay & Klahr, 1996 ; Kern, Mirels, & Hinshaw, 1983 ; Kuhn, Amsel, & O'Laughlin, 1988 ; Schauble & Glaser, 1990 ; Siegler & Liebert, 1975 .) Such fundamentally incommensurate conclusions suggest that this very field—children's scientific thinking—is ripe for a conceptual revolution!

A recent comprehensive review (Duschl, Schweingruber, & Shouse, 2007 ) of what children bring to their science classes offers the following concise summary of the extensive developmental and educational research literature on children's scientific thinking:

Children entering school already have substantial knowledge of the natural world, much of which is implicit.

What children are capable of at a particular age is the result of a complex interplay among maturation, experience, and instruction. What is developmentally appropriate is not a simple function of age or grade, but rather is largely contingent on children's prior opportunities to learn.

Students' knowledge and experience play a critical role in their science learning, influencing four aspects of science understanding, including (a) knowing, using, and interpreting scientific explanations of the natural world; (b) generating and evaluating scientific evidence and explanations, (c) understanding how scientific knowledge is developed in the scientific community, and (d) participating in scientific practices and discourse.

Students learn science by actively engaging in the practices of science.

In the previous section of this article we discussed conceptual change with respect to scientific fields and undergraduate science students. However, the idea that children undergo radical conceptual change in which old “theories” need to be overthrown and reorganized has been a central topic in understanding changes in scientific thinking in both children and across the life span. This radical conceptual change is thought to be necessary for acquiring many new concepts in physics and is regarded as the major source of difficulty for students. The factors that are at the root of this conceptual shift view have been difficult to determine, although there have been a number of studies in cognitive development (Carey, 1985 ; Chi 1992 ; Chi & Roscoe, 2002 ), in the history of science (Thagard, 1992 ), and in physics education (Clement, 1982 ; Mestre 1991 ) that give detailed accounts of the changes in knowledge representation that occur while people switch from one way of representing scientific knowledge to another.

One area where students show great difficulty in understanding scientific concepts is physics. Analyses of students' changing conceptions, using interviews, verbal protocols, and behavioral outcome measures, indicate that large-scale changes in students' concepts occur in physics education (see McDermott & Redish, 1999 , for a review of this literature). Following Kuhn ( 1962 ), many researchers, but not all, have noted that students' changing conceptions resemble the sequences of conceptual changes in physics that have occurred in the history of science. These notions of radical paradigm shifts and ensuing incompatibility with past knowledge-states have called attention to interesting parallels between the development of particular scientific concepts in children and in the history of physics. Investigations of nonphysicists' understanding of motion indicate that students have extensive misunderstandings of motion. Some researchers have interpreted these findings as an indication that many people hold erroneous beliefs about motion similar to a medieval “impetus” theory (McCloskey, Caramazza, & Green, 1980 ). Furthermore, students appear to maintain “impetus” notions even after one or two courses in physics. In fact, some authors have noted that students who have taken one or two courses in physics can perform worse on physics problems than naive students (Mestre, 1991 ). Thus, it is only after extensive learning that we see a conceptual shift from impetus theories of motion to Newtonian scientific theories.

How one's conceptual representation shifts from “naive” to Newtonian is a matter of contention, as some have argued that the shift involves a radical conceptual change, whereas others have argued that the conceptual change is not really complete. For example, Kozhevnikov and Hegarty ( 2001 ) argue that much of the naive impetus notions of motion are maintained at the expense of Newtonian principles even with extensive training in physics. However, they argue that such impetus principles are maintained at an implicit level. Thus, although students can give the correct Newtonian answer to problems, their reaction times to respond indicate that they are also using impetus theories when they respond. An alternative view of conceptual change focuses on whether there are real conceptual changes at all. Gupta, Hammer and Redish ( 2010 ) and Disessa ( 2004 ) have conducted detailed investigations of changes in physics students' accounts of phenomena covered in elementary physics courses. They have found that rather than students possessing a naive theory that is replaced by the standard theory, many introductory physics students have no stable physical theory but rather construct their explanations from elementary pieces of knowledge of the physical world.

Computational Approaches to Scientific Thinking

Computational approaches have provided a more complete account of the scientific mind. Computational models provide specific detailed accounts of the cognitive processes underlying scientific thinking. Early computational work consisted of taking a scientific discovery and building computational models of the reasoning processes involved in the discovery. Langley, Simon, Bradshaw, and Zytkow ( 1987 ) built a series of programs that simulated discoveries such as those of Copernicus, Bacon, and Stahl. These programs had various inductive reasoning algorithms built into them, and when given the data that the scientists used, they were able to propose the same rules. Computational models make it possible to propose detailed models of the cognitive subcomponents of scientific thinking that specify exactly how scientific theories are generated, tested, and amended (see Darden, 1997 , and Shrager & Langley, 1990 , for accounts of this branch of research). More recently, the incorporation of scientific knowledge into computer programs has resulted in a shift in emphasis from using programs to simulate discoveries to building programs that are used to help scientists make discoveries. A number of these computer programs have made novel discoveries. For example, Valdes-Perez ( 1994 ) has built systems for discoveries in chemistry, and Fajtlowicz has done this in mathematics (Erdos, Fajtlowicz, & Staton, 1991 ).

These advances in the fields of computer discovery have led to new fields, conferences, journals, and even departments that specialize in the development of programs devised to search large databases in the hope of making new scientific discoveries (Langley, 2000 , 2002 ). This process is commonly known as “data mining.” This approach has only proved viable relatively recently, due to advances in computer technology. Biswal et al. ( 2010 ), Mitchell ( 2009 ), and Yang ( 2009 ) provide recent reviews of data mining in different scientific fields. Data mining is at the core of drug discovery, our understanding of the human genome, and our understanding of the universe for a number of reasons. First, vast databases concerning drug actions, biological processes, the genome, the proteome, and the universe itself now exist. Second, the development of high throughput data-mining algorithms makes it possible to search for new drug targets, novel biological mechanisms, and new astronomical phenomena in relatively short periods of time. Research programs that took decades, such as the development of penicillin, can now be done in days (Yang, 2009 ).

Another recent shift in the use of computers in scientific discovery has been to have both computers and people make discoveries together, rather than expecting that computers make an entire scientific discovery. Now instead of using computers to mimic the entire scientific discovery process as used by humans, computers can use powerful algorithms that search for patterns on large databases and provide the patterns to humans who can then use the output of these computers to make discoveries, ranging from the human genome to the structure of the universe. However, there are some robots such as ADAM, developed by King ( 2011 ), that can actually perform the entire scientific process, from the generation of hypotheses, to the conduct of experiments and the interpretation of results, with little human intervention. The ongoing development of scientific robots by some scientists (King et al., 2009 ) thus continues the tradition started by Herbert Simon in the 1960s. However, many of the controversies as to whether the robot is a “real scientist” or not continue to the present (Evans & Rzhetsky, 2010 , Gianfelici, 2010 ; Haufe, Elliott, Burian, & O' Malley, 2010 ; O'Malley 2011 ).

Scientific Thinking and Science Education

Accounts of the nature of science and research on scientific thinking have had profound effects on science education along many levels, particularly in recent years. Science education from the 1900s until the 1970s was primarily concerned with teaching students both the content of science (such as Newton's laws of motion) or the methods that scientists need to use in their research (such as using experimental and control groups). Beginning in the 1980s, a number of reports (e.g., American Association for the Advancement of Science, 1993; National Commission on Excellence in Education, 1983; Rutherford & Ahlgren, 1991 ) stressed the need for teaching scientific thinking skills rather than just methods and content. The addition of scientific thinking skills to the science curriculum from kindergarten through adulthood was a major shift in focus. Many of the particular scientific thinking skills that have been emphasized are skills covered in previous sections of this chapter, such as teaching deductive and inductive thinking strategies. However, rather than focusing on one particular skill, such as induction, researchers in education have focused on how the different components of scientific thinking are put together in science. Furthermore, science educators have focused upon situations where science is conducted collaboratively, rather than being the product of one person thinking alone. These changes in science education parallel changes in methodologies used to investigate science, such as analyzing the ways that scientists think and reason in their laboratories.

By looking at science as a complex multilayered and group activity, many researchers in science education have adopted a constructivist approach. This approach sees learning as an active rather than a passive process, and it suggests that students learn through constructing their scientific knowledge. We will first describe a few examples of the constructivist approach to science education. Following that, we will address several lines of work that challenge some of the assumptions of the constructivist approach to science education.

Often the goal of constructivist science education is to produce conceptual change through guided instruction where the teacher or professor acts as a guide to discovery, rather than the keeper of all the facts. One recent and influential approach to science education is the inquiry-based learning approach. Inquiry-based learning focuses on posing a problem or a puzzling event to students and asking them to propose a hypothesis that could explain the event. Next, the student is asked to collect data that test the hypothesis, make conclusions, and then reflect upon both the original problem and the thought processes that they used to solve the problem. Often students use computers that aid in their construction of new knowledge. The computers allow students to learn many of the different components of scientific thinking. For example, Reiser and his colleagues have developed a learning environment for biology, where students are encouraged to develop hypotheses in groups, codify the hypotheses, and search databases to test these hypotheses (Reiser et al., 2001 ).

One of the myths of science is the lone scientist suddenly shouting “Eureka, I have made a discovery!” Instead, in vivo studies of scientists (e.g., Dunbar, 1995 , 2002 ), historical analyses of scientific discoveries (Nersessian, 1999 ), and studies of children learning science at museums have all pointed to collaborative scientific discovery mechanisms as being one of the driving forces of science (Atkins et al., 2009 ; Azmitia & Crowley, 2001 ). What happens during collaborative scientific thinking is that there is usually a triggering event, such as an unexpected result or situation that a student does not understand. This results in other members of the group adding new information to the person's representation of knowledge, often adding new inductions and deductions that both challenge and transform the reasoner's old representations of knowledge (Chi & Roscoe, 2002 ; Dunbar 1998 ). Social mechanisms play a key component in fostering changes in concepts that have been ignored in traditional cognitive research but are crucial for both science and science education. In science education there has been a shift to collaborative learning, particularly at the elementary level; however, in university education, the emphasis is still on the individual scientist. As many domains of science now involve collaborations across scientific disciplines, we expect the explicit teaching of heuristics for collaborative science to increase.

What is the best way to teach and learn science? Surprisingly, the answer to this question has been difficult to uncover. For example, toward the end of the last century, influenced by several thinkers who advocated a constructivist approach to learning, ranging from Piaget (Beilin, 1994 ) to Papert ( 1980 ), many schools answered this question by adopting a philosophy dubbed “discovery learning.” Although a clear operational definition of this approach has yet to be articulated, the general idea is that children are expected to learn science by reconstructing the processes of scientific discovery—in a range of areas from computer programming to chemistry to mathematics. The premise is that letting students discover principles on their own, set their own goals, and collaboratively explore the natural world produces deeper knowledge that transfers widely.

The research literature on science education is far from consistent in its use of terminology. However, our reading suggests that “discovery learning” differs from “inquiry-based learning” in that few, if any, guidelines are given to students in discovery learning contexts, whereas in inquiry learning, students are given hypotheses and specific goals to achieve (see the second paragraph of this section for a definition of inquiry-based learning). Even though thousands of schools have adopted discovery learning as an alternative to more didactic approaches to teaching and learning, the evidence showing that it is more effective than traditional, direct, teacher-controlled instructional approaches is mixed, at best (Lorch et al., 2010 ; Minner, Levy, & Century, 2010 ). In several cases where the distinctions between direct instruction and more open-ended constructivist instruction have been clearly articulated, implemented, and assessed, direct instruction has proven to be superior to the alternatives (Chen & Klahr, 1999 ; Toth, Klahr, & Chen, 2000 ). For example, in a study of third- and fourth-grade children learning about experimental design, Klahr and Nigam ( 2004 ) found that many more children learned from direct instruction than from discovery learning. Furthermore, they found that among the few children who did manage to learn from a discovery method, there was no better performance on a far transfer test of scientific reasoning than that observed for the many children who learned from direct instruction.

The idea of children learning most of their science through a process of self-directed discovery has some romantic appeal, and it may accurately describe the personal experience of a handful of world-class scientists. However, the claim has generated some contentious disagreements (Kirschner, Sweller, & Clark, 2006 ; Klahr, 2010 ; Taber 2009 ; Tobias & Duffy, 2009 ), and the jury remains out on the extent to which most children can learn science that way.

Conclusions and Future Directions

The field of scientific thinking is now a thriving area of research with strong underpinnings in cognitive psychology and cognitive science. In recent years, a new professional society has been formed that aims to facilitate this integrative and interdisciplinary approach to the psychology of science, with its own journal and regular professional meetings. 1 Clearly the relations between these different aspects of scientific thinking need to be combined in order to produce a truly comprehensive picture of the scientific mind.

While much is known about certain aspects of scientific thinking, much more remains to be discovered. In particular, there has been little contact between cognitive, neuroscience, social, personality, and motivational accounts of scientific thinking. Research in thinking and reasoning has been expanded to use the methods and theories of cognitive neuroscience (see Morrison & Knowlton, Chapter 6 ). A similar approach can be taken in exploring scientific thinking (see Dunbar et al., 2007 ). There are two main reasons for taking a neuroscience approach to scientific thinking. First, functional neuroimaging allows the researcher to look at the entire human brain, making it possible to see the many different sites that are involved in scientific thinking and gain a more complete understanding of the entire range of mechanisms involved in this type of thought. Second, these brain-imaging approaches allow researchers to address fundamental questions in research on scientific thinking, such as the extent to which ordinary thinking in nonscientific contexts and scientific thinking recruit similar versus disparate neural structures of the brain.

Dunbar ( 2009 ) has used some novel methods to explore Simon's assertion, cited at the beginning of this chapter, that scientific thinking uses the same cognitive mechanisms that all human beings possess (rather than being an entirely different type of thinking) but combines them in ways that are specific to a particular aspect of science or a specific discipline of science. For example, Fugelsang and Dunbar ( 2009 ) compared causal reasoning when two colliding circular objects were labeled balls or labeled subatomic particles. They obtained different brain activation patterns depending on whether the stimuli were labeled balls or subatomic particles. In another series of experiments, Dunbar and colleagues used functional magnetic resonance imaging (fMRI) to study patterns of activation in the brains of students who have and who have not undergone conceptual change in physics. For example, Fugelsang and Dunbar ( 2005 ) and Dunbar et al. ( 2007 ) have found differences in the activation of specific brain sites (such as the anterior cingulate) for students when they encounter evidence that is inconsistent with their current conceptual understandings. These initial cognitive neuroscience investigations have the potential to reveal the ways that knowledge is organized in the scientific brain and provide detailed accounts of the nature of the representation of scientific knowledge. Petitto and Dunbar ( 2004 ) proposed the term “educational neuroscience” for the integration of research on education, including science education, with research on neuroscience. However, see Fitzpatrick (in press) for a very different perspective on whether neuroscience approaches are relevant to education. Clearly, research on the scientific brain is just beginning. We as scientists are beginning to get a reasonable grasp of the inner workings of the subcomponents of the scientific mind (i.e., problem solving, analogy, induction). However, great advances remain to be made concerning how these processes interact so that scientific discoveries can be made. Future research will focus on both the collaborative aspects of scientific thinking and the neural underpinnings of the scientific mind.

