critical thinking journal

OPINION article

Redefining critical thinking: teaching students to think like scientists.

• Department of Psychology, MacEwan University, Edmonton, AB, Canada

From primary to post-secondary school, critical thinking (CT) is an oft cited focus or key competency (e.g., DeAngelo et al., 2009 ; California Department of Education, 2014 ; Alberta Education, 2015 ; Australian Curriculum Assessment and Reporting Authority, n.d. ). Unfortunately, the definition of CT has become so broad that it can encompass nearly anything and everything (e.g., Hatcher, 2000 ; Johnson and Hamby, 2015 ). From discussion of Foucault, critique and the self ( Foucault, 1984 ) to Lawson's (1999) definition of CT as the ability to evaluate claims using psychological science, the term critical thinking has come to refer to an ever-widening range of skills and abilities. We propose that educators need to clearly define CT, and that in addition to teaching CT, a strong focus should be placed on teaching students how to think like scientists. Scientific thinking is the ability to generate, test, and evaluate claims, data, and theories (e.g., Bullock et al., 2009 ; Koerber et al., 2015 ). Simply stated, the basic tenets of scientific thinking provide students with the tools to distinguish good information from bad. Students have access to nearly limitless information, and the skills to understand what is misinformation or a questionable scientific claim is crucially important ( Smith, 2011 ), and these skills may not necessarily be included in the general teaching of critical thinking ( Wright, 2001 ).

This is an issue of more than semantics. While some definitions of CT include key elements of the scientific method (e.g., Lawson, 1999 ; Lawson et al., 2015 ), this emphasis is not consistent across all interpretations of CT ( Huber and Kuncel, 2016 ). In an attempt to provide a comprehensive, detailed definition of CT, the American Philosophical Association (APA), outlined six CT skills, 16 subskills, and 19 dispositions ( Facione, 1990 ). Skills include interpretation, analysis, and inference; dispositions include inquisitiveness and open-mindedness. 1 From our perspective, definitions of CT such as those provided by the APA or operationally defined by researchers in the context of a scholarly article (e.g., Forawi, 2016 ) are not problematic—the authors clearly define what they are referring to as CT. Potential problems arise when educators are using different definitions of CT, or when the banner of CT is applied to nearly any topic or pedagogical activity. Definitions such as those provided by the APA provide a comprehensive framework for understanding the multi-faceted nature of CT, however the definition is complex and may be difficult to work with at a policy level for educators, especially those who work primarily with younger students.

The need to develop scientific thinking skills is evident in studies showing that 55% of undergraduate students believe that a full moon causes people to behave oddly, and an estimated 67% of students believe creatures such as Bigfoot and Chupacabra exist, despite the lack of scientific evidence supporting these claims ( Lobato et al., 2014 ). Additionally, despite overwhelming evidence supporting the existence of anthropogenic climate change, and the dire need to mitigate its effects, many people still remain skeptical of climate change and its impact ( Feygina et al., 2010 ; Lewandowsky et al., 2013 ). One of the goals of education is to help students foster the skills necessary to be informed consumers of information ( DeAngelo et al., 2009 ), and providing students with the tools to think scientifically is a crucial component of reaching this goal. By focusing on scientific thinking in conjunction with CT, educators may be better able design specific policies that aim to facilitate the necessary skills students should have when they enter post-secondary training or the workforce. In other words, students should leave secondary school with the ability to rule out rival hypotheses, understand that correlation does not equal causation, the importance of falsifiability and replicability, the ability to recognize extraordinary claims, and use the principle of parsimony (e.g., Lett, 1990 ; Bartz, 2002 ).

Teaching scientific thinking is challenging, as people are vulnerable to trusting their intuitions and subjective observations and tend to prioritize them over objective scientific findings (e.g., Lilienfeld et al., 2012 ). Students and the public at large are prone to naïve realism, or the tendency to believe that our experiences and observations constitute objective reality ( Ross and Ward, 1996 ), when in fact our experiences and observations are subjective and prone to error (e.g., Kahneman, 2011 ). Educators at the post-secondary level tend to prioritize scientific thinking ( Lilienfeld, 2010 ), however many students do not continue on to a post-secondary program after they have completed high school. Further, students who are told they are learning critical thinking may believe they possess the skills to accurately assess the world around them. However, if they are not taught the specific skills needed to be scientifically literate, they may still fall prey to logical fallacies and biases. People tend to underestimate or not understand fallacies that can prevent them from making sound decisions ( Lilienfeld et al., 2001 ; Pronin et al., 2004 ; Lilienfeld, 2010 ). Thus, it is reasonable to think that a person who has not been adequately trained in scientific thinking would nonetheless consider themselves a strong critical thinker, and therefore would be even less likely consider his or her own personal biases. Another concern is that when teaching scientific thinking there is always the risk that students become overly critical or cynical (e.g., Mercier et al., 2017 ). By this, a student may be skeptical of nearly all findings, regardless of the supporting evidence. By incorporating and focusing on cognitive biases, instructors can help students understand their own biases, and demonstrate how the rigor of the scientific method can, at least partially, control for these biases.

Teaching CT remains controversial and confusing for many instructors ( Bensley and Murtagh, 2012 ). This is partly due to the lack of clarity in the definition of CT and the wide range of methods proposed to best teach CT ( Abrami et al., 2008 ; Bensley and Murtagh, 2012 ). For instance, Bensley and Spero (2014) found evidence for the effectiveness of direct approaches to teaching CT, a claim echoed in earlier research ( Abrami et al., 2008 ; Marin and Halpern, 2011 ). Despite their positive findings, some studies have failed to find support for measures of CT ( Burke et al., 2014 ) and others have found variable, yet positive, support for instructional methods ( Dochy et al., 2003 ). Unfortunately, there is a lack of research demonstrating the best pedagogical approaches to teaching scientific thinking at different grade levels. More research is needed to provide an empirically grounded approach to teach scientific thinking, and there is also a need to develop evidence based measures of scientific thinking that are grade and age appropriate. One approach to teaching scientific thinking may be to frame the topic in its simplest terms—the ability to “detect baloney” ( Sagan, 1995 ).

Sagan (1995) has promoted the tools necessary to recognize poor arguments, fallacies to avoid, and how to approach claims using the scientific method. The basic tenets of Sagan's argument apply to most claims, and have the potential to be an effective teaching tool across a range of abilities and ages. Sagan discusses the idea of a baloney detection kit, which contains the “tools” for skeptical thinking. The development of “baloney detection kits” which include age-appropriate scientific thinking skills may be an effective approach to teaching scientific thinking. These kits could include the style of exercises that are typically found under the banner of CT training (e.g., group discussions, evaluations of arguments) with a focus on teaching scientific thinking. An empirically validated kit does not yet exist, though there is much to draw from in the literature on pedagogical approaches to correcting cognitive biases, combatting pseudoscience, and teaching methodology (e.g., Smith, 2011 ). Further research is needed in this area to ensure that the correct, and age-appropriate, tools are part of any baloney detection kit.

Teaching Sagan's idea of baloney detection in conjunction with CT provides educators with a clear focus—to employ a pedagogical approach that helps students create sound and cogent arguments while avoiding falling prey to “baloney”. This is not to say that all of the information taught under the current banner of “critical thinking” is without value. In fact, many of the topics taught under the current approach of CT are important, even though they would not fit within the framework of some definitions of critical thinking. If educators want to ensure that students have the ability to be accurate consumers of information, a focus should be placed on including scientific thinking as a component of the science curriculum, as well as part of the broader teaching of CT.

Educators need to be provided with evidence-based approaches to teach the principles of scientific thinking. These principles should be taught in conjunction with evidence-based methods that mitigate the potential for fallacious reasoning and false beliefs. At a minimum, when students first learn about science, there should also be an introduction to the basics tenets of scientific thinking. Courses dedicated to promoting scientific thinking may also be effective. A course focused on cognitive biases, logical fallacies, and the hallmarks of scientific thinking adapted for each grade level may provide students with the foundation of solid scientific thinking skills to produce and evaluate arguments, and allow expansion of scientific thinking into other scholastic areas and classes. Evaluations of the efficacy of these courses would be essential, along with research to determine the best approach to incorporate scientific thinking into the curriculum.