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Why Design Thinking Works

• Jeanne Liedtka

While we know a lot about practices that stimulate new ideas, innovation teams often struggle to apply them. Why? Because people’s biases and entrenched behaviors get in the way. In this article a Darden professor explains how design thinking helps people overcome this problem and unleash their creativity.

Though ostensibly geared to understanding and molding the experiences of customers, design thinking also profoundly reshapes the experiences of the innovators themselves. For example, immersive customer research helps them set aside their own views and recognize needs customers haven’t expressed. Carefully planned dialogues help teams build on their diverse ideas, not just negotiate compromises when differences arise. And experiments with new solutions reduce all stakeholders’ fear of change.

At every phase—customer discovery, idea generation, and testing—a clear structure makes people more comfortable trying new things, and processes increase collaboration. Because it combines practical tools and human insight, design thinking is a social technology —one that the author predicts will have an impact as large as an earlier social technology: total quality management.

It addresses the biases and behaviors that hamper innovation.

Idea in Brief

The problem.

While we know a lot about what practices stimulate new ideas and creative solutions, most innovation teams struggle to realize their benefits.

People’s intrinsic biases and behavioral habits inhibit the exercise of the imagination and protect unspoken assumptions about what will or will not work.

The Solution

Design thinking provides a structured process that helps innovators break free of counterproductive tendencies that thwart innovation. Like TQM, it is a social technology that blends practical tools with insights into human nature.

Occasionally, a new way of organizing work leads to extraordinary improvements. Total quality management did that in manufacturing in the 1980s by combining a set of tools—kanban cards, quality circles, and so on—with the insight that people on the shop floor could do much higher level work than they usually were asked to. That blend of tools and insight, applied to a work process, can be thought of as a social technology.

• JL Jeanne Liedtka is a professor of business administration at the University of Virginia’s Darden School of Business.

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Ideas Made to Matter

Design thinking, explained

Rebecca Linke

Sep 14, 2017

What is design thinking?

Design thinking is an innovative problem-solving process rooted in a set of skills.The approach has been around for decades, but it only started gaining traction outside of the design community after the 2008 Harvard Business Review article [subscription required] titled “Design Thinking” by Tim Brown, CEO and president of design company IDEO.

Since then, the design thinking process has been applied to developing new products and services, and to a whole range of problems, from creating a business model for selling solar panels in Africa to the operation of Airbnb .

At a high level, the steps involved in the design thinking process are simple: first, fully understand the problem; second, explore a wide range of possible solutions; third, iterate extensively through prototyping and testing; and finally, implement through the customary deployment mechanisms. 

The skills associated with these steps help people apply creativity to effectively solve real-world problems better than they otherwise would. They can be readily learned, but take effort. For instance, when trying to understand a problem, setting aside your own preconceptions is vital, but it’s hard.

Creative brainstorming is necessary for developing possible solutions, but many people don’t do it particularly well. And throughout the process it is critical to engage in modeling, analysis, prototyping, and testing, and to really learn from these many iterations.

Once you master the skills central to the design thinking approach, they can be applied to solve problems in daily life and any industry.

Here’s what you need to know to get started.

Understand the problem 

The first step in design thinking is to understand the problem you are trying to solve before searching for solutions. Sometimes, the problem you need to address is not the one you originally set out to tackle.

“Most people don’t make much of an effort to explore the problem space before exploring the solution space,” said MIT Sloan professor Steve Eppinger. The mistake they make is to try and empathize, connecting the stated problem only to their own experiences. This falsely leads to the belief that you completely understand the situation. But the actual problem is always broader, more nuanced, or different than people originally assume.

Take the example of a meal delivery service in Holstebro, Denmark. When a team first began looking at the problem of poor nutrition and malnourishment among the elderly in the city, many of whom received meals from the service, it thought that simply updating the menu options would be a sufficient solution. But after closer observation, the team realized the scope of the problem was much larger , and that they would need to redesign the entire experience, not only for those receiving the meals, but for those preparing the meals as well. While the company changed almost everything about itself, including rebranding as The Good Kitchen, the most important change the company made when rethinking its business model was shifting how employees viewed themselves and their work. That, in turn, helped them create better meals (which were also drastically changed), yielding happier, better nourished customers.

Involve users

Imagine you are designing a new walker for rehabilitation patients and the elderly, but you have never used one. Could you fully understand what customers need? Certainly not, if you haven’t extensively observed and spoken with real customers. There is a reason that design thinking is often referred to as human-centered design.

“You have to immerse yourself in the problem,” Eppinger said.

How do you start to understand how to build a better walker? When a team from MIT’s Integrated Design and Management program together with the design firm Altitude took on that task, they met with walker users to interview them, observe them, and understand their experiences.  

“We center the design process on human beings by understanding their needs at the beginning, and then include them throughout the development and testing process,” Eppinger said.

Central to the design thinking process is prototyping and testing (more on that later) which allows designers to try, to fail, and to learn what works. Testing also involves customers, and that continued involvement provides essential user feedback on potential designs and use cases. If the MIT-Altitude team studying walkers had ended user involvement after its initial interviews, it would likely have ended up with a walker that didn’t work very well for customers. 

It is also important to interview and understand other stakeholders, like people selling the product, or those who are supporting the users throughout the product life cycle.

The second phase of design thinking is developing solutions to the problem (which you now fully understand). This begins with what most people know as brainstorming.

Hold nothing back during brainstorming sessions — except criticism. Infeasible ideas can generate useful solutions, but you’d never get there if you shoot down every impractical idea from the start.

“One of the key principles of brainstorming is to suspend judgment,” Eppinger said. “When we're exploring the solution space, we first broaden the search and generate lots of possibilities, including the wild and crazy ideas. Of course, the only way we're going to build on the wild and crazy ideas is if we consider them in the first place.”

That doesn’t mean you never judge the ideas, Eppinger said. That part comes later, in downselection. “But if we want 100 ideas to choose from, we can’t be very critical.”

In the case of The Good Kitchen, the kitchen employees were given new uniforms. Why? Uniforms don’t directly affect the competence of the cooks or the taste of the food.

But during interviews conducted with kitchen employees, designers realized that morale was low, in part because employees were bored preparing the same dishes over and over again, in part because they felt that others had a poor perception of them. The new, chef-style uniforms gave the cooks a greater sense of pride. It was only part of the solution, but if the idea had been rejected outright, or perhaps not even suggested, the company would have missed an important aspect of the solution.

Prototype and test. Repeat.

You’ve defined the problem. You’ve spoken to customers. You’ve brainstormed, come up with all sorts of ideas, and worked with your team to boil those ideas down to the ones you think may actually solve the problem you’ve defined.

“We don’t develop a good solution just by thinking about a list of ideas, bullet points and rough sketches,” Eppinger said. “We explore potential solutions through modeling and prototyping. We design, we build, we test, and repeat — this design iteration process is absolutely critical to effective design thinking.”

Repeating this loop of prototyping, testing, and gathering user feedback is crucial for making sure the design is right — that is, it works for customers, you can build it, and you can support it.

“After several iterations, we might get something that works, we validate it with real customers, and we often find that what we thought was a great solution is actually only just OK. But then we can make it a lot better through even just a few more iterations,” Eppinger said.

Implementation

The goal of all the steps that come before this is to have the best possible solution before you move into implementing the design. Your team will spend most of its time, its money, and its energy on this stage.

“Implementation involves detailed design, training, tooling, and ramping up. It is a huge amount of effort, so get it right before you expend that effort,” said Eppinger.

Design thinking isn’t just for “things.” If you are only applying the approach to physical products, you aren’t getting the most out of it. Design thinking can be applied to any problem that needs a creative solution. When Eppinger ran into a primary school educator who told him design thinking was big in his school, Eppinger thought he meant that they were teaching students the tenets of design thinking.

“It turns out they meant they were using design thinking in running their operations and improving the school programs. It’s being applied everywhere these days,” Eppinger said.

In another example from the education field, Peruvian entrepreneur Carlos Rodriguez-Pastor hired design consulting firm IDEO to redesign every aspect of the learning experience in a network of schools in Peru. The ultimate goal? To elevate Peru’s middle class.

As you’d expect, many large corporations have also adopted design thinking. IBM has adopted it at a company-wide level, training many of its nearly 400,000 employees in design thinking principles .

What can design thinking do for your business?

The impact of all the buzz around design thinking today is that people are realizing that “anybody who has a challenge that needs creative problem solving could benefit from this approach,” Eppinger said. That means that managers can use it, not only to design a new product or service, “but anytime they’ve got a challenge, a problem to solve.”

Applying design thinking techniques to business problems can help executives across industries rethink their product offerings, grow their markets, offer greater value to customers, or innovate and stay relevant. “I don’t know industries that can’t use design thinking,” said Eppinger.

Ready to go deeper?

Read “ The Designful Company ” by Marty Neumeier, a book that focuses on how businesses can benefit from design thinking, and “ Product Design and Development ,” co-authored by Eppinger, to better understand the detailed methods.

Register for an MIT Sloan Executive Education course:

Systematic Innovation of Products, Processes, and Services , a five-day course taught by Eppinger and other MIT professors.

• Leadership by Design: Innovation Process and Culture , a two-day course taught by MIT Integrated Design and Management director Matthew Kressy.
• Managing Complex Technical Projects , a two-day course taught by Eppinger.
• Apply for M astering Design Thinking , a 3-month online certificate course taught by Eppinger and MIT Sloan senior lecturers Renée Richardson Gosline and David Robertson.

Steve Eppinger is a professor of management science and innovation at MIT Sloan. He holds the General Motors Leaders for Global Operations Chair and has a PhD from MIT in engineering. He is the faculty co-director of MIT's System Design and Management program and Integrated Design and Management program, both master’s degrees joint between the MIT Sloan and Engineering schools. His research focuses on product development and technical project management, and has been applied to improving complex engineering processes in many industries.

Read next: 10 agile ideas worth sharing

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Design Thinking (DT)

What is design thinking (dt).

Design thinking is a non-linear, iterative process that teams use to understand users, challenge assumptions, redefine problems and create innovative solutions to prototype and test. It is most useful to tackle ill-defined or unknown problems and involves five phases: Empathize, Define, Ideate, Prototype and Test.

• Transcript loading…

Why Is Design Thinking so Important?

“Design thinking is a human-centered approach to innovation that draws from the designer's toolkit to integrate the needs of people, the possibilities of technology, and the requirements for business success.”

— Tim Brown, CEO of IDEO

Design thinking fosters innovation . Companies must innovate to survive and remain competitive in a rapidly changing environment. In design thinking, cross-functional teams work together to understand user needs and create solutions that address those needs. Moreover, the design thinking process helps unearth creative solutions.

Design teams use design thinking to tackle ill-defined/unknown problems (aka wicked problems ). Alan Dix, Professor of Human-Computer Interaction, explains what wicked problems are in this video.

Wicked problems demand teams to think outside the box, take action immediately, and constantly iterate—all hallmarks of design thinking.

Don Norman, a pioneer of user experience design, explains why the designer’s way of thinking is so powerful when it comes to such complex problems.

Design thinking offers practical methods and tools that major companies like Google, Apple and Airbnb use to drive innovation. From architecture and engineering to technology and services, companies across industries have embraced the methodology to drive innovation and address complex problems. 

The End Goal of Design Thinking: Be Desirable, Feasible and Viable

The design thinking process aims to satisfy three criteria: desirability (what do people desire?), feasibility (is it technically possible to build the solution?) and viability (can the company profit from the solution?). Teams begin with desirability and then bring in the other two lenses.

© Interaction Design Foundation, CC BY-SA 4.0

Desirability: Meet People’s Needs

The design thinking process starts by looking at the needs, dreams and behaviors of people—the end users. The team listens with empathy to understand what people want, not what the organization thinks they want or need. The team then thinks about solutions to satisfy these needs from the end user’s point of view.

Feasibility: Be Technologically Possible

Once the team identifies one or more solutions, they determine whether the organization can implement them. In theory, any solution is feasible if the organization has infinite resources and time to develop the solution. However, given the team’s current (or future resources), the team evaluates if the solution is worth pursuing. The team may iterate on the solution to make it more feasible or plan to increase its resources (say, hire more people or acquire specialized machinery).

At the beginning of the design thinking process, teams should not get too caught up in the technical implementation. If teams begin with technical constraints, they might restrict innovation.

Viability: Generate Profits

A desirable and technically feasible product isn’t enough. The organization must be able to generate revenues and profits from the solution. The viability lens is essential not only for commercial organizations but also for non-profits. 

Traditionally, companies begin with feasibility or viability and then try to find a problem to fit the solution and push it to the market. Design thinking reverses this process and advocates that teams begin with desirability and bring in the other two lenses later.

The Five Stages of Design Thinking

Stanford University’s Hasso Plattner Institute of Design, commonly known as the d.school, is renowned for its pioneering approach to design thinking. Their design process has five phases: Empathize, Define, Ideate, Prototype, and Test. These stages are not always sequential. Teams often run them in parallel, out of order, and repeat them as needed.

Stage 1: Empathize —Research Users' Needs

The team aims to understand the problem, typically through user research. Empathy is crucial to design thinking because it allows designers to set aside your assumptions about the world and gain insight into users and their needs.

Stage 2: Define—State Users' Needs and Problems

Once the team accumulates the information, they analyze the observations and synthesize them to define the core problems. These definitions are called problem statements . The team may create personas to help keep efforts human-centered.

Stage 3: Ideate—Challenge Assumptions and Create Ideas

With the foundation ready, teams gear up to “think outside the box.” They brainstorm alternative ways to view the problem and identify innovative solutions to the problem statement.

Stage 4: Prototype—Start to Create Solutions

This is an experimental phase. The aim is to identify the best possible solution for each problem. The team produces inexpensive, scaled-down versions of the product (or specific features found within the product) to investigate the ideas. This may be as simple as paper prototypes .

Stage 5: Test—Try the Solutions Out

The team tests these prototypes with real users to evaluate if they solve the problem. The test might throw up new insights, based on which the team might refine the prototype or even go back to the Define stage to revisit the problem.

These stages are different modes that contribute to the entire design project rather than sequential steps. The goal is to gain a deep understanding of the users and their ideal solution/product.

Design Thinking Frameworks

There is no single definition or process for design thinking. The five-stage design thinking methodology described above is just one of several frameworks.

Hasso-Platner Institute Panorama

Ludwig Wilhelm Wall, CC BY-SA 3.0 , via Wikimedia Commons

Innovation doesn’t follow a linear path or have a clear-cut formula. Global design leaders and consultants have interpreted the abstract design process in different ways and have proposed other frameworks of design thinking.

Head, Heart and Hand by the American Institution of Graphic Arts (AIGA)

The Head, Heart, and Hand approach by AIGA (American Institute of Graphic Arts) is a holistic perspective on design. It integrates the intellectual, emotional, and practical aspects of the creative process.

More than a process, the Head, Heart and Hand framework outlines the different roles that designers must perform to create great results.

© American Institute of Graphic Arts, Fair Use

“ Head ” symbolizes the intellectual component. The team focuses on strategic thinking, problem-solving and the cognitive aspects of design. It involves research and analytical thinking to ensure that design decisions are purposeful.

“ Heart ” represents the emotional dimension. It emphasizes empathy, passion, and human-centeredness. This aspect is crucial in understanding the users’ needs, desires, and experiences to ensure that designs resonate on a deeper, more personal level.

“ Hand ” signifies the practical execution of ideas, the craftsmanship, and the skills necessary to turn concepts into tangible solutions. This includes the mastery of tools, techniques, and materials, as well as the ability to implement and execute design ideas effectively.

Inspire, Ideate, Implement by IDEO

IDEO is a leading design consultancy and has developed its own version of the design thinking framework.

IDEO’s design thinking process is a cyclical three-step process that involves Inspiration, Ideation and Implementation.

© IDEO, Public License

In the “ Inspire ” phase, the team focuses on understanding users’ needs, behaviors, and motivations. The team empathizes with people through observation and user interviews to gather deep insights.

In the “ Ideate ” phase, the team synthesizes the insights gained to brainstorm a wide array of creative solutions. This stage encourages divergent thinking, where teams focus on quantity and variety of ideas over immediate practicality. The goal is to explore as many possibilities as possible without constraints.

In the “ Implement ” phase, the team brings these ideas to life through prototypes. The team tests, iterates and refines these ideas based on user feedback. This stage is crucial for translating abstract concepts into tangible, viable products, services, or experiences.

The methodology emphasizes collaboration and a multidisciplinary approach throughout each phase to ensure solutions are innovative and deeply rooted in real human needs and contexts.

The Double Diamond by the Design Council

In the book Designing Social Systems in a Changing World , Béla Heinrich Bánáthy, Professor at San Jose State University and UC Berkeley, created a “divergence-convergence model” diagram. The British Design Council interpreted this diagram to create the Double Diamond design process model.

As the name suggests, the double diamond model consists of two diamonds—one for the problem space and the other for the solution space. The model uses diamonds to represent the alternating diverging and converging activities.

© Design Council, CC BY 4.0

In the diverging “ Discover ” phase, designers gather insights and empathize with users’ needs. The team then converges in the “ Define ” phase to identify the problem.

The second, solution-related diamond, begins with “ Develop ,” where the team brainstorms ideas. The final stage is “ Deliver ,” where the team tests the concepts and implements the most viable solution.

This model balances expansive thinking with focused execution to ensure that design solutions are both creative and practical. It underscores the importance of understanding the problem thoroughly and carefully crafting the solution, making it a staple in many design and innovation processes.

With the widespread adoption of the double diamond framework, Design Council’s simple visual evolved.

In this expanded and annotated version, the framework emphasizes four design principles:

Be people-centered.

Communicate (visually and inclusively).

Collaborate and co-create.

Iterate, iterate, iterate!

The updated version also highlights the importance of leadership (to create an environment that allows innovation) and engagement (to connect with different stakeholders and involve them in the design process).

Common Elements of Design Thinking Frameworks

On the surface, design thinking frameworks look very different—they use alternative names and have different numbers of steps. However, at a fundamental level, they share several common traits.

Start with empathy . Focus on the people to come up with solutions that work best for individuals, business, and society.

Reframe the problem or challenge at hand . Don’t rush into a solution. Explore the problem space and look at the issue through multiple perspectives to gain a more holistic, nuanced understanding.

Initially, employ a divergent style of thinking (analyze) . In the problem space, gather as many insights as possible. In the solution space, encourage team members to generate and explore as many solutions as possible in an open, judgment-free ideation space.

Later, employ a convergent style of thinking (synthesize) . In the problem space, synthesize all data points to define the problem. In the solution space, whittle down all the ideas—isolate, combine and refine potential solutions to create more mature ideas.

Create and test prototypes . Solutions that make it through the previous stages get tested further to remove potential issues.

Iterate . As the team progresses through the various stages, they revisit different stages and may redefine the challenge based on new insights.

Design thinking is a non-linear process. For example, teams may jump from the test stage to the define stage if the tests reveal insights that redefine the problem. Or, a prototype might spark a new idea, prompting the team to step back into the ideate stage. Tests may also create new ideas for projects or reveal insights about users.

Design Thinking Mindsets: More than a Process

A mindset is a characteristic mental attitude that determines how one interprets and responds to situations . Design thinking mindsets are how individuals think , feel and express themselves during design thinking activities. It includes people’s expectations and orientations during a design project.

Without the right mindset, it can be very challenging to change how we work and think.

The key mindsets that ensure a team can successfully implement design thinking are.

Be empathetic: Empathy is the ability to place yourself, your thinking and feelings in another person’s shoes. Design thinking begins from a deep understanding of the needs and motivations of people—the parents, neighbors, children, colleagues, and strangers who make up a community. 

Be collaborative: No one person is responsible for the outcome when you work in a team. Several great minds are always stronger than just one. Design thinking benefits from the views of multiple perspectives and lets others’ creativity bolster your own.

Be optimistic: Be confident about achieving favorable outcomes. Design thinking is the fundamental belief that we can all create change—no matter how big a problem, how little time, or how small a budget. Designing can be a powerful process no matter what constraints exist around you.

Embrace ambiguity: Get comfortable with ambiguous and complex situations. If you expect perfection, it is difficult to take risks, which limits your ability to create radical change. Design thinking is all about experimenting and learning by doing. It gives you the confidence to believe that new, better things are possible and that you can help make them a reality. 

Be curious: Be open to different ideas. Recognize that you are not the user.

Reframe: Challenge and reframe assumptions associated with a given situation or problem. Don’t take problems at face value. Humans are primed to look for patterns. The unfortunate side effect of these patterns is that we form (often false and sometimes dangerous) stereotypes and assumptions. Design thinking aims to help you break through any preconceived notions and biases and reframe challenges.

Embrace diversity: Work with and engage people with different cultural backgrounds, experiences, and ways of thinking and working. Everyone brings a unique perspective to the team. When you include diverse voices in a team, you learn from each other’s experiences, further helping you break through your assumptions.

Make tangible: When you make ideas tangible, it is faster and easier for everyone on the team to be on the same page. For example, sketching an idea or enacting a scenario is far more convenient and easy to interpret than an elaborate presentation or document.

Take action: Run experiments and learn from them.

Design Thinking vs Agile Methodology

Teams often use design thinking and agile methodologies in project management, product development, and software development. These methodologies have distinct approaches but share some common principles.

Similarities between Design Thinking and Agile

Iterative process.

Both methodologies emphasize iterative development. In design thinking, teams may jump from one phase to another, not necessarily in a set cyclical or linear order. For example, on testing a prototype, teams may discover something new about their users and realize that they must redefine the problem. Agile teams iterate through development sprints.

User-Centered

The agile and design thinking methodologies focus on the end user. All design thinking activities—from empathizing to prototyping and testing—keep the end users front and center. Agile teams continually integrate user feedback into development cycles.

Collaboration and Teamwork

Both methodologies rely heavily on collaboration among cross-functional teams and encourage diverse perspectives and expertise.

Flexibility and Adaptability

With its focus on user research, prototyping and testing, design thinking ensures teams remain in touch with users and get continuous feedback. Similarly, agile teams monitor user feedback and refine the product in a reasonably quick time.

In this video, Laura Klein, author of Build Better Products , describes a typical challenge designers face on agile teams. She encourages designers to get comfortable with the idea of a design not being perfect. Notice the many parallels between Laura’s advice for designers on agile teams and the mindsets of design thinking.

Differences between Design Thinking and Agile

While design thinking and agile teams share principles like iteration, user focus, and collaboration, they are neither interchangeable nor mutually exclusive. A team can apply both methodologies without any conflict.

From a user experience design perspective, design thinking applies to the more abstract elements of strategy and scope. At the same time, agile is more relevant to the more concrete elements of UX: structure, skeleton and surface. For quick reference, here’s an overview of the five elements of user experience.

Design thinking is more about exploring and defining the right problem and solution, whereas agile is about efficiently executing and delivering a product.

Here are the key differences between design thinking and agile.

Design Sprint: A Condensed Version of Design Thinking

A design sprint is a 5-day intensive workshop where cross-functional teams aim to develop innovative solutions.

The design sprint is a very structured version of design thinking that fits into the timeline of a sprint (a sprint is a short timeframe in which agile teams work to produce deliverables). Developed by Google Ventures, the design sprint seeks to fast-track innovation.

In this video, user researcher Ditte Hvas Mortensen explains the design sprint in detail.

Learn More about Design Thinking

Design consultancy IDEO’s designkit is an excellent repository of design thinking tools and case studies.

To keep up with recent developments in design thinking, read IDEO CEO Tim Brown’s blog .

Enroll in our course Design Thinking: The Ultimate Guide —an excellent guide to get you started on your design thinking projects.

Questions related to Design Thinking

You don’t need any certification to practice design thinking. However, learning about the nuances of the methodology can help you:

Pick the appropriate methods and tailor the process to suit the unique needs of your project.

Avoid common pitfalls when you apply the methods.

Better lead a team and facilitate workshops.

Increase the chances of coming up with innovative solutions.

IxDF has a comprehensive course to help you gain the most from the methodology: Design Thinking: The Ultimate Guide .

Anyone can apply design thinking to solve problems. Despite what the name suggests, non-designers can use the methodology in non-design-related scenarios. The methodology helps you think about problems from the end user’s perspective. Some areas where you can apply this process:

Develop new products with greater chances of success.

Address community-related issues (such as education, healthcare and environment) to improve society and living standards.

Innovate/enhance existing products to gain an advantage over the competition.

Achieve greater efficiencies in operations and reduce costs.

Use the Design Thinking: The Ultimate Guide course to apply design thinking to your context today.

A framework is the basic structure underlying a system, concept, or text. There are several design thinking frameworks with slight differences. However, all the frameworks share some traits. Each framework: 

Begins with empathy.

Reframes the problem or challenge at hand.

Initially employs divergent styles of thinking to generate ideas.

Later, it employs convergent styles of thinking to narrow down the best ideas,

Creates and tests prototypes.

Iterates based on the tests.

Some of the design thinking frameworks are:

5-stage design process by d.school

7-step early traditional design process by Herbert Simon

The 5-Stage DeepDive™ by IDEO

The “Double Diamond” Design Process Model by the Design Council

Collective Action Toolkit (CAT) by Frog Design

The LUMA System of Innovation by LUMA Institute

For details about each of these frameworks, see 10 Insightful Design Thinking Frameworks: A Quick Overview .

IDEO’s 3-Stage Design Thinking Process consists of inspiration, ideation and implementation:

Inspire : The problem or opportunity inspires and motivates the search for a solution.

Ideate : A process of synthesis distills insights which can lead to solutions or opportunities for change.

Implement : The best ideas are turned into a concrete, fully conceived action plan.

IDEO is a leader in applying design thinking and has developed many frameworks. Find out more in 10 Insightful Design Thinking Frameworks: A Quick Overview .

Design Council's Double Diamond diagram depicts the divergent and convergent stages of the design process.

Béla H. Bánáthy, founder of the White Stag Leadership Development Program, created the “divergence-convergence” model in 1996. In the mid-2000s, the British Design Council made this famous as the Double Diamond model.

The Double Diamond diagram graphically represents a design thinking process. It highlights the divergent and convergent styles of thinking in the design process. It has four distinct phases:

Discover: Initial idea or inspiration based on user needs.

Define: Interpret user needs and align them with business objectives.

Develop: Develop, iterate and test design-led solutions.

Deliver: Finalize and launch the end product into the market.

Double Diamond is one of several design thinking frameworks. Find out more in 10 Insightful Design Thinking Frameworks: A Quick Overview .

There are several design thinking methods that you can choose from, depending on what stage of the process you’re in. Here are a few common design thinking methods:

User Interviews: to understand user needs, pain points, attitudes and behaviors.

5 Whys Method: to dig deeper into problems to diagnose the root cause.

User Observations: to understand how users behave in real life (as opposed to what they say they do).

Affinity Diagramming: to organize research findings.

Empathy Mapping: to empathize with users based on research insights.

Journey Mapping: to visualize a user’s experience as they solve a problem.

6 Thinking Hats: to encourage a group to think about a problem or solution from multiple perspectives.

Brainstorming: to generate ideas.

Prototyping: to make abstract ideas more tangible and test them.

Dot Voting: to select ideas.

Start applying these methods to your work today with the Design Thinking template bundle .

For most of the design thinking process, you will need basic office stationery:

Pen and paper

Sticky notes

Whiteboard and markers

Print-outs of templates and canvases as needed (such as empathy maps, journey maps, feedback capture grid etc.) You can also draw these out manually.

Prototyping materials such as UI stencils, string, clay, Lego bricks, sticky tapes, scissors and glue.

A space to work in.

You can conduct design thinking workshops remotely by:

Using collaborative software to simulate the whiteboard and sticky notes.

Using digital templates instead of printed canvases.

Download print-ready templates you can share with your team to practice design thinking today.

Design thinking is a problem-solving methodology that helps teams better identify, understand, and solve business and customer problems.

When businesses prioritize and empathize with customers, they can create solutions catering to their needs. Happier customers are more likely to be loyal and organically advocate for the product.

Design thinking helps businesses develop innovative solutions that give them a competitive advantage.

Gain a competitive advantage in your business with Design Thinking: The Ultimate Guide .

The evolution of Design Thinking can be summarised in 8 key events from the 1960s to 2004.

© Interaction Design Foundation, CC BY-SA 4.0.

Herbert Simon’s 1969 book, "The Sciences of the Artificial," has one of the earliest references to design thinking. David Kelley, founder of the design consultancy IDEO, coined the term “design thinking” and helped make it popular.

For a more comprehensive discussion on the origins of design thinking, see The History of Design Thinking .

Some organizations that have employed design thinking successfully are:

Airbnb: Airbnb used design thinking to create a platform for people to rent out their homes to travelers. The company focused on the needs of both hosts and guests . The result was a user-friendly platform to help people find and book accommodations.