If instructors know that students have at least some familiarity with the fundamental tenets of scientific thinking, the ability to expand and build upon these ideas in a variety of subject specific areas would further foster and promote these skills. For example, when discussing climate change, an instructor could add a brief discussion of why some people reject the science of climate change by relating this back to the information students will be familiar with from their scientific thinking courses. In terms of an issue like climate change, many students may have heard in political debates or popular culture that global warming trends are not real, or a “hoax” ( Lewandowsky et al., 2013 ). In this case, only teaching the data and facts may not be sufficient to change a student's mind about the reality of climate change ( Lewandowsky et al., 2012 ). Instructors would have more success by presenting students with the data on global warming trends as well as information on the biases that could lead some people reject the data ( Kowalski and Taylor, 2009 ; Lewandowsky et al., 2012 ). This type of instruction helps educators create informed citizens who are better able to guide future decision making and ensure that students enter the job market with the skills needed to be valuable members of the workforce and society as a whole.

By promoting scientific thinking, educators can ensure that students are at least exposed to the basic tenets of what makes a good argument, how to create their own arguments, recognize their own biases and those of others, and how to think like a scientist. There is still work to be done, as there is a need to put in place educational programs built on empirical evidence, as well as research investigating specific techniques to promote scientific thinking for children in earlier grade levels and develop measures to test if students have acquired the necessary scientific thinking skills. By using an evidence based approach to implement strategies to promote scientific thinking, and encouraging researchers to further explore the ideal methods for doing so, educators can better serve their students. When students are provided with the core ideas of how to detect baloney, and provided with examples of how baloney detection relates to the real world (e.g., Schmaltz and Lilienfeld, 2014 ), we are confident that they will be better able to navigate through the oceans of information available and choose the right path when deciding if information is valid.

Author Contribution

RS was the lead author and this paper, and both EJ and NW contributed equally.

Conflict of Interest Statement

The 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.

1. ^ There is some debate about the role of dispositional factors in the ability for a person to engage in critical thinking, specifically that dispositional factors may mitigate any attempt to learn CT. The general consensus is that while dispositional traits may play a role in the ability to think critically, the general skills to be a critical thinker can be taught ( Niu et al., 2013 ; Abrami et al., 2015 ).

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Keywords: scientific thinking, critical thinking, teaching resources, skepticism, education policy

Citation: Schmaltz RM, Jansen E and Wenckowski N (2017) Redefining Critical Thinking: Teaching Students to Think like Scientists. Front. Psychol . 8:459. doi: 10.3389/fpsyg.2017.00459

Received: 13 December 2016; Accepted: 13 March 2017; Published: 29 March 2017.

Reviewed by:

Copyright © 2017 Schmaltz, Jansen and Wenckowski. 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) or licensor 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: Rodney M. Schmaltz, [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.

Double Helix: A Journal of Critical Thinking and Writing

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Promoting critical thinking through an evidence-based skills fair intervention

Journal of Research in Innovative Teaching & Learning

ISSN : 2397-7604

Article publication date: 23 November 2020

Issue publication date: 1 April 2022

The lack of critical thinking in new graduates has been a concern to the nursing profession. The purpose of this study was to investigate the effects of an innovative, evidence-based skills fair intervention on nursing students' achievements and perceptions of critical thinking skills development.

Design/methodology/approach

The explanatory sequential mixed-methods design was employed for this study.

The findings indicated participants perceived the intervention as a strategy for developing critical thinking.

Originality/value

The study provides educators helpful information in planning their own teaching practice in educating students.

Critical thinking

Evidence-based practice, skills fair intervention.

Gonzalez, H.C. , Hsiao, E.-L. , Dees, D.C. , Noviello, S.R. and Gerber, B.L. (2022), "Promoting critical thinking through an evidence-based skills fair intervention", Journal of Research in Innovative Teaching & Learning , Vol. 15 No. 1, pp. 41-54. https://doi.org/10.1108/JRIT-08-2020-0041

Emerald Publishing Limited

Copyright © 2020, Heidi C. Gonzalez, E-Ling Hsiao, Dianne C. Dees, Sherri R. Noviello and Brian L. Gerber

Published in Journal of Research in Innovative Teaching & Learning . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

Introduction

Critical thinking (CT) was defined as “cognitive skills of analyzing, applying standards, discriminating, information seeking, logical reasoning, predicting, and transforming knowledge” ( Scheffer and Rubenfeld, 2000 , p. 357). Critical thinking is the basis for all professional decision-making ( Moore, 2007 ). The lack of critical thinking in student nurses and new graduates has been a concern to the nursing profession. It would negatively affect the quality of service and directly relate to the high error rates in novice nurses that influence patient safety ( Arli et al. , 2017 ; Saintsing et al. , 2011 ). It was reported that as many as 88% of novice nurses commit medication errors with 30% of these errors due to a lack of critical thinking ( Ebright et al. , 2004 ). Failure to rescue is another type of error common for novice nurses, reported as high as 37% ( Saintsing et al. , 2011 ). The failure to recognize trends or complications promptly or take action to stabilize the patient occurs when health-care providers do not recognize signs and symptoms of the early warnings of distress ( Garvey and CNE series, 2015 ). Internationally, this lack of preparedness and critical thinking attributes to the reported 35–60% attrition rate of new graduate nurses in their first two years of practice ( Goodare, 2015 ). The high attrition rate of new nurses has expensive professional and economic costs of $82,000 or more per nurse and negatively affects patient care ( Twibell et al. , 2012 ). Facione and Facione (2013) reported the failure to utilize critical thinking skills not only interferes with learning but also results in poor decision-making and unclear communication between health-care professionals, which ultimately leads to patient deaths.

Due to the importance of critical thinking, many nursing programs strive to infuse critical thinking into their curriculum to better prepare graduates for the realities of clinical practice that involves ever-changing, complex clinical situations and bridge the gap between education and practice in nursing ( Benner et al. , 2010 ; Kim et al. , 2019 ; Park et al. , 2016 ; Newton and Moore, 2013 ; Nibert, 2011 ). To help develop students' critical thinking skills, nurse educators must change the way they teach nursing, so they can prepare future nurses to be effective communicators, critical thinkers and creative problem solvers ( Rieger et al. , 2015 ). Nursing leaders also need to redefine teaching practice and educational guidelines that drive innovation in undergraduate nursing programs.

Evidence-based practice has been advocated to promote critical thinking and help reduce the research-practice gap ( Profetto-McGrath, 2005 ; Stanley and Dougherty, 2010 ). Evidence-based practice was defined as “the conscientious, explicit, and judicious use of current best evidence in making decisions about the care of the individual patient” ( Sackett et al. , 1996 , p. 71). Skills fair intervention, one type of evidence-based practice, can be used to engage students, promote active learning and develop critical thinking ( McCausland and Meyers, 2013 ; Roberts et al. , 2009 ). Skills fair intervention helps promote a consistent teaching practice of the psychomotor skills to the novice nurse that decreased anxiety, gave clarity of expectations to the students in the clinical setting and increased students' critical thinking skills ( Roberts et al. , 2009 ). The researchers of this study had an opportunity to create an active, innovative skills fair intervention for a baccalaureate nursing program in one southeastern state. This intervention incorporated evidence-based practice rationale with critical thinking prompts using Socratic questioning, evidence-based practice videos to the psychomotor skill rubrics, group work, guided discussions, expert demonstration followed by guided practice and blended learning in an attempt to promote and develop critical thinking in nursing students ( Hsu and Hsieh, 2013 ; Oermann et al. , 2011 ; Roberts et al. , 2009 ). The effects of an innovative skills fair intervention on senior baccalaureate nursing students' achievements and their perceptions of critical thinking development were examined in the study.

Literature review

The ability to use reasoned opinion focusing equally on processes and outcomes over emotions is called critical thinking ( Paul and Elder, 2008 ). Critical thinking skills are desired in almost every discipline and play a major role in decision-making and daily judgments. The roots of critical thinking date back to Socrates 2,500 years ago and can be traced to the ancient philosopher Aristotle ( Paul and Elder, 2012 ). Socrates challenged others by asking inquisitive questions in an attempt to challenge their knowledge. In the 1980s, critical thinking gained nationwide recognition as a behavioral science concept in the educational system ( Robert and Petersen, 2013 ). Many researchers in both education and nursing have attempted to define, measure and teach critical thinking for decades. However, a theoretical definition has yet to be accepted and established by the nursing profession ( Romeo, 2010 ). The terms critical literacy, CT, reflective thinking, systems thinking, clinical judgment and clinical reasoning are used synonymously in the reviewed literature ( Clarke and Whitney, 2009 ; Dykstra, 2008 ; Jones, 2010 ; Swing, 2014 ; Turner, 2005 ).

Watson and Glaser (1980) viewed critical thinking not only as cognitive skills but also as a combination of skills, knowledge and attitudes. Paul (1993) , the founder of the Foundation for Critical Thinking, offered several definitions of critical thinking and identified three essential components of critical thinking: elements of thought, intellectual standards and affective traits. Brunt (2005) stated critical thinking is a process of being practical and considered it to be “the process of purposeful thinking and reflective reasoning where practitioners examine ideas, assumptions, principles, conclusions, beliefs, and actions in the contexts of nursing practice” (p. 61). In an updated definition, Ennis (2011) described critical thinking as, “reasonable reflective thinking focused on deciding what to believe or do” (para. 1).

The most comprehensive attempt to define critical thinking was under the direction of Facione and sponsored by the American Philosophical Association ( Scheffer and Rubenfeld, 2000 ). Facione (1990) surveyed 53 experts from the arts and sciences using the Delphi method to define critical thinking as a “purposeful, self-regulatory judgment which results in interpretation, analysis, evaluation, and inference, as well as an explanation of the evidential, conceptual, methodological, criteriological, or contextual considerations upon which judgment, is based” (p. 2).

To come to a consensus definition for critical thinking, Scheffer and Rubenfeld (2000) also conducted a Delphi study. Their study consisted of an international panel of nurses who completed five rounds of sequenced questions to arrive at a consensus definition. Critical thinking was defined as “habits of mind” and “cognitive skills.” The elements of habits of mind included “confidence, contextual perspective, creativity, flexibility, inquisitiveness, intellectual integrity, intuition, open-mindedness, perseverance, and reflection” ( Scheffer and Rubenfeld, 2000 , p. 352). The elements of cognitive skills were recognized as “analyzing, applying standards, discriminating, information seeking, logical reasoning, predicting, and transforming knowledge” ( Scheffer and Rubenfeld, 2000 , p. 352). In addition, Ignatavicius (2001) defined the development of critical thinking as a long-term process that must be practiced, nurtured and reinforced over time. Ignatavicius believed that a critical thinker required six cognitive skills: interpretation, analysis, evaluation, inference, explanation and self-regulation ( Chun-Chih et al. , 2015 ). According to Ignatavicius (2001) , the development of critical thinking is difficult to measure or describe because it is a formative rather than summative process.

Fero et al. (2009) noted that patient safety might be compromised if a nurse cannot provide clinically competent care due to a lack of critical thinking. The Institute of Medicine (2001) recommended five health care competencies: patient-centered care, interdisciplinary team care, evidence-based practice, informatics and quality improvement. Understanding the development and attainment of critical thinking is the key for gaining these future competencies ( Scheffer and Rubenfeld, 2000 ). The development of a strong scientific foundation for nursing practice depends on habits such as contextual perspective, inquisitiveness, creativity, analysis and reasoning skills. Therefore, the need to better understand how these critical thinking habits are developed in nursing students needs to be explored through additional research ( Fero et al. , 2009 ). Despite critical thinking being listed since the 1980s as an accreditation outcome criteria for baccalaureate programs by the National League for Nursing, very little improvement has been observed in practice ( McMullen and McMullen, 2009 ). James (2013) reported the number of patient harm incidents associated with hospital care is much higher than previously thought. James' study indicated that between 210,000 and 440,000 patients each year go to the hospital for care and end up suffering some preventable harm that contributes to their death. James' study of preventable errors is attributed to other sources besides nursing care, but having a nurse in place who can advocate and critically think for patients will make a positive impact on improving patient safety ( James, 2013 ; Robert and Peterson, 2013 ).

Adopting teaching practice to promote CT is a crucial component of nursing education. Research by Nadelson and Nadelson (2014) suggested evidence-based practice is best learned when integrated into multiple areas of the curriculum. Evidence-based practice developed its roots through evidence-based medicine, and the philosophical origins extend back to the mid-19th century ( Longton, 2014 ). Florence Nightingale, the pioneer of modern nursing, used evidence-based practice during the Crimean War when she recognized a connection between poor sanitary conditions and rising mortality rates of wounded soldiers ( Rahman and Applebaum, 2011 ). In professional nursing practice today, a commonly used definition of evidence-based practice is derived from Dr. David Sackett: the conscientious, explicit and judicious use of current best evidence in making decisions about the care of the individual patient ( Sackett et al. , 1996 , p. 71). As professional nurses, it is imperative for patient safety to remain inquisitive and ask if the care provided is based on available evidence. One of the core beliefs of the American Nephrology Nurses' Association's (2019) 2019–2020 Strategic Plan is “Anna must support research to develop evidence-based practice, as well as to advance nursing science, and that as individual members, we must support, participate in, and apply evidence-based research that advances our own skills, as well as nursing science” (p. 1). Longton (2014) reported the lack of evidence-based practice in nursing resulted in negative outcomes for patients. In fact, when evidence-based practice was implemented, changes in policies and procedures occurred that resulted in decreased reports of patient harm and associated health-care costs. The Institute of Medicine (2011) recommendations included nurses being leaders in the transformation of the health-care system and achieving higher levels of education that will provide the ability to critically analyze data to improve the quality of care for patients. Student nurses must be taught to connect and integrate CT and evidence-based practice throughout their program of study and continue that practice throughout their careers.

One type of evidence-based practice that can be used to engage students, promote active learning and develop critical thinking is skills fair intervention ( McCausland and Meyers, 2013 ; Roberts et al. , 2009 ). Skills fair intervention promoted a consistent teaching approach of the psychomotor skills to the novice nurse that decreased anxiety, gave clarity of expectations to the students in the clinical setting and increased students' critical thinking skills ( Roberts et al. , 2009 ). The skills fair intervention used in this study is a teaching strategy that incorporated CT prompts, Socratic questioning, group work, guided discussions, return demonstrations and blended learning in an attempt to develop CT in nursing students ( Hsu and Hsieh, 2013 ; Roberts et al. , 2009 ). It melded evidence-based practice with simulated CT opportunities while students practiced essential psychomotor skills.

Research methodology

Context – skills fair intervention.

According to Roberts et al. (2009) , psychomotor skills decline over time even among licensed experienced professionals within as little as two weeks and may need to be relearned within two months without performing a skill. When applying this concept to student nurses for whom each skill is new, it is no wonder their competency result is diminished after having a summer break from nursing school. This skills fair intervention is a one-day event to assist baccalaureate students who had taken the summer off from their studies in nursing and all faculty participated in operating the stations. It incorporated evidence-based practice rationale with critical thinking prompts using Socratic questioning, evidence-based practice videos to the psychomotor skill rubrics, group work, guided discussions, expert demonstration followed by guided practice and blended learning in an attempt to promote and develop critical thinking in baccalaureate students.