PillPack: PillPack is a prescription home-delivery system. The company focused on the needs of people who take multiple medications and created a system that organizes pills by date and time. Amazon bought PillPack in 2018 for $1 billion .

Google Creative Lab: Google Creative Lab collaborated with IDEO to discover how kids physically play and learn. The team used design thinking to create Project Bloks . The project helps children develop foundational problem-solving skills "through coding experiences that are playful, tactile and collaborative.”

See more examples of design thinking and learn practical methods in Design Thinking: The Ultimate Guide .

Innovation essentially means a new idea. Design thinking is a problem-solving methodology that helps teams develop new ideas. In other words, design thinking can lead to innovation.

Human-Centered Design is a newer term for User-Centered Design

“Human-centred design is an approach to interactive systems development that aims to make systems usable and useful by focusing on the users, their needs and requirements, and by applying human factors/ergonomics, and usability knowledge and techniques. This approach enhances effectiveness and efficiency, improves human well-being, user satisfaction, accessibility and sustainability; and counteracts possible adverse effects of use on human health, safety and performance.”

— ISO 9241-210:2019(en), ISO (the International Organization for Standardization)  

User experience expert Don Norman describes human-centered design (HCD) as a more evolved form of user-centered design (UCD). The word "users" removes their importance and treats them more like objects than people. By replacing “user” with “human,” designers can empathize better with the people for whom they are designing. Don Norman takes HCD a step further and prefers the term People-Centered Design.

Design thinking has a broader scope and takes HCD beyond the design discipline to drive innovation.

People sometimes use design thinking and human-centered design to mean the same thing. However, they are not the same. HCD is a formal discipline with a specific process used only by designers and usability engineers to design products. Design thinking borrows the design methods and applies them to problems in general.

Design Sprint condenses design thinking into a 1-week structured workshop

Google Ventures condensed the design thinking framework into a time-constrained 5-day workshop format called the Design Sprint. The sprint follows one step per day of the week:

Monday: Unpack

Tuesday: Sketch

Wednesday: Decide

Thursday: Prototype

Friday: Test

Learn more about the design sprint in Make Your UX Design Process Agile Using Google’s Methodology .

Systems Thinking is a distinct discipline with a broader approach to problem-solving

“Systems thinking is a way of exploring and developing effective action by looking at connected wholes rather than separate parts.”

— Introduction to Systems thinking, Report of GSE and GORS seminar, Civil Service Live

Both HCD and Systems Thinking are formal disciplines. Designers and usability engineers primarily use HCD. Systems thinking has applications in various fields, such as medical, environmental, political, economic, human resources, and educational systems.

HCD has a much narrower focus and aims to create and improve products. Systems thinking looks at the larger picture and aims to change entire systems.

Don Norman encourages designers to incorporate systems thinking in their work. Instead of looking at people and problems in isolation, designers must look at them from a systems point of view.

In summary, UCD and HCD refer to the same field, with the latter being a preferred phrase.

Design thinking is a broader framework that borrows methods from human-centered design to approach problems beyond the design discipline. It encourages people with different backgrounds and expertise to work together and apply the designer’s way of thinking to generate innovative solutions to problems.

Systems thinking is another approach to problem-solving that looks at the big picture instead of specific problems in isolation.

The design sprint is Google Ventures’ version of the design thinking process, structured to fit the design process in 1 week.

There are multiple design thinking frameworks, each with a different number of steps and phase names. One of the most popular frameworks is the Stanford d.School 5-stage process.

Design thinking is an iterative and non-linear process. It contains five phases: 1. Empathize, 2. Define, 3. Ideate, 4. Prototype and 5. Test. It is important to note the five stages of design thinking are not always sequential. They do not have to follow a specific order, and they can often occur in parallel or be repeated iteratively. The stages should be understood as different modes which contribute to the entire design project, rather than sequential steps.

For more details, see The 5 Stages in the Design Thinking Process .

IDEO is a leading design consultancy and has developed its own version of the design thinking framework and adds the dimension of implementation in the process.

IDEO’s framework uses slightly different terms than d.school’s design thinking process and adds an extra dimension of implementation. The steps in the DeepDive™ Methodology are: Understand, Observe, Visualize, Evaluate and Implement.

IDEO’s DeepDive™ Methodology includes the following steps:

Understand: Conduct research and identify what the client needs and the market landscape

Observe: Similar to the Empathize step, teams observe people in live scenarios and conduct user research to identify their needs and pain points.

Visualize: In this step, the team visualizes new concepts. Similar to the Ideate phase, teams focus on creative, out-of-the-box and novel ideas.

Evaluate: The team prototypes ideas and evaluates them. After refining the prototypes, the team picks the most suitable one.

Implement: The team then sets about to develop the new concept for commercial use.

IDEO’s DeepDive™ is one of several design thinking frameworks. Find out more in 10 Insightful Design Thinking Frameworks: A Quick Overview .

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What are the stages in the design thinking process?

• Brainstorm, Prototype, Design, Launch, Test
• Define, Ideate, Research, Design, Test
• Empathize, Define, Ideate, Prototype, Test

Why is empathy critical in the design thinking process?

• It allows designers to understand and address the real needs of users.
• It helps designers maintain control over the creative process.
• It makes sure the solution is inexpensive and easy to create.

What is the primary purpose of the prototyping phase in design thinking?

• To explore potential solutions and how they might work in real-world situations
• To finalize the product design for mass production
• To sell the idea to stakeholders with a high-fidelity (hi-fi) demonstration

What is a "wicked problem" in design thinking?

• Problems that are complex, ill-defined and have no single correct answer.
• Problems that are straightforward and have a clear, single solution.
• Problems that are tricky, but can be solved quickly with conventional methods.

Why is the iterative process important in design thinking?

• It allows design teams to use up all available resources.
• It allows for the improvement of solutions based on user feedback and testing.
• It makes sure the solution remains unchanged throughout development.

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Literature on Design Thinking (DT)

Here’s the entire UX literature on Design Thinking (DT) by the Interaction Design Foundation, collated in one place:

Learn more about Design Thinking (DT)

Take a deep dive into Design Thinking (DT) with our course Design Thinking: The Ultimate Guide .

Some of the world’s leading brands, such as Apple, Google, Samsung, and General Electric, have rapidly adopted the design thinking approach, and design thinking is being taught at leading universities around the world, including Stanford d.school, Harvard, and MIT. What is design thinking, and why is it so popular and effective?

Design Thinking is not exclusive to designers —all great innovators in literature, art, music, science, engineering and business have practiced it. So, why call it Design Thinking? Well, that’s because design work processes help us systematically extract, teach, learn and apply human-centered techniques to solve problems in a creative and innovative way—in our designs, businesses, countries and lives. And that’s what makes it so special.

The overall goal of this design thinking course is to help you design better products, services, processes, strategies, spaces, architecture, and experiences. Design thinking helps you and your team develop practical and innovative solutions for your problems. It is a human-focused , prototype-driven , innovative design process . Through this course, you will develop a solid understanding of the fundamental phases and methods in design thinking, and you will learn how to implement your newfound knowledge in your professional work life. We will give you lots of examples; we will go into case studies, videos, and other useful material, all of which will help you dive further into design thinking. In fact, this course also includes exclusive video content that we've produced in partnership with design leaders like Alan Dix, William Hudson and Frank Spillers!

This course contains a series of practical exercises that build on one another to create a complete design thinking project. The exercises are optional, but you’ll get invaluable hands-on experience with the methods you encounter in this course if you complete them, because they will teach you to take your first steps as a design thinking practitioner. What’s equally important is you can use your work as a case study for your portfolio to showcase your abilities to future employers! A portfolio is essential if you want to step into or move ahead in a career in the world of human-centered design.

Design thinking methods and strategies belong at every level of the design process . However, design thinking is not an exclusive property of designers—all great innovators in literature, art, music, science, engineering, and business have practiced it. What’s special about design thinking is that designers and designers’ work processes can help us systematically extract, teach, learn, and apply these human-centered techniques in solving problems in a creative and innovative way—in our designs, in our businesses, in our countries, and in our lives.

That means that design thinking is not only for designers but also for creative employees , freelancers , and business leaders . It’s for anyone who seeks to infuse an approach to innovation that is powerful, effective and broadly accessible, one that can be integrated into every level of an organization, product, or service so as to drive new alternatives for businesses and society.

You earn a verifiable and industry-trusted Course Certificate once you complete the course. You can highlight them on your resume, CV, LinkedIn profile or your website .

All open-source articles on Design Thinking (DT)

What is design thinking and why is it so popular.

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Stage 2 in the Design Thinking Process: Define the Problem and Interpret the Results

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Affinity Diagrams: How to Cluster Your Ideas and Reveal Insights

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Stage 4 in the Design Thinking Process: Prototype

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Stage 3 in the Design Thinking Process: Ideate

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Stage 1 in the Design Thinking Process: Empathise with Your Users

Empathy Map – Why and How to Use It

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Design Thinking: New Innovative Thinking for New Problems

Test Your Prototypes: How to Gather Feedback and Maximize Learning

The History of Design Thinking

The Ultimate Guide to Understanding UX Roles and Which One You Should Go For

Stage 5 in the Design Thinking Process: Test

What Are Wicked Problems and How Might We Solve Them?

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World Leaders in Research-Based User Experience

Design thinking in practice: research methodology.

January 10, 2021 2021-01-10

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Project Overview 

Over the last decade, we have seen design thinking gain popularity across industries. Nielsen Norman Group conducted a long-term research project to understand design thinking in practice. The research project included 3 studies involving more than 1000 participants and took place from 2018 to 2020: 

• Intercepts and interviews with 87 participants
• Digital survey with 1067 respondents
• In-depth case study at an institution practicing design thinking 

The primary goals of the project were to investigate the following:

• How do practitioners learn and use design thinking?
• How does design thinking provide value to individuals and organizations?
• What makes design thinking successful or unsuccessful? 

This description of what we did may be useful in helping you interpret our results and apply them to your own design-thinking practice. 

Project Findings

The findings from this research are shared in the following articles and videos:

• What Is Design Thinking, Really? (What Practitioners Say) (Article) 
• How UX Professionals Define Design Thinking in Practice (Video) 
• Design Thinking: The Learner’s Journey (Article)

In This Article:

Study 1: intercepts and interviews , study 2:  digital survey, study 3: case study .

In the first study we investigated how UX and design professionals define design thinking.  

This study consisted of 71 in-person intercepts in Washington DC, San Francisco, Boston, and North Carolina and 16 remote interviews over the phone and via video conferencing. These 87 participants were UX professionals from a diverse range of countries with varying roles and experience.

Intercepts consisted of two questions:

• What do think of when you hear the phrase “design thinking”?
• How would you define design thinking?

Interviews consisted of 10 questions, excluding demographic-related questions:

• What are the first words that come to mind when I say “design thinking”?
• Can you tell me more about [word they supplied in response to question 1]?
• How would you define design thinking? Why?
• What does it mean to practice design thinking?
• What are the positive or negative effects of design thinking?
• Products and services
• Clients/customers
• Using this scale, what is your experience using design thinking?
• Using this same scale, how successful has design thinking been in your experience?
• What could have been better?
• What is good about design thinking? What is bad about design thinking?

Our second study consisted of a qualitative digital survey that ran for two months and had 1067 professional respondents primarily from UX-related fields. The survey had 14 questions, excluding demographic-related questions. An alternative set of 4 questions was shown to those with little to no experience using design thinking.  

• Which of the following best describes your experience with design thinking?
• Where did you learn design thinking?  
• UX maturity 
• Frequency of crossteam collaboration 
• User-centered approach 
• Research-driven decision making
• How often do you, yourself, practice design thinking?
• In your own words, what does it mean to practice design thinking? 
• When do you use design thinking?
• What methods or exercises are used?
• In what situations is each one used and why?
• Which ones are done individually versus as a group?
• How is each exercise executed?
• Gives your organization a competitive advantage
• Drives innovation
• Fosters collaboration
• Provides structure to the organization
• Increases likelihood of success
• Please describe a situation where design thinking positively influenced your organization and why it was successful. 
• Please describe a situation where design thinking may have negatively influenced your organization and why it was negative. 
• Design thinking negatively affects efficiency.
• Design thinking requires a collaborative environment to work well.
• Anyone can learn and practice design thinking.
• Design thinking is rigid.
• Design thinking requires all involved to be human-centered.
• Design thinking takes a lot of time.
• Design thinking has low return on investment.
• Design thinking empowers personal growth.
• Design thinking grows interpersonal relationships.
• Design thinking improves organizational progress.

The 1067 survey participants had diverse backgrounds: they held varying roles across industries and were located across the globe. 94 responses were invalid, so we excluded them from our analysis.  

The majority of participants (33%) were UX designers, followed by UX researchers (13%) and UX consultants (12%). 

Of participants who responded “Other”, the most common response provided was an executive role (n=20). This included roles such as CEO, VP, director, founder, and “head of.” Other mentioned roles included service designer (n=17), manager (n=14), business designer or business analyst (n=11), and educator (including teacher, instructor, and curriculum designer) (n=11).

Geographically, we had respondents from 67 different countries. The majority of survey participants work in the United States (34%), followed by India (8%), United Kingdom (7%), and Canada (5%). 

Our survey participants also represented diverse industries, with the majority in software (22%) and finance or insurance (14%). 

Of participants who responded Other , the most common response provided was agency or consulting (n=26), followed by telecommunications (n=17), marketing (n=8), and tourism (n=7).

Our third and final study consisted of an in-person case study at a large, public ecommerce company. The case study involved 9 interviews with company employees, 6 observation sessions of design-thinking (or related) workshops, and an internal resource and literature audit. 

The interviews were 1-hour long and semistructured. Of the 8 participants, 3 were on the same team but had different roles: 1 UX designer, 1 product manager, and 1 engineer. The other 5 interviewees (3 design leaders and 2 UX designers) worked in different groups across the organization. Each participant completed the same digital survey from the second study prior to interviewing.    

In addition to interviews, we conducted 6 observation sessions: 3 design-thinking workshops, 2 meetings, and 1 lunch-and-learn. After the workshops, all participants were invited to fill out a survey about the workshop. The survey had 5 questions: 

• We achieved our goal of [x]. 
• The time and resources spent to conduct the workshop were worth it.
• What aspects were of greatest value to you, and why? 
• Where there any aspects you felt were not useful, and why?
• Will the workshop or its output impact any of your future work? If so, how?
• What is your role?

Lastly, we conducted a resource and literature audit of the company’s internal resources related to design thinking available to employees.  

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PERSPECTIVE article

Leveraging systems science and design thinking to advance implementation science: moving toward a solution-oriented paradigm.