Students were scheduled and placed randomly into eight teams based on attributes of critical thinking as described by Wittmann-Price (2013) : Team A – Perseverance, Team B – Flexibility, Team C – Confidence, Team D – Creativity, Team E – Inquisitiveness, Team F – Reflection, Team G – Analyzing and Team H – Intuition. The students rotated every 20 minutes through eight stations: Medication Administration: Intramuscular and Subcutaneous Injections, Initiating Intravenous Therapy, ten-minute Focused Physical Assessment, Foley Catheter Insertion, Nasogastric Intubation, Skin Assessment/Braden Score and Restraints, Vital Signs and a Safety Station. When the students completed all eight stations, they went to the “Check-Out” booth to complete a simple evaluation to determine their perceptions of the effectiveness of the innovative intervention. When the evaluations were complete, each of the eight critical thinking attribute teams placed their index cards into a hat, and a student won a small prize. All Junior 2, Senior 1 and Senior 2 students were required to attend the Skills Fair. The Skills Fair Team strove to make the event as festive as possible, engaging nursing students with balloons, candy, tri-boards, signs and fun pre and postactivities. The Skills Fair rubrics, scheduling and instructions were shared electronically with students and faculty before the skills fair intervention to ensure adequate preparation and continuous resource availability as students move forward into their future clinical settings.

Research design

Institutional review board (IRB) approval was obtained from XXX University to conduct this study and protect human subject rights. The explanatory sequential mixed-methods design was employed for this study. The design was chosen to identify what effects a skills fair intervention that had on senior baccalaureate nursing students' achievements on the Kaplan Critical Thinking Integrated Test (KCTIT) and then follow up with individual interviews to explore those test results in more depth. In total, 52 senior nursing students completed the KCTIT; 30 of them participated in the skills fair intervention and 22 of them did not participate. The KCTIT is a computerized 85-item exam in which 85 equates to 100%, making each question worth one point. It has high reliability and validity ( Kaplan Nursing, 2012 ; Swing, 2014 ). The reliability value of the KCTIT ranged from 0.72 to 0.89. A t -test was used to analyze the test results.

A total of 11 participants were purposefully selected based on a range of six high achievers and five low achievers on the KCTIT for open-ended one-on-one interviews. Each interview was conducted individually and lasted for about 60 minutes. An open-ended interview protocol was used to guide the flow of data collection. The interviewees' ages ranged from 21 to 30 years, with an average of 24 years. One of 11 interviewees was male. Among them, seven were White, three were Black and one was Indian American. The data collected were used to answer the following research questions: (1) What was the difference in achievements on the KCTIT among senior baccalaureate nursing students who participated in the skills fair intervention and students who did not participate? (2) What were the senior baccalaureate nursing students' perceptions of internal and external factors impacting the development of critical thinking skills during the skills fair intervention? and (3) What were the senior baccalaureate nursing students' perceptions of the skills fair intervention as a critical thinking developmental strategy?

Inductive content analysis was used to analyze interview data by starting with the close reading of the transcripts and writing memos for initial coding, followed by an analysis of patterns and relationships among the data for focused coding. The intercoder reliability was established for qualitative data analysis with a nursing expert. The lead researcher and the expert read the transcript several times and assigned a code to significant units of text that corresponded with answering the research questions. The codes were compared based on differences and similarities and sorted into subcategories and categories. Then, headings and subheadings were used based on similar comments to develop central themes and patterns. The process of establishing intercoder reliability helped to increase dependability, conformability and credibility of the findings ( Graneheim and Lundman, 2004 ). In addition, methods of credibility, confirmability, dependability and transferability were applied to increase the trustworthiness of this study ( Graneheim and Lundman, 2004 ). First, reflexivity was observed by keeping journals and memos. This practice allowed the lead researcher to reflect on personal views to minimize bias. Data saturation was reached through following the recommended number of participants as well as repeated immersion in the data during analysis until no new data surfaced. Member checking was accomplished through returning the transcript and the interpretation to the participants to check the accuracy and truthfulness of the findings. Finally, proper documentation was conducted to allow accurate crossreferencing throughout the study.

Quantitative results

Results for the quantitative portion showed there was no difference in scores on the KCTIT between senior nursing students who participated in the skills fair intervention and senior nursing students who did not participate, t (50) = −0.174, p  = 0.86 > 0.05. The test scores between the nonparticipant group ( M  = 67.59, SD = 5.81) and the participant group ( M  = 67.88, SD = 5.99) were almost equal.

Qualitative results

Initial coding.

The results from the initial coding and generated themes are listed in Table 1 . First, the participants perceived the skills fair intervention as “promoting experience” and “confidence” by practicing previously learned knowledge and reinforcing it with active learning strategies. Second, the participants perceived the skills fair intervention as a relaxed, nonthreatening learning environment due to the festive atmosphere, especially in comparison to other learning experiences in the nursing program. The nonthreatening environment of the skills fair intervention allowed students to learn without fear. Third, the majority of participants believed their critical thinking was strengthened after participating. Several participants believed their perception of critical thinking was “enhanced” or “reinforced” rather than significantly changed.

Focused coding results

The final themes were derived from the analysis of patterns and relationships among the content of the data using inductive content analysis ( Saldana, 2009 ). The following was examined across the focused coding process: (1) factors impacting critical thinking skills development during skills fair intervention and (2) skills fair intervention a critical thinking skills developmental strategy.

Factors impacting critical thinking skills development . The factors impacting the development of critical thinking during the skills fair intervention were divided into two themes: internal factors and external factors. The internal factors were characteristics innate to the students. The identified internal factors were (1) confidence and anxiety levels, (2) attitude and (3) age. The external factors were the outside influences that affected the students. The external factors were (1) experience and practice, (2) faculty involvement, (3) positive learning environment and (4) faculty prompts.

I think that confidence and anxiety definitely both have a huge impact on your ability to be able to really critically think. If you start getting anxious and panicking you cannot think through the process like you need too. I do not really think gender or age necessarily would have anything to do with critical thinking.

Definitely the confidence level, I think, the more advanced you get in the program, your confidence just keeps on growing. Level of anxiety, definitely… I think the people who were in the Skills Fair for the first time, had more anxiety because they did not really know to think, they did not know how strict it was going to be, or if they really had to know everything by the book. I think the Skills Fair helped everyone's confidence levels, but especially the Jr. 2's.

Attitude was an important factor in the development of critical thinking skills during the skills fair intervention as participants believed possessing a pleasant and positive attitude meant a student was eager to learn, participate, accept responsibility for completing duties and think seriously. Participant 6 believed attitude contributed to performance in the Skills Fair.

I feel like, certain things bring critical thinking out in you. And since I'm a little bit older than some of the other students, I have had more life experiences and am able to figure stuff out better. Older students have had more time to learn by trial and error, and this and that.

Like when I had clinical with you, you'd always tell us to know our patients' medications. To always know and be prepared to answer questions – because at first as a Junior 1 we did not do that in the clinical setting… and as a Junior 2, I did not really have to know my medications, but with you as a Senior 1, I started to realize that the patients do ask about their meds, so I was making sure that I knew everything before they asked it. And just having more practice with IVs – at first, I was really nervous, but when I got to my preceptorship – I had done so many IVs and with all of the practice, it just built up my confidence with that skill so when I performed that skill during the Fair, I was confident due to my clinical experiences and able to think and perform better.

I think teachers will always affect the ability to critically think just because you want [to] get the right answer because they are there and you want to seem smart to them [Laugh]. Also, if you are leading in the wrong direction of your thinking – they help steer you back to [in] the right direction so I think that was very helpful.

You could tell the faculty really tried to make it more laid back and fun, so everybody would have a good experience. The faculty had a good attitude. I think making it fun and active helped keep people positive. You know if people are negative and not motivated, nothing gets accomplished. The faculty did an amazing job at making the Skills Fair a positive atmosphere.

However, for some of the participants, a positive learning environment depended on their fellow students. The students were randomly assigned alphabetically to groups, and the groups were assigned to starting stations at the Skills Fair. The participants claimed some students did not want to participate and displayed cynicism toward the intervention. The participants believed their cynicism affected the positive learning environment making critical thinking more difficult during the Skills Fair.