• 1 Center for Systems and Community Design and NYU-CUNY Prevention Research Center, Graduate School of Public Health & Health Policy, City University of New York, New York, NY, United States
• 2 EAC Health and Nutrition, LLC, Leesburg, VA, United States
• 3 School of Medicine, Wake Forest University, Winston-Salem, NC, United States
• 4 School of Social Work, Brigham Young University, Provo, UT, United States
• 5 d.studio, Charlotte, NC, United States
• 6 Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, United States
• 7 Center for Systems and Community Design, Graduate School of Public Health & Health Policy, City University of New York, New York, NY, United States
• 8 Center for Community Health Integration, School of Medicine, Case Western Reserve University, Cleveland, OH, United States

Many public health challenges are characterized by complexity that reflects the dynamic systems in which they occur. Such systems involve multiple interdependent factors, actors, and sectors that influence health, and are a primary driver of challenges of insufficient implementation, sustainment, and scale of evidence-based public health interventions. Implementation science frameworks have been developed to help embed evidence-based interventions in diverse settings and identify key factors that facilitate or hinder implementation. These frameworks are largely static in that they do not explain the nature and dynamics of interrelationships among the identified determinants, nor how those determinants might change over time. Furthermore, most implementation science frameworks are top-down, deterministic, and linear, leaving critical gaps in understanding of both how to intervene on determinants of successful implementation and how to scale evidence-based solutions. Design thinking and systems science offer methods for transforming this problem-oriented paradigm into one that is solution-oriented. This article describes these two approaches and how they can be integrated into implementation science strategies to promote implementation, sustainment, and scaling of public health innovation, ultimately resulting in transformative systems changes that improve population health.

1 Introduction

Many persistent public health challenges are marked by dynamic complexity that makes them particularly difficult for practitioners and policymakers to address ( 1 ). A key characteristic of such problems—for example, rising prevalence of obesity ( 2 ) and widespread opioid addiction and dependence ( 3 )—is involvement of multiple heterogenous factors, actors, and sectors that affect relevant behaviors and health outcomes. Additional key characteristics include interconnectivity and interdependencies with other problems; persistence over time and adaptation to changing circumstances; high economic and/or political stakes; and lack of agreement or clarity regarding solutions ( 4 ).

This inherent complexity is a primary driver of the present challenges of insufficient implementation, sustainment, and scale of evidence-based public health interventions. It has been estimated that about half of available public health innovations are used in practice, ( 5 ) and that it takes 17 years for just 14% of evidence-based research outcomes to be implemented in real-world settings ( 6 , 7 ). Moreover, many evidence-based public health interventions are seldom sustained, with limited funding and resources identified as a primary barrier ( 8 ).

A goal of implementation science for public health is to identify the factors, processes, and methods that can successfully embed evidence-based interventions in policy and practice, hastening the translation from discovery to application and population health benefits ( 9 ). Multiple implementation science frameworks have been developed to offer strategies to generalize findings across diverse settings, identify implementation determinants (e.g., contextual barriers and facilitators), inform data collection, enhance conceptual clarity, and guide implementation planning ( 10 ). Identifying key factors that facilitate or hinder implementation is beneficial, but many of the frameworks are static in that they do not explain the nature and dynamics of interrelationships among the identified determinants nor how those determinants might change over time. Many implementation science frameworks are top-down and deterministic ( 11 ), although some recent advances have been made to address community engagement in intervention implementation ( 12 ) and to guide adaptations during program implementation ( 13 ). Most implementation science frameworks, however, do not necessarily pinpoint the most promising focus areas or determinants, let alone how to intervene on them (i.e., how to select, design, tailor, and deliver implementation strategies) ( 14 ).

Design thinking and systems science offer methods for transforming a traditionally problem-oriented paradigm into one that is solution-oriented. We propose that integrating these two approaches into implementation science strategies can promote implementation, sustainment, and scaling of public health innovation, ultimately resulting in transformative systems changes that improve population health. Herein we describe how design thinking and systems science can be leveraged to advance implementation science, first by describing each approach and its benefits and then discussing how the two can be integrated in implementation science approaches to fill gaps.

2 Innovating through design thinking

Design thinking is a human-centered approach to innovation that includes methods from the designer’s toolkit to solve problems through creativity. A design thinking approach embraces complexity and provides a methodologically deliberate, non-linear way of developing an understanding of and structural empathy ( 15 ) with the populations affected by an issue. It reframes an issue to generate new ideas or surprising solutions by rapidly prototyping and testing the ideas and learning from them in an iterative manner ( 16 ). Key stages in the design thinking process have been summarized as moving from problem identification to design challenge opportunity by empathizing, defining, ideating, prototyping, and testing new solutions for refinement. The process is ultimately action-oriented and focused on solving implementation challenges identified by current implementation science frameworks. Figure 1 highlights stages in the design thinking process and lists exercises used or questions asked in each stage.

Figure 1 . A process model of design thinking. Source: adapted from Altman et al. ( 16 ), https://www.cdc.gov/pcd/issues/2018/18_0128a.htm#1 .

These dynamic stages can be performed multiple times as the design challenge solution is iterated through experimentation, testing, and refining of ideas toward a feasible solution. Because the process emphasizes rapid and iterative ideation, prototyping, and testing of solution ideas in an environment where it is safe—and even encouraged—to repeatedly fail, lessons learned about what leads to failure can be rapidly parlayed into new and improved solutions ideas ( 14 ). For example, a Danish project that investigated how health professionals practice shared decision-making with cancer patients was successful in using design thinking methods to better understand patients’ needs before, during, and after treatment and in prototyping novel potential solutions for supporting and empowering patients during their treatment ( 17 ). In another case, a human-centered design approach was used to improve feasibility and acceptability of an HIV response intervention that promoted patient-centered care practices among health care workers in Zambia ( 18 ).

The design thinking process can also generate insights about usability and fit of evidence-based community health strategies based on user needs and contexts, as well as other implementation factors such as feasibility and complexity ( 19 ). For example, Haines et al. used a design thinking approach to promote implementation and effectiveness of a care coordination intervention for young adults with cancer, concluding that the approach helped harmonize evidence-based practices, contexts, and implementation strategies so that the intervention and its delivery were best designed to fit the implementation context ( 20 ).

Whereas design thinking concepts have been applied in business, engineering, social services, and health care ( 21 ), training in and use of this approach is relatively new in public health. Evidence is accumulating, however, to suggest design thinking’s utility for strategizing the application of evidence-based public health interventions. For example, design thinking has been used to develop mobile health interventions, ( 22 ) to promote healthy eating and physical activity in schools, ( 23 , 24 ) and has been integrated with community engagement to address violence-related health disparities among Latino youth ( 25 ) and to ideate potential solutions to increase neighborhood park use ( 26 ). The combination of design thinking and implementation science approaches has been promoted for its potential to improve translation of evidence-based interventions into real-world settings ( 27 ). It has been suggested that design thinking methods can be used to address difficulties encountered during such translation ( 28 ), and to consistently operationalize implementation strategies ( 29 ).

As a human-centered approach, design thinking provides public health researchers and practitioners a systematic way to engage communities in co-producing solutions that align with community-identified needs, values, preferences, and assets. In other words, design thinking is set up to develop a contextually specific solution that solves the user’s “pain point” ( 30 ). For example, design charettes (i.e., collaborative workshops) are commonly used to engage participants to empathetically explore end-user experiences in order to tailor implementation strategies to the specific context of each community. The empathic, solution-oriented process of design thinking can also be effective in fostering social relationships within community groups, which contributes to the effectiveness of such groups in activating and sustaining social infrastructure ( 31 ).

Though more research is needed, the growing body of literature suggests that design thinking allows for more innovative, strategic, and contextually tailored intervention designs, which may increase participant adoption and maintenance of health behaviors. Furthermore, iterative problem solving associated with design thinking could support ongoing adaptation of evidence-based interventions to promote intervention sustainability in dynamic contexts.

3 Addressing complexity with systems science

Systems science is a broad term referring to the scientific understanding of complex adaptive systems, ( 32 ) as well as a set of tools to study the behavior of complex systems with applications ranging from developing theory to forecasting outcomes of interventions and informing policy. Systems science techniques are well-suited to examine dynamic, multi-level, and non-linear relationships and feedback loops that exist within evidence-based interventions and implementation contexts and strategies, making systems science a promising support for implementation sustainment and scale-up ( 33 , 34 ).

The focus of implementation science during the past decade has shifted toward studying how to accelerate translation of evidence-based interventions into policy and practice, considering the complex, adaptive systems in which such interventions are implemented ( 9 ). The introduction of systems thinking as an effort to bring insights from systems science to implementation science challenges the thinking that implementation is a simple linear process, or that participatory research in itself will improve the use of evidence-based interventions in practice and policy settings ( 35 ). The use and sustainability of an evidence-based intervention is dependent on multiple, multi-level, dynamic processes ( 35 , 36 ). The implication of this transition to systems thinking is that scientists must consider how to move their methods of investigation beyond a reductionist, isolated focus on a single part of a system toward a more holistic view ( 9 ).

Systems science includes informal causal mapping, mapping of social networks, and plotting of spatial relationships, as well as more formal mathematical modeling and computer simulation. Causal mapping and formal modeling with computer simulation are used to study system components and their dynamic interactions at multiple levels to better understand the behavior of complex systems ( 37 ). System science also includes methods for engaging actors directly in conceptualizing problems and participating in the development, interpretation, and transfer of ownership of results (e.g., soft systems methodology, participatory systems modeling, group model building, and community-based system dynamics) ( 38 , 39 ).

The goals of participatory systems science approaches are to build a common vocabulary and agenda to describe a complex problem and its drivers, design potential solutions, and garner buy-in to implement the solutions that rise to the top after a range of options have been assessed with quantitative modeling approaches ( 40 ). For example, participatory systems science approaches have been applied to help community stakeholders in the HEALing Communities Study-New York State (HCS-NY) develop a shared understanding of the opioid crisis in order to inform local strategies for prevention and treatment ( 41 ). Community stakeholders who participated in the New York effort reported that the causal mapping approach helped them see the interconnectivity of complex factors, actors, and sectors and appreciate the need for multiple, mutually reinforcing strategies to avert opioid overdose and fatality.

Formal modeling with simulation methods have been used in public health research to increase the rigor of understanding complexity underlying public health challenges and to uncover novel ways to intervene in the system to solve real-world problems ( 4 ). Computer simulation modeling approaches are also used to test the potential impact of policies or interventions through simulations in which researchers create a virtual representation of a system ( 1 ). The computer simulation plays out the anticipated behavior of the system based on the input parameters, which can be changed to mirror various potential scenarios. This is particularly useful for interventions and policies that are time-consuming, expensive, and/or infeasible to test in controlled trials or in real-world settings.

The knowledge generated from such systems modeling can inform research and policy decision making, such as helping policymakers leverage an ideal combination of interventions to create the most impact or prioritize use of limited resources, as well as identifying potential unintended consequences of a modeled intervention. Unintended consequences may arise because complex systems are governed by feedback loops, time delays, and process of accumulation, which are not fully understood and usually ignored when designing interventions ( 1 ). This has important implications that need to be considered a priori , such as how to combine and sequence intervention strategies ( 14 ).

4 Integrating design thinking and systems science

Design thinking or systems science approaches can be used on their own, but embedding systems science approaches into the design thinking process can enhance design thinking’s usefulness as a framework to facilitate an iterative process toward systems change. Thus, certain systems science methods can enhance problem definition, while others can support the prototyping and testing phases.

If design thinking methods are most helpful in ‘packaging’ strategies (tools, devices, procedures) that will help achieve a desired objective or goal, then systems science methods are most helpful in defining and testing, via simulation, a theory of change, expressed as an overarching dynamic hypothesis. For example, again with reference to the HCS-NY, system dynamics modeling explains simulated trends in overdose fatality as a function of core feedback loops (structures). In turn, purposeful scenario analyses demonstrate how and when a specific (future) goal could be reached over time, given sufficient resources to support access to evidence-based harm reduction and treatment capacities. In this manner, the system dynamics model helps inform how to get ‘from A to B,’ and reveals key trade-offs and/or unintended consequences associated with achieving (and sustaining) such a goal. Thus, systems science methods model a problem (drivers of the status quo, or base case) and its associated solution space (potential goals), which calls for design thinking methods to ideate and prototype how to effectively achieve a goal.

Figure 2 describes how systems science approaches could fit into a design thinking framework that aims to fill gaps in implementation science—namely, how to intervene on determinants of successful implementation and how to sustain and scale evidence-based public health solutions. For example, the systems science method of participatory modeling to engage communities and develop a shared model of a problem can serve design thinking’s first phase of empathizing and defining the problem, as in the HCS-NY State example described earlier. Systems science can also serve the experimentation and rapid prototyping process in design thinking by applying simulation modeling methods to test potential interventions. For example, applications of systems modeling approaches to improve food environments in Baltimore, Maryland included use of agent-based models—which use computer simulation to study complex systems from the ground up by examining how individual elements of a system (agents) behave as a function of individual properties, their environment, and their interactions with each other—to simulate the effects of placing warning labels on sugar-sweetened beverages in different combinations of grocery stores, corner stores, schools, and other settings where the beverages are available. The model estimated the potential effect of the labels on purchase and consumption of sugar-sweetened beverages ( 42 ).

Figure 2 . Leveraging systems science and design thinking to advance implementation science. Source: developed by T. T-K. Huang for this manuscript. In practice, it is an iterative process to bring together systems science, design thinking, and implementation science. As iterations occur both within each approach as well as collectively, the learnings immediately feed back into other parts of the process.

Figure 2 also shows business modeling as an integral input to the process of sustaining and scaling solutions. This is because to move toward market feasibility, a viable and scalable model of revenue generation and cost-effective operation identified through the design thinking process must accompany the solution-oriented innovation. Business modeling entails defining the value proposition of a proposed public health solution, key inputs (e.g., partners, resources, and activities), customer base and segments, go-to-market strategies, and a revenue model. Even not-for-profit organizations must generate resources to achieve sustainability beyond traditional grant mechanisms.

5 Discussion

We urge three actions for the field of public health to help promote effective implementation, sustainment, and scaling of evidence-based public health solutions.

First, we must move toward use of a solution-oriented paradigm when approaching public health challenges. The public health community is generally organized around the problem-solving paradigm driven by scientific method, which is marked by a hypothesis-driven, linear, and often top-down approach to both problem solving and intervention design. This paradigm is well-suited for identifying drivers of health or disease but has less utility in determining how to intervene on these determinants, let alone sustain and scale an intervention, in an innately dynamic and complex system.

Second, we need more rigorous research and engagement in cross-disciplinary collaboration to test best practices for incorporating design thinking and systems science approaches into the implementation of community-engaged public health interventions. A recent systematic review concluded that few published peer-reviewed studies exist that use systems thinking and implementation science for designing and delivering population health interventions ( 43 ). Furthermore, we are not aware of any examples of empirical research that integrates all three—systems science, design thinking, and implementation science—in service of optimizing future intervention design and delivery. Documenting future efforts and assessing how these approaches facilitate and enhance intervention implementation can support development and dissemination of tools and training in best practices.