Okay, when [instructor name] was demonstrating the Chevron technique right after we inserted the IV catheter and we were trying to secure the catheter, put on the extension set, and flush the line at what seemed to be all at the same time. I forgot about how you do not want to put the tape right over the hub of the catheter because when you go back in and try to assess the IV site – you're trying to assess whether or not it is patent or infiltrated – you have to visualize the insertion site. That was one of the things that I had been doing wrong because I was just so excited that I got the IV in the vein in the first place – that I did not think much about the tape or the tegaderm for sterility. So I think an important part of critical thinking is to be able to recognize when you've made a mistake and stop, stop yourself from doing it in the future (see Table 2 ).

Skills fair intervention as a developmental strategy for critical thinking . The participants identified the skills fair intervention was effective as a developmental strategy for critical thinking, as revealed in two themes: (1) develops alternative thinking and (2) thinking before doing (See Table 3 ).

Develops alternative thinking . The participants perceived the skills fair intervention helped enhance critical thinking and confidence by developing alternative thinking. Alternative thinking was described as quickly thinking of alternative solutions to problems based on the latest evidence and using that information to determine what actions were warranted to prevent complications and prevent injury. It helped make better connections through the learning of rationale between knowledge and skills and then applying that knowledge to prevent complications and errors to ensure the safety of patients. The participants stated the learning of rationale for certain procedures provided during the skills fair intervention such as the evidence and critical thinking prompts included in the rubrics helped reinforce this connection. The participants also shared they developed alternative thinking after participating in the skills fair intervention by noticing trends in data to prevent potential complications from the faculty prompts. Participant 1 stated her instructor prompted her alternative thinking through questioning about noticing trends to prevent potential complications. She said the following:

Another way critical thinking occurred during the skills fair was when [instructor name] was teaching and prompted us about what it would be like to care for a patient with a fractured hip – I think this was at the 10-minute focused assessment station, but I could be wrong. I remember her asking, “What do you need to be on the look-out for? What can go wrong?” I automatically did not think critically very well and was only thinking circulation in the leg, dah, dah, dah. But she was prompting us to think about mobility alterations and its effect on perfusion and oxygenation. She was trying to help us build those connections. And I think that's a lot of the aspects of critical thinking that gets overlooked with the nursing student – trouble making connections between our knowledge and applying it in practice.

Thinking before doing . The participants perceived thinking before doing, included thinking of how and why certain procedures, was necessary through self-examination prior to taking action. The hands-on situational learning allowed the participants in the skills fair intervention to better notice assessment data and think at a higher level as their previous learning of the skills was perceived as memorization of steps. This higher level of learning allowed participants to consider different future outcomes and analyze pertinent data before taking action.

I think what helped me the most is considering outcomes of my actions before I do anything. For instance, if you're thinking, “Okay. Well, I need to check their blood pressure before I administer this blood pressure medication – or the blood pressure could potentially bottom out.” I really do not want my patient to bottom out and get hypotensive because I administered a medication that was ordered, but not safe to give. I could prevent problems from happening if I know what to be on alert for and act accordingly. So ultimately knowing that in the clinical setting, I can prevent complications from happening and I save myself, my license, and promote patient safety. I think knowing that I've seen the importance of critical thinking already in practice has helped me value and understand why I should be critically thinking. Yes, we use the 5-rights of medication safety – but we also have to think. For instance, if I am going to administer insulin – what do I need to know or do to give this safely? What is the current blood sugar? Has the patient been eating? When is the next meal scheduled? Is the patient NPO for a procedure? Those are examples of questions to consider and the level of thinking that needs to take place prior to taking actions in the clinical setting.

Although the results of quantitative data showed no significant difference in scores on the KCTIT between the participant and nonparticipant groups, during the interviews some participants attributed this result to the test not being part of a course grade and believed students “did not try very hard to score well.” However, the participants who attended interviews did identify the skills fair intervention as a developmental strategy for critical thinking by helping them develop alternative thinking and thinking before doing. The findings are supported in the literature as (1) nurses must recognize signs of clinical deterioration and take action promptly to prevent potential complications ( Garvey and CNE series 2015 ) and (2) nurses must analyze pertinent data and consider all possible solutions before deciding on the most appropriate action for each patient ( Papathanasiou et al. , 2014 ).

The skills fair intervention also enhanced the development of self-confidence by participants practicing previously learned skills in a controlled, safe environment. The nonthreatening environment of the skills fair intervention allowed students to learn without fear and the majority of participants believed their critical thinking was strengthened after participating. The interview data also revealed a combination of internal and external factors that influenced the development of critical thinking during the skills fair intervention including confidence and anxiety levels, attitude, age, experience and practice, faculty involvement, positive learning environment and faculty prompts. These factors should be considered when addressing the promotion and development of critical thinking.

Conclusions, limitations and recommendations

A major concern in the nursing profession is the lack of critical thinking in student nurses and new graduates, which influences the decision-making of novice nurses and directly affects patient care and safety ( Saintsing et al. , 2011 ). Nurse educators must use evidence-based practice to prepare students to critically think with the complicated and constantly evolving environment of health care today ( Goodare, 2015 ; Newton and Moore, 2013 ). Evidence-based practice has been advocated to promote critical thinking ( Profetto-McGrath, 2005 ; Stanley and Dougherty, 2010 ). The skills fair intervention can be one type of evidence-based practice used to promote critical thinking ( McCausland and Meyers, 2013 ; Roberts et al. , 2009 ). The Intervention used in this study incorporated evidence-based practice rationale with critical thinking prompts using Socratic questioning, evidence-based practice videos to the psychomotor skill rubrics, group work, guided discussions, expert demonstration followed by guided practice and blended learning in an attempt to promote and develop critical thinking in nursing students.

The explanatory sequential mixed-methods design was employed to investigate the effects of the innovative skills fair intervention on senior baccalaureate nursing students' achievements and their perceptions of critical thinking skills development. Although the quantitative results showed no significant difference in scores on the KCTIT between students who participated in the skills fair intervention and those who did not, those who attended the interviews perceived their critical thinking was reinforced after the skills fair intervention and believed it was an effective developmental strategy for critical thinking, as it developed alternative thinking and thinking before doing. This information is useful for nurse educators who plan their own teaching practice to promote critical thinking and improve patient outcomes. The findings also provide schools and educators information that helps review their current approach in educating nursing students. As evidenced in the findings, the importance of developing critical thinking skills is crucial for becoming a safe, professional nurse. Internal and external factors impacting the development of critical thinking during the skills fair intervention were identified including confidence and anxiety levels, attitude, age, experience and practice, faculty involvement, positive learning environment and faculty prompts. These factors should be considered when addressing the promotion and development of critical thinking.

There were several limitations to this study. One of the major limitations of the study was the limited exposure of students' time of access to the skills fair intervention, as it was a one-day learning intervention. Another limitation was the sample selection and size. The skills fair intervention was limited to only one baccalaureate nursing program in one southeastern state. As such, the findings of the study cannot be generalized as it may not be representative of baccalaureate nursing programs in general. In addition, this study did not consider students' critical thinking achievements prior to the skills fair intervention. Therefore, no baseline measurement of critical thinking was available for a before and after comparison. Other factors in the nursing program could have affected the students' scores on the KCTIT, such as anxiety or motivation that was not taken into account in this study.

The recommendations for future research are to expand the topic by including other regions, larger samples and other baccalaureate nursing programs. In addition, future research should consider other participant perceptions, such as nurse educators, to better understand the development and growth of critical thinking skills among nursing students. Finally, based on participant perceptions, future research should include a more rigorous skills fair intervention to develop critical thinking and explore the link between confidence and critical thinking in nursing students.

Initial coding results

Factors impacting critical thinking skill development during skills fair intervention

Skills fair intervention as a developmental strategy for critical thinking

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A Crash Course in Critical Thinking

What you need to know—and read—about one of the essential skills needed today..

Posted April 8, 2024 | Reviewed by Michelle Quirk

• In research for "A More Beautiful Question," I did a deep dive into the current crisis in critical thinking.
• Many people may think of themselves as critical thinkers, but they actually are not.
• Here is a series of questions you can ask yourself to try to ensure that you are thinking critically.

Conspiracy theories. Inability to distinguish facts from falsehoods. Widespread confusion about who and what to believe.

These are some of the hallmarks of the current crisis in critical thinking—which just might be the issue of our times. Because if people aren’t willing or able to think critically as they choose potential leaders, they’re apt to choose bad ones. And if they can’t judge whether the information they’re receiving is sound, they may follow faulty advice while ignoring recommendations that are science-based and solid (and perhaps life-saving).

Moreover, as a society, if we can’t think critically about the many serious challenges we face, it becomes more difficult to agree on what those challenges are—much less solve them.

On a personal level, critical thinking can enable you to make better everyday decisions. It can help you make sense of an increasingly complex and confusing world.

In the new expanded edition of my book A More Beautiful Question ( AMBQ ), I took a deep dive into critical thinking. Here are a few key things I learned.

First off, before you can get better at critical thinking, you should understand what it is. It’s not just about being a skeptic. When thinking critically, we are thoughtfully reasoning, evaluating, and making decisions based on evidence and logic. And—perhaps most important—while doing this, a critical thinker always strives to be open-minded and fair-minded . That’s not easy: It demands that you constantly question your assumptions and biases and that you always remain open to considering opposing views.

In today’s polarized environment, many people think of themselves as critical thinkers simply because they ask skeptical questions—often directed at, say, certain government policies or ideas espoused by those on the “other side” of the political divide. The problem is, they may not be asking these questions with an open mind or a willingness to fairly consider opposing views.

When people do this, they’re engaging in “weak-sense critical thinking”—a term popularized by the late Richard Paul, a co-founder of The Foundation for Critical Thinking . “Weak-sense critical thinking” means applying the tools and practices of critical thinking—questioning, investigating, evaluating—but with the sole purpose of confirming one’s own bias or serving an agenda.

In AMBQ , I lay out a series of questions you can ask yourself to try to ensure that you’re thinking critically. Here are some of the questions to consider:

• Why do I believe what I believe?
• Are my views based on evidence?
• Have I fairly and thoughtfully considered differing viewpoints?
• Am I truly open to changing my mind?

Of course, becoming a better critical thinker is not as simple as just asking yourself a few questions. Critical thinking is a habit of mind that must be developed and strengthened over time. In effect, you must train yourself to think in a manner that is more effortful, aware, grounded, and balanced.

For those interested in giving themselves a crash course in critical thinking—something I did myself, as I was working on my book—I thought it might be helpful to share a list of some of the books that have shaped my own thinking on this subject. As a self-interested author, I naturally would suggest that you start with the new 10th-anniversary edition of A More Beautiful Question , but beyond that, here are the top eight critical-thinking books I’d recommend.

The Demon-Haunted World: Science as a Candle in the Dark , by Carl Sagan

This book simply must top the list, because the late scientist and author Carl Sagan continues to be such a bright shining light in the critical thinking universe. Chapter 12 includes the details on Sagan’s famous “baloney detection kit,” a collection of lessons and tips on how to deal with bogus arguments and logical fallacies.

Clear Thinking: Turning Ordinary Moments Into Extraordinary Results , by Shane Parrish

The creator of the Farnham Street website and host of the “Knowledge Project” podcast explains how to contend with biases and unconscious reactions so you can make better everyday decisions. It contains insights from many of the brilliant thinkers Shane has studied.

Good Thinking: Why Flawed Logic Puts Us All at Risk and How Critical Thinking Can Save the World , by David Robert Grimes

A brilliant, comprehensive 2021 book on critical thinking that, to my mind, hasn’t received nearly enough attention . The scientist Grimes dissects bad thinking, shows why it persists, and offers the tools to defeat it.

Think Again: The Power of Knowing What You Don't Know , by Adam Grant

Intellectual humility—being willing to admit that you might be wrong—is what this book is primarily about. But Adam, the renowned Wharton psychology professor and bestselling author, takes the reader on a mind-opening journey with colorful stories and characters.

Think Like a Detective: A Kid's Guide to Critical Thinking , by David Pakman

The popular YouTuber and podcast host Pakman—normally known for talking politics —has written a terrific primer on critical thinking for children. The illustrated book presents critical thinking as a “superpower” that enables kids to unlock mysteries and dig for truth. (I also recommend Pakman’s second kids’ book called Think Like a Scientist .)

Rationality: What It Is, Why It Seems Scarce, Why It Matters , by Steven Pinker

The Harvard psychology professor Pinker tackles conspiracy theories head-on but also explores concepts involving risk/reward, probability and randomness, and correlation/causation. And if that strikes you as daunting, be assured that Pinker makes it lively and accessible.

How Minds Change: The Surprising Science of Belief, Opinion and Persuasion , by David McRaney

David is a science writer who hosts the popular podcast “You Are Not So Smart” (and his ideas are featured in A More Beautiful Question ). His well-written book looks at ways you can actually get through to people who see the world very differently than you (hint: bludgeoning them with facts definitely won’t work).

A Healthy Democracy's Best Hope: Building the Critical Thinking Habit , by M Neil Browne and Chelsea Kulhanek

Neil Browne, author of the seminal Asking the Right Questions: A Guide to Critical Thinking, has been a pioneer in presenting critical thinking as a question-based approach to making sense of the world around us. His newest book, co-authored with Chelsea Kulhanek, breaks down critical thinking into “11 explosive questions”—including the “priors question” (which challenges us to question assumptions), the “evidence question” (focusing on how to evaluate and weigh evidence), and the “humility question” (which reminds us that a critical thinker must be humble enough to consider the possibility of being wrong).

Warren Berger is a longtime journalist and author of A More Beautiful Question .

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Teaching scientific evidence and critical thinking for policy making.

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Natalia Pasternak Taschner, Paulo Almeida, Teaching scientific evidence and critical thinking for policy making, Biology Methods and Protocols , 2024;, bpae023, https://doi.org/10.1093/biomethods/bpae023

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While there is worldwide tendency to promote the use of scientific evidence to inform policy making, little has been done to train scientists and policy makers for this interaction. If we want to bridge the gap between academia, scientific knowledge and policy, we must begin by providing formal training and skill building for actors and stakeholders. Scientists are not trained to communciate and inform policy, and policy makers are not trained to understand scientific process and assess evidence. Building an environment where this collaboration can flourish depends on teaching competencies and abilities specific for decison-making processess. As professors of policy with a background in science, we have started teaching preliminary courses on the use of scientific evidence in policy making. Feedback from students and institutions has been positive, paving the way for similar courses in other schools and institutions and maybe even new career paths. This paper is intended to share our experience in designing and teaching courses aimed at training policy makers. Moving forward we plan to include training for science majors, thus encompassing the two main sides of this dialogue and opening new career opportunities for scientists and policy makers.

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The role of critical thinking skills and learning styles of university students in their academic performance

Zohre ghazivakili.

1 Emergency medical services department, Paramedical school, Alborz University of Medical Sciences, Karaj, Iran;

ROOHANGIZ NOROUZI NIA

2 Educational Development Center, Alborz University of Medical Sciences, Karaj, Iran;

FARIDE PANAHI

3 Nursing and midwifery school, Shahid Beheshti University of Medical Sciences, Tehran, Iran;

MEHRDAD KARIMI

4 Department of Epidemiology and Biostatistics, Public Health School, Tehran, Iran;

HAYEDE GHOLSORKHI

5 Medical school, Alborz University of Medical Sciences, Karaj, Iran;

ZARRIN AHMADI

6 Amirkabir University of Technology(Polytechnic), Tehran, Iran

Introduction: The Current world needs people who have a lot of different abilities such as cognition and application of different ways of thinking, research, problem solving, critical thinking skills and creativity. In addition to critical thinking, learning styles is another key factor which has an essential role in the process of problem solving. This study aimed to determine the relationship between learning styles and critical thinking of students and their academic performance in Alborz University of Medical Science.