Third, design thinking and systems science curricula should be routinely incorporated into public health education. Design thinking is not a standard subject domain or practice method taught in public health schools, ( 44 ) and systems thinking—although now listed as a competency of graduate programs accredited by the Council on Education for Public Health (CEPH)—is not addressed in adequate depth in most public health programs. Interest is emerging in offering design thinking and entrepreneurship training in public health curriculum to teach future public health professionals how to create and apply business models, among other skills (such as marketing, finance, and business development and operations) that are important to scaling public health solutions ( 45 – 47 ). Such exposure to other disciplines can help set the stage for public health professionals to reach across disciplines and work with diverse collaborators, including those in the private sector.

In conclusion, the magnitude and persistence of current public health challenges demands creativity, innovation, and market feasibility. Most implementation science frameworks are top-down, deterministic, and linear, leaving gaps in understanding of both how to intervene on determinants of successful implementation and how to sustain and scale evidence-based solutions. Design thinking provides a deliberate, replicable process for intervention implementation and scaling that may increase acceptability and effectiveness of public health interventions by actively engaging communities in the design process and rapidly iterating innovation prototypes to maximize success. Systems science provides processes for understanding and managing complexity as well as testing assumptions and intervention ideas. A new paradigm for transformative change in public health is the integration of a design thinking approach with systems science and implementation science to promote more effective implementation, sustainment, and scaling of public health innovation.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

TH: Writing – review & editing, Conceptualization, Funding acquisition, Supervision. EC: Writing – review & editing, Writing – original draft. EH: Writing – review & editing. CH: Writing – review & editing. DS: Writing – review & editing. DL: Writing – review & editing. NS: Writing – review & editing. PH: Writing – review & editing.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported in part by grants from the National Cancer Institute (R01CA206877), National Institute of Minority Health and Health Disparities (R01MD018209), and the Centers for Disease Control and Prevention (U48DP006396). The funders were not involved in writing the manuscript nor in the decision to submit for publication.

Conflict of interest

EC was employed by EAC Health and Nutrition, LLC, served as a science writer and editor for this manuscript, and received consulting fees from the CUNY Graduate School of Public Health and Health Policy for these services. EH and CH each reported receipt of an honorarium for delivering a guest lecture for CUNY on topics related to the content of the manuscript. DS was employed by company d.studio.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: systems science, design thinking, implementation science, human-centered design, complex systems, prevention, intervention sustainability, intervention scale

Citation: Huang TT-K, Callahan EA, Haines ER, Hooley C, Sorensen DM, Lounsbury DW, Sabounchi NS and Hovmand PS (2024) Leveraging systems science and design thinking to advance implementation science: moving toward a solution-oriented paradigm. Front. Public Health . 12:1368050. doi: 10.3389/fpubh.2024.1368050

Received: 07 March 2024; Accepted: 23 April 2024; Published: 15 May 2024.

Reviewed by:

Copyright © 2024 Huang, Callahan, Haines, Hooley, Sorensen, Lounsbury, Sabounchi and Hovmand. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Terry T.-K. Huang, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Systems Thinking in Action: Undergraduate Research Takes On Complex Problems

The projects presented at this year’s IEEE Systems and Information Engineering Design Symposium , hosted by the UVA School of Engineering and Applied Sciences, explored alternative solutions to a wide range of problems: a team of Duke University students worked to improve audible safety alerts for freedivers; a Purdue University research group applied systems thinking to assess the performance of spaceports (the sites for launching or receiving spacecraft); and students from the UVA School of Data Science harnessed artificial intelligence as a method for detecting signs of human trafficking in state-level court cases. 

One UVA team from the Department of Systems and Information Engineering sought to help neurodiverse members of the Charlottesville community gain meaningful employment.

Building Bridges

Through a local business called VIAble Ventures , the VIA Center for Neurodevelopment provides adults with intellectual and developmental disorders with jobs as artisans, making candles, bath salts, and sachets and selling them online and at Charlottesville farmer’s markets. For Sophie Kikuchi, a fourth-year student in the Department of Systems and Information Engineering , the service-minded business was a dream client for a systems engineering project.  In research showcased at the symposium, Kikuchi and her teammates, advised by systems engineering professors Sara Riggs and Robert Riggs , applied systems thinking to help VIAble Ventures boost its sales and employ more adults with autism. Focusing on online sales, the group found ways to highlight top-selling products on the VIAble Ventures website and put the business’s unique mission front and center online.

“The mission of VIAble Ventures is just one that I've really grown to love,” Kikuchi said. “I feel like it's making an impact in the sense that, hopefully, getting the new website up will help increase the sales and employ more people with autism. The program does a lot of on-site job training and soft-skill teaching. It’s a great stepping stone for these individuals to be able to gain the experience they need to earn a paycheck.”

Kikuchi’s team presented one of the 100 papers featured this year at UVA’s design symposium, which has become one of the field’s leading student-focused forums for applied research, development and design over the past 20 years. Joining Kikuchi and her peers from UVA systems and information engineering at the conference were students from 32 higher education institutions in total, nine of which were international.

From Consulting to Co-design  

Another student team from the Department of Systems and Information Engineering looked at ways to streamline operations for outpatient cancer infusion centers, which will need to meet the nation’s growing demand for cancer care.

Rupa Valdez , an associate professor in the Department of Systems and Information Engineering, advised the team as they closely examined the workings of an outpatient cancer infusion center in the Mid-Atlantic region. “It’s exciting to see students learn to integrate their quantitative skills with qualitative approaches to understanding system complexity,” Valdez said. “I also find it meaningful to watch students build trusting relationships with clients throughout the project, enabling us to move from a consulting to a co-design model.”

Kikuchi was able to experience that same shift to co-designing solutions with the client while working with VIAble Ventures. “It’s been really fun working with the people at VIA,” Kikuchi said. “They’ve been so responsive. They like to be very hands-on and give feedback on new website designs. They’re excited about it, too, which has made the experience even better on our end.”

For students, presenting projects at the Systems and Information Engineering Design Symposium each year allows them not only to showcase their own work, but to see the vast array of subjects that systems engineers can explore, Kikuchi said. “It really shows how broadly you can apply the education you get through UVA systems engineering,” she said.

Explore Systems and Information Engineering

Many of the new technologies being introduced today hold the promise of substantial societal benefit if we successfully apply the technologies to solve real-world problems, such as treating and curing diseases and protecting critical systems from cyber attacks.

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Chemistry laboratories play a critical role in scientific discovery and innovation, propelling our understanding of the natural world, informing our daily lives and shaping the course of society.

Within chemistry teaching labs, the next generation of scientists can discover and learn. In research labs, chemists unravel the mysteries of matter, forge new compounds, and pioneer breakthroughs that reverberate across disciplines. From advancing medicine and clean energy solutions to training young scientists, chemistry labs spark novel ideas that galvanize scientific progress and promise. 

Design is pivotal in propelling chemistry labs toward greater efficiency, safety and scientific breakthroughs. Here are four projects that show what’s possible with chemistry lab design today. 

A radical mid-century chemistry lab renovation  

Yale Sterling Chemistry Laboratory Renovation  

Higher education facilities around the U.S. face the challenge of renovating mid-century laboratories in existing buildings — some with historical value and importance to the campus fabric. One such laboratory building was the Sterling Chemistry Lab (SCL) on Yale University’s Science Hill. To improve STEM teaching and learning at Yale, this building underwent a major interior transformation while still preserving the beauty of the historic exterior architecture. 

In a bold approach to sustainability and preservation, our design carved out the building interior, inserted state-of-the-art chemistry and biology labs, and married the new STEM environment with the existing building shell. Science is at the forefront of the design and is on display throughout, showing current and prospective students Yale’s commitment to STEM education. Ultimately, this renovation helps the university enhance STEM teaching principles through collaborative learning spaces and hands-on approaches to science education. 

Enhancing capabilities for an R1 research center  

University of Wisconsin-Milwaukee, Chemistry Building  

Chemistry forms the fundamental basis for numerous growing industries, and the demand for STEM professionals is rapidly outpacing that of non-STEM fields. To meet this demand, the University of Wisconsin-Milwaukee (UWM) is embarking on constructing a cutting-edge new chemistry building that will equip its students with the necessary tools to excel across a broad range of industries. 

One of the few R1 research institutions in the Midwest, UWM seeks to provide state-of-the-art science facilities that fully support learning and research as they evolve. The new chemistry building, replacing a 50-year-old facility that has not been updated since 1972, will shift paradigms for how chemistry is taught, studied and shared, and include space for the thousands of students who take chemistry and biochemistry classes each year. 

A mass timber home for bold climate solutions  

California Institute of Technology, Resnick Sustainability Center  

A giant maker space for scientists, Caltech’s Resnick Sustainability Center will be a dynamic hub for critical research into our most pressing climate and sustainability challenges. Chemistry labs are expertly integrated into the larger building program, including physical sciences, life sciences, and engineering. The building is designed not only to support undergraduate education with state-of-the-art teaching laboratories and learning centers but also to maximize wellness by providing daylight-filled environments where students can participate in group activities or individual study and experience a positive, welcoming space open to imaginative inquiry. 

Aligned with the building’s sustainability mission, a soaring, low-carbon, timber-framed atrium houses the center’s social and collaborative spaces, and the swooping glass curtain wall, which floods this multi-story space with natural light, incorporates a mass timber grid shell. This project is targeting LEED Platinum certification. 

Advancing integration at one of the top laboratories in the world  

Johns Hopkins University Applied Physics Laboratory, Building 201  

The Johns Hopkins Applied Physics Laboratory (APL) Building 201 is a veritable wonderland for scientists, boasting cutting-edge tools, equipment and laboratories that push the boundaries of research and development. Whether unraveling the mysteries of the universe, pioneering new materials, or confronting complex national security challenges, Building 201 equips researchers with everything they need to explore new frontiers. 

Chemistry labs are radically interwoven into other disciplines like materials testing, nanotechnologies, and synthetic chemistry. The building’s overall design promotes scientists’ interaction through openness, transparency and visual connectivity; leverages workplace strategies to integrate lab and office environments; and helps to increase connections to the university and outside industries. 

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Design that accelerates scientific discovery.

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Designing the lab of the future: A look inside the Johns Hopkins Applied Physics Laboratory Building 201

Building 201 is widely lauded as one of the most innovative examples of laboratory design anywhere in the world.

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Scientific Thinking and Critical Thinking in Science Education 

Two Distinct but Symbiotically Related Intellectual Processes

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• Published: 05 September 2023

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• Antonio García-Carmona   ORCID: orcid.org/0000-0001-5952-0340 1  

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Scientific thinking and critical thinking are two intellectual processes that are considered keys in the basic and comprehensive education of citizens. For this reason, their development is also contemplated as among the main objectives of science education. However, in the literature about the two types of thinking in the context of science education, there are quite frequent allusions to one or the other indistinctly to refer to the same cognitive and metacognitive skills, usually leaving unclear what are their differences and what are their common aspects. The present work therefore was aimed at elucidating what the differences and relationships between these two types of thinking are. The conclusion reached was that, while they differ in regard to the purposes of their application and some skills or processes, they also share others and are related symbiotically in a metaphorical sense; i.e., each one makes sense or develops appropriately when it is nourished or enriched by the other. Finally, an orientative proposal is presented for an integrated development of the two types of thinking in science classes.

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Multiple Intelligences Theory—Howard Gardner

Discovery learning—jerome bruner, 21st century skills.

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Education is not the learning of facts, but the training of the mind to think. Albert Einstein

1 Introduction

In consulting technical reports, theoretical frameworks, research, and curricular reforms related to science education, one commonly finds appeals to scientific thinking and critical thinking as essential educational processes or objectives. This is confirmed in some studies that include exhaustive reviews of the literature in this regard such as those of Bailin ( 2002 ), Costa et al. ( 2020 ), and Santos ( 2017 ) on critical thinking, and of Klarh et al. ( 2019 ) and Lehrer and Schauble ( 2006 ) on scientific thinking. However, conceptualizing and differentiating between both types of thinking based on the above-mentioned documents of science education are generally difficult. In many cases, they are referred to without defining them, or they are used interchangeably to represent virtually the same thing. Thus, for example, the document A Framework for K-12 Science Education points out that “Critical thinking is required, whether in developing and refining an idea (an explanation or design) or in conducting an investigation” (National Research Council (NRC), 2012 , p. 46). The same document also refers to scientific thinking when it suggests that basic scientific education should “provide students with opportunities for a range of scientific activities and scientific thinking , including, but not limited to inquiry and investigation, collection and analysis of evidence, logical reasoning, and communication and application of information” (NRC, 2012 , p. 251).

A few years earlier, the report Science Teaching in Schools in Europe: Policies and Research (European Commission/Eurydice, 2006 ) included the dimension “scientific thinking” as part of standardized national science tests in European countries. This dimension consisted of three basic abilities: (i) to solve problems formulated in theoretical terms , (ii) to frame a problem in scientific terms , and (iii) to formulate scientific hypotheses . In contrast, critical thinking was not even mentioned in such a report. However, in subsequent similar reports by the European Commission/Eurydice ( 2011 , 2022 ), there are some references to the fact that the development of critical thinking should be a basic objective of science teaching, although these reports do not define it at any point.

The ENCIENDE report on early-year science education in Spain also includes an explicit allusion to critical thinking among its recommendations: “Providing students with learning tools means helping them to develop critical thinking , to form their own opinions, to distinguish between knowledge founded on the evidence available at a certain moment (evidence which can change) and unfounded beliefs” (Confederation of Scientific Societies in Spain (COSCE), 2011 , p. 62). However, the report makes no explicit mention to scientific thinking. More recently, the document “ Enseñando ciencia con ciencia ” (Teaching science with science) (Couso et al., 2020 ), sponsored by Spain’s Ministry of Education, also addresses critical thinking:

(…) with the teaching approach through guided inquiry students learn scientific content, learn to do science (procedures), learn what science is and how it is built, and this (...) helps to develop critical thinking , that is, to question any statement that is not supported by evidence. (Couso et al., 2020 , p. 54)

On the other hand, in referring to what is practically the same thing, the European report Science Education for Responsible Citizenship speaks of scientific thinking when it establishes that one of the challenges of scientific education should be: “To promote a culture of scientific thinking and inspire citizens to use evidence-based reasoning for decision making” (European Commission, 2015 , p. 14). However, the Pisa 2024 Strategic Vision and Direction for Science report does not mention scientific thinking but does mention critical thinking in noting that “More generally, (students) should be able to recognize the limitations of scientific inquiry and apply critical thinking when engaging with its results” (Organization for Economic Co-operation and Development (OECD), 2020 , p. 9).