Methods: This cross-correlation study was performed in 2012, on 216 students of Alborz University who were selected randomly by the stratified random sampling. The data was obtained via a three-part questionnaire included demographic data, Kolb standardized questionnaire of learning style and California critical thinking standardized questionnaire. The academic performance of the students was extracted by the school records. The validity of the instruments was determined in terms of content validity, and the reliability was gained through internal consistency methods. Cronbach's alpha coefficient was found to be 0.78 for the California critical thinking questionnaire. The Chi Square test, Independent t-test, one way ANOVA and Pearson correlation test were used to determine relationship between variables. The Package SPSS14 statistical software was used to analyze data with a significant level of p<0.05.

Results: Our findings indicated the significant difference of mean score in four learning style, suggesting university students with convergent learning style have better performance than other groups. Also learning style had a relationship with age, gender, field of study, semester and job. The results about the critical thinking of the students showed that the mean of deductive reasoning and evaluation skills were higher than that of other skills and analytical skills had the lowest mean and there was a positive significant relationship between the students’ performance with inferential skill and the total score of critical thinking skills (p<0.05). Furthermore, evaluation skills and deductive reasoning had significant relationship. On the other hand, the mean total score of critical thinking had significant difference between different learning styles.

Conclusion: The results of this study showed that the learning styles, critical thinking and academic performance are significantly associated with one another. Considering the growing importance of critical thinking in enhancing the professional competence of individuals, it's recommended to use teaching methods consistent with the learning style because it would be more effective in this context.

Introduction

The current world needs people with a lot of capabilities such as understanding and using different ways of thinking, research, problem solving, critical thinking and creativity. Critical thinking is one of the aspects of thinking that has been accepted as a way to overcome the difficulties and to facilitate the access to information in life ( 1 ).

To Watson and Glizer, critical thinking is a combination of knowledge, attitude, and performance of every individual. They also believe that there are some skills of critical thinking such as perception, assumption recognition deduction, interpretation and evaluation of logical reasoning. They argue that the ability of critical thinking, processing and evaluation of previous information with new information result from inductive and deductive reasoning of solving problems. Watson and Glizer definition of critical thinking has been the basis of critical thinking tests that are widely used to measure the critical thinking today ( 2 ).

World Federation for Medical Education has considered critical thinking one of the medical training standards so that in accredited colleges this subject is one of the key points. In fact, one of the criteria for the accreditation of a learning institute is the measurement of critical thinking in its students ( 3 ).

In addition to critical thinking, learning style, i.e. the information processing method, of the learners, is an important key factor that has a major role in problem solving. According to David Kolb’s theory, learning is a four-step process that includes concrete experience, reflective observation, abstract conceptualization and active experimentation. This position represents two dimensions: concrete experience versus abstract thinking, and reflective observation to active experimentation. These dimensions include four learning styles: divergent, convergent, assimilate, and accommodate. According to Kolb and Ferry, the learner needs four different abilities to function efficiently: Learning styles involve several variables such as academic performance of learner, higher education improvement; critical thinking and problem solving ( 4 ).

Due to the importance of learning styles and critical thinking in students' academic performance, a large volume of educational research has been devoted to these issues in different countries. Demirhan, Besoluk and Onder (2011) in their study on critical thinking and students’ academic performance from the first semester to two years later have found that contrary to expectations the students’ critical thinking level reduced but the total mean of students’ scores increased. This is due to the fact that the students are likely to increase adaptive behavior with environment and university and reduce the stress during their education ( 1 ).

In another study over 330 students in Turkey, the students who had divergent learning style, had lower scores in critical thinking in contrast with students who have accommodator learning style ( 5 ).

Also Mahmoud examined the relationship between critical thinking and learning styles of the Bachelor students with their academic performance in 2012. In this study all the nursing students of the university in the semesters four, six and eight were studied. The results did not show any significant relationship between critical thinking and learning styles of nursing students with their academic performance ( 6 ).

Another research by Nasrabadi in 2012 showed a positive relationship between critical thinking attitudes and student's academic achievement. The results showed that there was a significant difference between the levels of critical thinking of assimilating and converge styles. Also converging, diverging, assimilating and accommodating styles had the highest level of critical thinking, respectively ( 4 ). Among other studies we can refer to Sharma’s study in 2011 whose results suggested a relationship between the academic performance and learning styles ( 7 ).

Today university students should not only think but also should think differently and should not only remember the knowledge in their mind but also should research the best learning style among different learning styles. Therefore, the study on the topic of how the students think and how they learn has received great emphasis in recent years. In this regard, with the importance of the subject, researchers attempted to doa research in this area to determine the relationship between critical thinking and learning styles with academic performance of the students at Alborz University of Medical Sciences.

This study is a descriptive-analytic, cross sectional study and investigates the relationship between critical thinking and learning styles with students’ academic performance of Alborz University of Medical Science in 2012. After approval and permission from university’s authorities and in coordination with official faculties, the critical thinking and learning styles questionnaire was given to the undergraduate students in associate degree, bachelor, medicine (second semester and after that). The total number of participants in the study was 216 students with different majors such as medical, nursing and midwifery, and health and medical emergency students. The tool to collect the data was a two-part questionnaire of Kolb's learning styles and California's critical thinking skills test (form B). The Kolb's questionnaire has two parts. The first part asks for demographic information and the second part includes 12 multiple choice questions. The participants respond to the questions with regard to how they learn, and the scores of respondents are ranked from 1 to 4 in which 4 is most consistent with the participants’ learning style 3 to some extent, 2 poorly consistent and 1 not consistent To find the participants’ learning styles, the first choice of all 12 questions were added together and this was repeated for other choices. Thus, four total scores for the four learning styles were obtained, the first for concrete experience learning style, the second for reflective observation of learning style, the third for abstract conceptualization learning style and the forth for active experimentation learning style. The highest score determined the learning style of the participant. The California critical thinking skills test (form B) includes 34 multiple choice questions with one correct answer in five different areas of critical thinking skills, including evaluation, inference, analysis, inductive reasoning and deductive reasoning. The answering time was 45 minutes and the final score is 34 and the achieved score in each section of the test varies from 0 to 16. In the evaluation section, the maximum point is 14, in analysis section 9, in inference section 11, in inductive reasoning 16 and in deductive reasoning the maximum point was 14. So there were 6 scores for each participant, which included a critical thinking total score and 5 score for critical thinking skills. Dehghani, Jafari Sani, Pakmehr and Malekzadeh found that the reliability of the questionnaire was 78% in a research. In the study of Khalili et al., the confidence coefficient was 62% and construct validity of all subscales with positive and high correlation were reported between 60%-65%. So this test was reliable for the research. Collecting the information was conducted in two stages. In the first stage, the questionnaires were given to the students and the objectives and importance of the research were mentioned. In the next stage, the students' academic performance was reviewed. After data collection, the data were coded and analyzed, using the SPSS 14 ( SPSS Inc, Chicago, IL, USA) software. To describe the data, descriptive statistics were used such as mean and standard deviation for continues variables and frequency for qualitative variables. Chi Square test, Independent t-test, one way ANOVA and Pearson correlation test were used to determine the relationship between variables at a significant level of p<0.05.

Research hypothesis

• There is a relationship between Alborz University of Medical Sciences students’ learning styles and their demographic information. 
• There is a relationship between Alborz University of Medical Sciences students’ critical thinking and their demographic information. 
• There is a relationship between Alborz University of Medical Sciences students’ academic performance and their demographic information. 
• There is a relationship between Alborz University of Medical Sciences students’ learning styles and their academic performance. 
• There is a relationship between Alborz University of Medical Sciences students’ learning styles and their critical thinking. 

225 questionnaires were distributed of which 216 were completely responded (96%). The age range of the participants was from 16 to 45 with the mean age of (22.44±3.7). 52.8% of participants (n=114) were female, 83.3% (n=180) were single, 30.1% of participants’ (n=65) major was pediatric anesthesiology of OR, 35.2% of participants (n=76) were in fourth semester, 74.5% (n=161) were unemployed and 48.6 % (n=105) had Persian ethnicity.