The new Spanish science curriculum for basic education (Royal Decree 217/ 2022 ) does make explicit reference to scientific thinking. For example, one of the STEM (Science, Technology, Engineering, and Mathematics) competency descriptors for compulsory secondary education reads:

Use scientific thinking to understand and explain the phenomena that occur around them, trusting in knowledge as a motor for development, asking questions and checking hypotheses through experimentation and inquiry (...) showing a critical attitude about the scope and limitations of science. (p. 41,599)

Furthermore, when developing the curriculum for the subjects of physics and chemistry, the same provision clarifies that “The essence of scientific thinking is to understand what are the reasons for the phenomena that occur in the natural environment to then try to explain them through the appropriate laws of physics and chemistry” (Royal Decree 217/ 2022 , p. 41,659). However, within the science subjects (i.e., Biology and Geology, and Physics and Chemistry), critical thinking is not mentioned as such. Footnote 1 It is only more or less directly alluded to with such expressions as “critical analysis”, “critical assessment”, “critical reflection”, “critical attitude”, and “critical spirit”, with no attempt to conceptualize it as is done with regard to scientific thinking.

The above is just a small sample of the concepts of scientific thinking and critical thinking only being differentiated in some cases, while in others they are presented as interchangeable, using one or the other indistinctly to talk about the same cognitive/metacognitive processes or practices. In fairness, however, it has to be acknowledged—as said at the beginning—that it is far from easy to conceptualize these two types of thinking (Bailin, 2002 ; Dwyer et al., 2014 ; Ennis, 2018 ; Lehrer & Schauble, 2006 ; Kuhn, 1993 , 1999 ) since they feed back on each other, partially overlap, and share certain features (Cáceres et al., 2020 ; Vázquez-Alonso & Manassero-Mas, 2018 ). Neither is there unanimity in the literature on how to characterize each of them, and rarely have they been analyzed comparatively (e.g., Hyytinen et al., 2019 ). For these reasons, I believed it necessary to address this issue with the present work in order to offer some guidelines for science teachers interested in deepening into these two intellectual processes to promote them in their classes.

2 An Attempt to Delimit Scientific Thinking in Science Education

For many years, cognitive science has been interested in studying what scientific thinking is and how it can be taught in order to improve students’ science learning (Klarh et al., 2019 ; Zimmerman & Klarh, 2018 ). To this end, Kuhn et al. propose taking a characterization of science as argument (Kuhn, 1993 ; Kuhn et al., 2008 ). They argue that this is a suitable way of linking the activity of how scientists think with that of the students and of the public in general, since science is a social activity which is subject to ongoing debate, in which the construction of arguments plays a key role. Lehrer and Schauble ( 2006 ) link scientific thinking with scientific literacy, paying especial attention to the different images of science. According to those authors, these images would guide the development of the said literacy in class. The images of science that Leherer and Schauble highlight as characterizing scientific thinking are: (i) science-as-logical reasoning (role of domain-general forms of scientific reasoning, including formal logic, heuristic, and strategies applied in different fields of science), (ii) science-as-theory change (science is subject to permanent revision and change), and (iii) science-as-practice (scientific knowledge and reasoning are components of a larger set of activities that include rules of participation, procedural skills, epistemological knowledge, etc.).

Based on a literature review, Jirout ( 2020 ) defines scientific thinking as an intellectual process whose purpose is the intentional search for information about a phenomenon or facts by formulating questions, checking hypotheses, carrying out observations, recognizing patterns, and making inferences (a detailed description of all these scientific practices or competencies can be found, for example, in NRC, 2012 ; OECD, 2019 ). Therefore, for Jirout, the development of scientific thinking would involve bringing into play the basic science skills/practices common to the inquiry-based approach to learning science (García-Carmona, 2020 ; Harlen, 2014 ). For other authors, scientific thinking would include a whole spectrum of scientific reasoning competencies (Krell et al., 2022 ; Moore, 2019 ; Tytler & Peterson, 2004 ). However, these competences usually cover the same science skills/practices mentioned above. Indeed, a conceptual overlap between scientific thinking, scientific reasoning, and scientific inquiry is often found in science education goals (Krell et al., 2022 ). Although, according to Leherer and Schauble ( 2006 ), scientific thinking is a broader construct that encompasses the other two.

It could be said that scientific thinking is a particular way of searching for information using science practices Footnote 2 (Klarh et al., 2019 ; Zimmerman & Klarh, 2018 ; Vázquez-Alonso & Manassero-Mas, 2018 ). This intellectual process provides the individual with the ability to evaluate the robustness of evidence for or against a certain idea, in order to explain a phenomenon (Clouse, 2017 ). But the development of scientific thinking also requires metacognition processes. According to what Kuhn ( 2022 ) argues, metacognition is fundamental to the permanent control or revision of what an individual thinks and knows, as well as that of the other individuals with whom it interacts, when engaging in scientific practices. In short, scientific thinking demands a good connection between reasoning and metacognition (Kuhn, 2022 ). Footnote 3

From that perspective, Zimmerman and Klarh ( 2018 ) have synthesized a taxonomy categorizing scientific thinking, relating cognitive processes with the corresponding science practices (Table 1 ). It has to be noted that this taxonomy was prepared in line with the categorization of scientific practices proposed in the document A Framework for K-12 Science Education (NRC, 2012 ). This is why one needs to understand that, for example, the cognitive process of elaboration and refinement of hypotheses is not explicitly associated with the scientific practice of hypothesizing but only with the formulation of questions. Indeed, the K-12 Framework document does not establish hypothesis formulation as a basic scientific practice. Lederman et al. ( 2014 ) justify it by arguing that not all scientific research necessarily allows or requires the verification of hypotheses, for example, in cases of exploratory or descriptive research. However, the aforementioned document (NRC, 2012 , p. 50) does refer to hypotheses when describing the practice of developing and using models , appealing to the fact that they facilitate the testing of hypothetical explanations .

In the literature, there are also other interesting taxonomies characterizing scientific thinking for educational purposes. One of them is that of Vázquez-Alonso and Manassero-Mas ( 2018 ) who, instead of science practices, refer to skills associated with scientific thinking . Their characterization basically consists of breaking down into greater detail the content of those science practices that would be related to the different cognitive and metacognitive processes of scientific thinking. Also, unlike Zimmerman and Klarh’s ( 2018 ) proposal, Vázquez-Alonso and Manassero-Mas’s ( 2018 ) proposal explicitly mentions metacognition as one of the aspects of scientific thinking, which they call meta-process . In my opinion, the proposal of the latter authors, which shells out scientific thinking into a broader range of skills/practices, can be more conducive in order to favor its approach in science classes, as teachers would have more options to choose from to address components of this intellectual process depending on their teaching interests, the educational needs of their students and/or the learning objectives pursued. Table 2 presents an adapted characterization of the Vázquez-Alonso and Manassero-Mas’s ( 2018 ) proposal to address scientific thinking in science education.

3 Contextualization of Critical Thinking in Science Education

Theorization and research about critical thinking also has a long tradition in the field of the psychology of learning (Ennis, 2018 ; Kuhn, 1999 ), and its application extends far beyond science education (Dwyer et al., 2014 ). Indeed, the development of critical thinking is commonly accepted as being an essential goal of people’s overall education (Ennis, 2018 ; Hitchcock, 2017 ; Kuhn, 1999 ; Willingham, 2008 ). However, its conceptualization is not simple and there is no unanimous position taken on it in the literature (Costa et al., 2020 ; Dwyer et al., 2014 ); especially when trying to relate it to scientific thinking. Thus, while Tena-Sánchez and León-Medina ( 2022 ) Footnote 4 and McBain et al. ( 2020 ) consider critical thinking to be the basis of or forms part of scientific thinking, Dowd et al. ( 2018 ) understand scientific thinking to be just a subset of critical thinking. However, Vázquez-Alonso and Manassero-Mas ( 2018 ) do not seek to determine whether critical thinking encompasses scientific thinking or vice versa. They consider that both types of knowledge share numerous skills/practices and the progressive development of one fosters the development of the other as a virtuous circle of improvement. Other authors, such as Schafersman ( 1991 ), even go so far as to say that critical thinking and scientific thinking are the same thing. In addition, some views on the relationship between critical thinking and scientific thinking seem to be context-dependent. For example, Hyytine et al. ( 2019 ) point out that in the perspective of scientific thinking as a component of critical thinking, the former is often used to designate evidence-based thinking in the sciences, although this view tends to dominate in Europe but not in the USA context. Perhaps because of this lack of consensus, the two types of thinking are often confused, overlapping, or conceived as interchangeable in education.

Even with such a lack of unanimous or consensus vision, there are some interesting theoretical frameworks and definitions for the development of critical thinking in education. One of the most popular definitions of critical thinking is that proposed by The National Council for Excellence in Critical Thinking (1987, cited in Inter-American Teacher Education Network, 2015 , p. 6). This conceives of it as “the intellectually disciplined process of actively and skillfully conceptualizing, applying, analyzing, synthesizing, and/or evaluating information gathered from, or generated by, observation, experience, reflection, reasoning, or communication, as a guide to belief and action”. In other words, critical thinking can be regarded as a reflective and reasonable class of thinking that provides people with the ability to evaluate multiple statements or positions that are defensible to then decide which is the most defensible (Clouse, 2017 ; Ennis, 2018 ). It thus requires, in addition to a basic scientific competency, notions about epistemology (Kuhn, 1999 ) to understand how knowledge is constructed. Similarly, it requires skills for metacognition (Hyytine et al., 2019 ; Kuhn, 1999 ; Magno, 2010 ) since critical thinking “entails awareness of one’s own thinking and reflection on the thinking of self and others as objects of cognition” (Dean & Kuhn, 2003 , p. 3).

In science education, one of the most suitable scenarios or resources, but not the only one, Footnote 5 to address all these aspects of critical thinking is through the analysis of socioscientific issues (SSI) (Taylor et al., 2006 ; Zeidler & Nichols, 2009 ). Without wishing to expand on this here, I will only say that interesting works can be found in the literature that have analyzed how the discussion of SSIs can favor the development of critical thinking skills (see, e.g., López-Fernández et al., 2022 ; Solbes et al., 2018 ). For example, López-Fernández et al. ( 2022 ) focused their teaching-learning sequence on the following critical thinking skills: information analysis, argumentation, decision making, and communication of decisions. Even some authors add the nature of science (NOS) to this framework (i.e., SSI-NOS-critical thinking), as, for example, Yacoubian and Khishfe ( 2018 ) in order to develop critical thinking and how this can also favor the understanding of NOS (Yacoubian, 2020 ). In effect, as I argued in another work on the COVID-19 pandemic as an SSI, in which special emphasis was placed on critical thinking, an informed understanding of how science works would have helped the public understand why scientists were changing their criteria to face the pandemic in the light of new data and its reinterpretations, or that it was not possible to go faster to get an effective and secure medical treatment for the disease (García-Carmona, 2021b ).

In the recent literature, there have also been some proposals intended to characterize critical thinking in the context of science education. Table 3 presents two of these by way of example. As can be seen, both proposals share various components for the development of critical thinking (respect for evidence, critically analyzing/assessing the validity/reliability of information, adoption of independent opinions/decisions, participation, etc.), but that of Blanco et al. ( 2017 ) is more clearly contextualized in science education. Likewise, that of these authors includes some more aspects (or at least does so more explicitly), such as developing epistemological Footnote 6 knowledge of science (vision of science…) and on its interactions with technology, society, and environment (STSA relationships), and communication skills. Therefore, it offers a wider range of options for choosing critical thinking skills/processes to promote it in science classes. However, neither proposal refers to metacognitive skills, which are also essential for developing critical thinking (Kuhn, 1999 ).

3.1 Critical thinking vs. scientific thinking in science education: differences and similarities

In accordance with the above, it could be said that scientific thinking is nourished by critical thinking, especially when deciding between several possible interpretations and explanations of the same phenomenon since this generally takes place in a context of debate in the scientific community (Acevedo-Díaz & García-Carmona, 2017 ). Thus, the scientific attitude that is perhaps most clearly linked to critical thinking is the skepticism with which scientists tend to welcome new ideas (Normand, 2008 ; Sagan, 1987 ; Tena-Sánchez and León-Medina, 2022 ), especially if they are contrary to well-established scientific knowledge (Bell, 2009 ). A good example of this was the OPERA experiment (García-Carmona & Acevedo-Díaz, 2016a ), which initially seemed to find that neutrinos could move faster than the speed of light. This finding was supposed to invalidate Albert Einstein’s theory of relativity (the finding was later proved wrong). In response, Nobel laureate in physics Sheldon L. Glashow went so far as to state that:

the result obtained by the OPERA collaboration cannot be correct. If it were, we would have to give up so many things, it would be such a huge sacrifice... But if it is, I am officially announcing it: I will shout to Mother Nature: I’m giving up! And I will give up Physics. (BBVA Foundation, 2011 )

Indeed, scientific thinking is ultimately focused on getting evidence that may support an idea or explanation about a phenomenon, and consequently allow others that are less convincing or precise to be discarded. Therefore when, with the evidence available, science has more than one equally defensible position with respect to a problem, the investigation is considered inconclusive (Clouse, 2017 ). In certain cases, this gives rise to scientific controversies (Acevedo-Díaz & García-Carmona, 2017 ) which are not always resolved based exclusively on epistemic or rational factors (Elliott & McKaughan, 2014 ; Vallverdú, 2005 ). Hence, it is also necessary to integrate non-epistemic practices into the framework of scientific thinking (García-Carmona, 2021a ; García-Carmona & Acevedo-Díaz, 2018 ), practices that transcend the purely rational or cognitive processes, including, for example, those related to emotional or affective issues (Sinatra & Hofer, 2021 ). From an educational point of view, this suggests that for students to become more authentically immersed in the way of working or thinking scientifically, they should also learn to feel as scientists do when they carry out their work (Davidson et al., 2020 ). Davidson et al. ( 2020 ) call it epistemic affect , and they suggest that it could be approach in science classes by teaching students to manage their frustrations when they fail to achieve the expected results; Footnote 7 or, for example, to moderate their enthusiasm with favorable results in a scientific inquiry by activating a certain skepticism that encourages them to do more testing. And, as mentioned above, for some authors, having a skeptical attitude is one of the actions that best visualize the application of critical thinking in the framework of scientific thinking (Normand, 2008 ; Sagan, 1987 ; Tena-Sánchez and León-Medina, 2022 ).

On the other hand, critical thinking also draws on many of the skills or practices of scientific thinking, as discussed above. However, in contrast to scientific thinking, the coexistence of two or more defensible ideas is not, in principle, a problem for critical thinking since its purpose is not so much to invalidate some ideas or explanations with respect to others, but rather to provide the individual with the foundations on which to position themself with the idea/argument they find most defensible among several that are possible (Ennis, 2018 ). For example, science with its methods has managed to explain the greenhouse effect, the phenomenon of the tides, or the transmission mechanism of the coronavirus. For this, it had to discard other possible explanations as they were less valid in the investigations carried out. These are therefore issues resolved by the scientific community which create hardly any discussion at the present time. However, taking a position for or against the production of energy in nuclear power plants transcends the scope of scientific thinking since both positions are, in principle, equally defensible. Indeed, within the scientific community itself there are supporters and detractors of the two positions, based on the same scientific knowledge. Consequently, it is critical thinking, which requires the management of knowledge and scientific skills, a basic understanding of epistemic (rational or cognitive) and non-epistemic (social, ethical/moral, economic, psychological, cultural, ...) aspects of the nature of science, as well as metacognitive skills, which helps the individual forge a personal foundation on which to position themself in one place or another, or maintain an uncertain, undecided opinion.

In view of the above, one can summarize that scientific thinking and critical thinking are two different intellectual processes in terms of purpose, but are related symbiotically (i.e., one would make no sense without the other or both feed on each other) and that, in their performance, they share a fair number of features, actions, or mental skills. According to Cáceres et al. ( 2020 ) and Hyytine et al. ( 2019 ), the intellectual skills that are most clearly common to both types of thinking would be searching for relationships between evidence and explanations , as well as investigating and logical thinking to make inferences . To this common space, I would also add skills for metacognition in accordance with what has been discussed about both types of knowledge (Khun, 1999 , 2022 ).

In order to compile in a compact way all that has been argued so far, in Table 4 , I present my overview of the relationship between scientific thinking and critical thinking. I would like to point out that I do not intend to be extremely extensive in the compilation, in the sense that possibly more elements could be added in the different sections, but rather to represent above all the aspects that distinguish and share them, as well as the mutual enrichment (or symbiosis) between them.

4 A Proposal for the Integrated Development of Critical Thinking and Scientific Thinking in Science Classes

Once the differences, common aspects, and relationships between critical thinking and scientific thinking have been discussed, it would be relevant to establish some type of specific proposal to foster them in science classes. Table 5 includes a possible script to address various skills or processes of both types of thinking in an integrated manner. However, before giving guidance on how such skills/processes could be approached, I would like to clarify that while all of them could be dealt within the context of a single school activity, I will not do so in this way. First, because I think that it can give the impression that the proposal is only valid if it is applied all at once in a specific learning situation, which can also discourage science teachers from implementing it in class due to lack of time or training to do so. Second, I think it can be more interesting to conceive the proposal as a set of thinking skills or actions that can be dealt with throughout the different science contents, selecting only (if so decided) some of them, according to educational needs or characteristics of the learning situation posed in each case. Therefore, in the orientations for each point of the script or grouping of these, I will use different examples and/or contexts. Likewise, these orientations in the form of comments, although founded in the literature, should be considered only as possibilities to do so, among many others possible.

Motivation and predisposition to reflect and discuss (point i ) demands, on the one hand, that issues are chosen which are attractive for the students. This can be achieved, for example, by asking the students directly what current issues, related to science and its impact or repercussions, they would like to learn about, and then decide on which issue to focus on (García-Carmona, 2008 ). Or the teacher puts forward the issue directly in class, trying for it be current, to be present in the media, social networks, etc., or what they think may be of interest to their students based on their teaching experience. In this way, each student is encouraged to feel questioned or concerned as a citizen because of the issue that is going to be addressed (García-Carmona, 2008 ). Also of possible interest is the analysis of contemporary, as yet unresolved socioscientific affairs (Solbes et al., 2018 ), such as climate change, science and social justice, transgenic foods, homeopathy, and alcohol and drug use in society. But also, everyday questions can be investigated which demand a decision to be made, such as “What car to buy?” (Moreno-Fontiveros et al., 2022 ), or “How can we prevent the arrival of another pandemic?” (Ushola & Puig, 2023 ).

On the other hand, it is essential that the discussion about the chosen issue is planned through an instructional process that generates an environment conducive to reflection and debate, with a view to engaging the students’ participation in it. This can be achieved, for example, by setting up a role-play game (Blanco-López et al., 2017 ), especially if the issue is socioscientific, or by critical and reflective reading of advertisements with scientific content (Campanario et al., 2001 ) or of science-related news in the daily media (García-Carmona, 2014 , 2021a ; Guerrero-Márquez & García-Carmona, 2020 ; Oliveras et al., 2013 ), etc., for subsequent discussion—all this, in a collaborative learning setting and with a clear democratic spirit.

Respect for scientific evidence (point ii ) should be the indispensable condition in any analysis and discussion from the prisms of scientific and of critical thinking (Erduran, 2021 ). Although scientific knowledge may be impregnated with subjectivity during its construction and is revisable in the light of new evidence ( tentativeness of scientific knowledge), when it is accepted by the scientific community it is as objective as possible (García-Carmona & Acevedo-Díaz, 2016b ). Therefore, promoting trust and respect for scientific evidence should be one of the primary educational challenges to combating pseudoscientists and science deniers (Díaz & Cabrera, 2022 ), whose arguments are based on false beliefs and assumptions, anecdotes, and conspiracy theories (Normand, 2008 ). Nevertheless, it is no simple task to achieve the promotion or respect for scientific evidence (Fackler, 2021 ) since science deniers, for example, consider that science is unreliable because it is imperfect (McIntyre, 2021 ). Hence the need to promote a basic understanding of NOS (point iii ) as a fundamental pillar for the development of both scientific thinking and critical thinking. A good way to do this would be through explicit and reflective discussion about controversies from the history of science (Acevedo-Díaz & García-Carmona, 2017 ) or contemporary controversies (García-Carmona, 2021b ; García-Carmona & Acevedo-Díaz, 2016a ).

Also, with respect to point iii of the proposal, it is necessary to manage basic scientific knowledge in the development of scientific and critical thinking skills (Willingham, 2008 ). Without this, it will be impossible to develop a minimally serious and convincing argument on the issue being analyzed. For example, if one does not know the transmission mechanism of a certain disease, it is likely to be very difficult to understand or justify certain patterns of social behavior when faced with it. In general, possessing appropriate scientific knowledge on the issue in question helps to make the best interpretation of the data and evidence available on this issue (OECD, 2019 ).

The search for information from reliable sources, together with its analysis and interpretation (points iv to vi ), are essential practices both in purely scientific contexts (e.g., learning about the behavior of a given physical phenomenon from literature or through enquiry) and in the application of critical thinking (e.g., when one wishes to take a personal, but informed, position on a particular socio-scientific issue). With regard to determining the credibility of information with scientific content on the Internet, Osborne et al. ( 2022 ) propose, among other strategies, to check whether the source is free of conflicts of interest, i.e., whether or not it is biased by ideological, political or economic motives. Also, it should be checked whether the source and the author(s) of the information are sufficiently reputable.

Regarding the interpretation of data and evidence, several studies have shown the difficulties that students often have with this practice in the context of enquiry activities (e.g., Gobert et al., 2018 ; Kanari & Millar, 2004 ; Pols et al., 2021 ), or when analyzing science news in the press (Norris et al., 2003 ). It is also found that they have significant difficulties in choosing the most appropriate data to support their arguments in causal analyses (Kuhn & Modrek, 2022 ). However, it must be recognized that making interpretations or inferences from data is not a simple task; among other reasons, because their construction is influenced by multiple factors, both epistemic (prior knowledge, experimental designs, etc.) and non-epistemic (personal expectations, ideology, sociopolitical context, etc.), which means that such interpretations are not always the same for all scientists (García-Carmona, 2021a ; García-Carmona & Acevedo-Díaz, 2018 ). For this reason, the performance of this scientific practice constitutes one of the phases or processes that generate the most debate or discussion in a scientific community, as long as no consensus is reached. In order to improve the practice of making inferences among students, Kuhn and Lerman ( 2021 ) propose activities that help them develop their own epistemological norms to connect causally their statements with the available evidence.

Point vii refers, on the one hand, to an essential scientific practice: the elaboration of evidence-based scientific explanations which generally, in a reasoned way, account for the causality, properties, and/or behavior of the phenomena (Brigandt, 2016 ). In addition, point vii concerns the practice of argumentation . Unlike scientific explanations, argumentation tries to justify an idea, explanation, or position with the clear purpose of persuading those who defend other different ones (Osborne & Patterson, 2011 ). As noted above, the complexity of most socioscientific issues implies that they have no unique valid solution or response. Therefore, the content of the arguments used to defend one position or another are not always based solely on purely rational factors such as data and scientific evidence. Some authors defend the need to also deal with non-epistemic aspects of the nature of science when teaching it (García-Carmona, 2021a ; García-Carmona & Acevedo-Díaz, 2018 ) since many scientific and socioscientific controversies are resolved by different factors or go beyond just the epistemic (Vallverdú, 2005 ).

To defend an idea or position taken on an issue, it is not enough to have scientific evidence that supports it. It is also essential to have skills for the communication and discussion of ideas (point viii ). The history of science shows how the difficulties some scientists had in communicating their ideas scientifically led to those ideas not being accepted at the time. A good example for students to become aware of this is the historical case of Semmelweis and puerperal fever (Aragón-Méndez et al., 2019 ). Its reflective reading makes it possible to conclude that the proposal of this doctor that gynecologists disinfect their hands, when passing from one parturient to another to avoid contagions that provoked the fever, was rejected by the medical community not only for epistemic reasons, but also for the difficulties that he had to communicate his idea. The history of science also reveals that some scientific interpretations were imposed on others at certain historical moments due to the rhetorical skills of their proponents although none of the explanations would convincingly explain the phenomenon studied. An example is the case of the controversy between Pasteur and Liebig about the phenomenon of fermentation (García-Carmona & Acevedo-Díaz, 2017 ), whose reading and discussion in science class would also be recommended in this context of this critical and scientific thinking skill. With the COVID-19 pandemic, for example, the arguments of some charlatans in the media and on social networks managed to gain a certain influence in the population, even though scientifically they were muddled nonsense (García-Carmona, 2021b ). Therefore, the reflective reading of news on current SSIs such as this also constitutes a good resource for the same educational purpose. In general, according to Spektor-Levy et al. ( 2009 ), scientific communication skills should be addressed explicitly in class, in a progressive and continuous manner, including tasks of information seeking, reading, scientific writing, representation of information, and representation of the knowledge acquired.

Finally (point ix ), a good scientific/critical thinker must be aware of what they know, of what they have doubts about or do not know, to this end continuously practicing metacognitive exercises (Dean & Kuhn, 2003 ; Hyytine et al., 2019 ; Magno, 2010 ; Willingham, 2008 ). At the same time, they must recognize the weaknesses and strengths of the arguments of their peers in the debate in order to be self-critical if necessary, as well as to revising their own ideas and arguments to improve and reorient them, etc. ( self-regulation ). I see one of the keys of both scientific and critical thinking being the capacity or willingness to change one’s mind, without it being frowned upon. Indeed, quite the opposite since one assumes it to occur thanks to the arguments being enriched and more solidly founded. In other words, scientific and critical thinking and arrogance or haughtiness towards the rectification of ideas or opinions do not stick well together.

5 Final Remarks

For decades, scientific thinking and critical thinking have received particular attention from different disciplines such as psychology, philosophy, pedagogy, and specific areas of this last such as science education. The two types of knowledge represent intellectual processes whose development in students, and in society in general, is considered indispensable for the exercise of responsible citizenship in accord with the demands of today’s society (European Commission, 2006 , 2015 ; NRC, 2012 ; OECD, 2020 ). As has been shown however, the task of their conceptualization is complex, and teaching students to think scientifically and critically is a difficult educational challenge (Willingham, 2008 ).

Aware of this, and after many years dedicated to science education, I felt the need to organize my ideas regarding the aforementioned two types of thinking. In consulting the literature about these, I found that, in many publications, scientific thinking and critical thinking are presented or perceived as being interchangeable or indistinguishable; a conclusion also shared by Hyytine et al. ( 2019 ). Rarely have their differences, relationships, or common features been explicitly studied. So, I considered that it was a matter needing to be addressed because, in science education, the development of scientific thinking is an inherent objective, but, when critical thinking is added to the learning objectives, there arise more than reasonable doubts about when one or the other would be used, or both at the same time. The present work came about motivated by this, with the intention of making a particular contribution, but based on the relevant literature, to advance in the question raised. This converges in conceiving scientific thinking and critical thinking as two intellectual processes that overlap and feed into each other in many aspects but are different with respect to certain cognitive skills and in terms of their purpose. Thus, in the case of scientific thinking, the aim is to choose the best possible explanation of a phenomenon based on the available evidence, and it therefore involves the rejection of alternative explanatory proposals that are shown to be less coherent or convincing. Whereas, from the perspective of critical thinking, the purpose is to choose the most defensible idea/option among others that are also defensible, using both scientific and extra-scientific (i.e., moral, ethical, political, etc.) arguments. With this in mind, I have described a proposal to guide their development in the classroom, integrating them under a conception that I have called, metaphorically, a symbiotic relationship between two modes of thinking.

Critical thinking is mentioned literally in other of the curricular provisions’ subjects such as in Education in Civics and Ethical Values or in Geography and History (Royal Decree 217/2022).

García-Carmona ( 2021a ) conceives of them as activities that require the comprehensive application of procedural skills, cognitive and metacognitive processes, and both scientific knowledge and knowledge of the nature of scientific practice .

Kuhn ( 2021 ) argues that the relationship between scientific reasoning and metacognition is especially fostered by what she calls inhibitory control , which basically consists of breaking down the whole of a thought into parts in such a way that attention is inhibited on some of those parts to allow a focused examination of the intended mental content.

Specifically, Tena-Sánchez and León-Medina (2020) assume that critical thinking is at the basis of rational or scientific skepticism that leads to questioning any claim that does not have empirical support.

As discussed in the introduction, the inquiry-based approach is also considered conducive to addressing critical thinking in science education (Couso et al., 2020 ; NRC, 2012 ).

Epistemic skills should not be confused with epistemological knowledge (García-Carmona, 2021a ). The former refers to skills to construct, evaluate, and use knowledge, and the latter to understanding about the origin, nature, scope, and limits of scientific knowledge.

For this purpose, it can be very useful to address in class, with the help of the history and philosophy of science, that scientists get more wrong than right in their research, and that error is always an opportunity to learn (García-Carmona & Acevedo-Díaz, 2018 ).

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García-Carmona, A. Scientific Thinking and Critical Thinking in Science Education . Sci & Educ (2023). https://doi.org/10.1007/s11191-023-00460-5

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Accepted : 30 July 2023

Published : 05 September 2023

DOI : https://doi.org/10.1007/s11191-023-00460-5

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