The range of participants’ average grade points, which were considered as their academic performance, were from 12.51 to 19.07 with a mean of (16.75±1.3). According to Kolbs' pattern, 42.7% (n=85) had the convergent learning style (the maximum percentage) followed by 33.2 % (n= 66) with the assimilating style and only 9.5%, (n= 19) with the accommodating style (the minimum percentage).

Among the 5 critical thinking skills, the maximum mean score belonged to deductive reasoning skill (3.38±1.58) and the minimum mean score belonged to analysis skill (1.67±1.08).

Table 1 shows the frequency distribution and demographic variables and the academic performance of the students. According to the Chi-square (Χ 2 ) p-value, there was a significant relationship between gender and learning style (p=0.032), so that nearly 50 percent of males had the assimilating learning style and nearly 52 percent of the females had the convergent learning style.

The relationship between demographic variable and student’s academic performance with learning styles

The relationship between employment, major and semester of studying with the learning style was significant at a p-value of 0.049, 0.006, 0.009 and 0.001, respectively. The mean and standard deviation of age and students' academic performance in the four learning styles are reported in Table 1 .

Using the one way analysis of variance (One way ANOVA) and comparing the mean age of four groups, we found a significant relation between age and academic performance with learning style (p=0.049).

The students with convergent learning style had a better academic performance than those with other learning styles and in the performance of those with the assimilating learning style the weakest.

Table 2 shows the relationship between the total score of critical thinking skills and each of the demographic variables and academic performance. The results of the t-test and one way ANOVA variance analysis are reported to investigate the relationship between each variable with skills below the mean standard deviation.

Relationships between CCT Skills and demographic variables Using t-test and ANOVA. Pearson Correlation coefficient between age and Student's performance with CCT Skills was reported

* Significant in surface 0.05 

** Significant in surface 0.01

Based on the t-test and ANOVA, p-value of t and F, the mean of total score of critical thinking skills had only significant relationship with students’ major (p=0.020). Also a significant relationship was found between the major of students and gender with inference skill; semester of study with deductive reasoning skill, and ethnicity with 2 skills of inference and deductive reasoning (p<0.05).

Also regarding the relationship between age and the student academic performance with each of the critical thinking skills, the Pearson correlation coefficient results indicated a significant positive relationship but a negative relationship between age and analysis skill, i.e. with the increase of age, the score of analysis skill was reduced (p<0.05). Academic performance of the students had a direct significant relationship with critical thinking total score and inference skill; the more the score, the better the academic performance of students (p<0.05).

Table 3 shows the mean and standard deviation of learning styles score in the 4 groups of learning style. Using ANOVA one way ANOVA, the relationship between learning style and critical thinking skills and the comparison of the mean score for each skill in four styles are reported in the last column of the Table 3 .

The Relationship between critical thinking styles with learning styles

Based on the p-value of ANOVA, the mean of evaluation skill and inductive reasoning skill had a significant difference and the relationship between these two skills with learning style was significant (p<0.05). Also the mean of critical thinking’s total score was significantly different in the four groups and the relationship between total score with learning style was significant, too (p<0.05).

The mean and confidence interval of university students’ performance in four learning  styles

The mean and confidene interval of critical thinking skills

The study findings showed that the popular learning style among the students was the convergent style followed by the assimilating style which is consistent with Kolb's theory stating that medical science students usually have this learning style ( 8 ). This result was consistent with the results of other studies ( 9 , 10 ). In Yenice's study in which the student of training teacher were the target of the project, the most frequent learning styles were divergent and assimilating styles and these differences originate from the different target group of study in 2012 ( 11 ).

This study showed a significant relationship between learning style and gender, age, semester and employment. Meyari et al. did not find any significant relationship between learning style, age and gender of the freshman but for the fifth semester students, a significant relationship with age and gender was found ( 10 ). Also in Yenice's study, no relationship with learning style, gender, semester and age was found.

Furthermore, in the first semester divergent style, in the second semester assimilating style and in the third and fourth semester divergent style were accounted for the highest percentage. Also in the group age of 17-20 years the assimilating style and the age of 21-24 years the divergent style were dominant styles ( 11 ).

In the present study, it was found a significant positive relationship between convergent learning style and academic performance. Also in the study of Pooladi et al. the majority of the students had convergent style and they also found a significant relationship between learning style, total mean score and the mean of practical courses ( 12 ). Nasrabadi et al. found that students with the highest achievement were those with convergent style with a significant difference with those with divergent style ( 4 ). But the results are inconsistent to Meyari et al.’s ( 10 ).

In this study, the obtained mean score from the critical thinking questionnaire was (7.15±2.41) that was compared with that in the study of Khalili and Hoseinzadeh which was to validate and make reliable the critical thinking skills questionnaire of California (form B) in the Iranian nursing students; the mean of total score was about the 11th percentile of this study ( 13 ).

In other words, the computed score for critical thinking of the students participating was lower than 11 score that is in the 50th percentile and of course is lower than normal range.

Hariri and Bagherinezhad had shown that the computed score for Bachelor and Master students of Health faculty was also lower than the norm in Iran ( 14 ). Also Mayer and Dayer came to a similar conclusion in critical thinking skill in the Agricultural university of Florida’s students in 2006 ( 15 ).

But in Gharib et al.’s study, the total score of critical thinking test among the freshman and senior of Health-care management was in normal range ( 16 ). Wangensteen et al., found that the critical thinking skills of the newest graduate nursing students were relatively high in Sweden in 2010 ( 17 ).

In this study, students of all levels (Associate, Bachelor and PhD) with various fields of study participated but other studies have been limited to certain graduate courses that may explain the differences in levels of special critical thinking skills score in this study. In this study we found a significant relationship between total score of critical thinking and major of the students. This result is consistent with Serin et al. ( 18 ).

It was found a significant relationship between major of participants, gender and inference skill, semester and deductive reasoning skill, ethnicity and both inference and deductive reasoning skills.

In the Yenice's study significant relationship between critical thinking, group of age, gender and semester was seen ( 11 ). In Wangensteen et al.’s ( 17 ) study in the older age group, the level of critical thinking score increased. In Serin et al.’s ( 18 ) study the level of communication skills in girls was better than that in boys. And also a significant relationship was found between critical thinking and academic semester, but in Mayer and Dayer’s study no significant relationship between critical thinking levels and gender was found ( 4 , 15 ).

The results also showed that the total score of critical thinking and analytical skills of students and their performance had a significant relationship. Nasrabady et al.’s study also showed that there was a positive relationship between critical thinking reflection attitude and academic achievement ( 4 ). This is contradictory with what Demirhan, Bosluk and Ander found ( 6 , 15 ).

The results of the relationship between learning style and critical thinking indicated that the relationship between evaluation and inductive reasoning was significant to learning style (p<0.05). The relationship of critical thinking total score with learning style was also significant (p<0.05). Thus the total score for those with the conforming style of critical skills was more than that with other styles. But in the subgroup of inference skills, those with the convergent style had a higher mean than those with other styles.

Yenice found a negative relationship between critical thinking score and divergent learning style and a positive relation between critical thinking score and accommodating style ( 11 ).

Siriopoulos and Pomonis in their study compared the learning style and critical thinking skills of students in two phases: at the beginning and end of education and came to this conclusion that the learning style of students changed in the second phase.

For example, the divergent, convergent and accommodating styles languished and the assimilating style (combination of abstract thinking and reflective observation) was noticeably strengthened. However, those with converging learning style had higher levels of critical thinking.

The level of students’ critical thinking was lower in all international standards styles. Perhaps it was because of widely used teacher-centered teaching methods (lectures) in that university ( 19 ).

The results in the study of Nasrabady et al. showed that there was a significant difference between the level of learners’ critical thinking and divergent and assimilating styles ( 4 ).

Those with converging, diverging, assimilating and accommodating styles had the highest level of critical thinking, respectively.

Also there was a positive significant relationship between the reflective observation method and critical thinking and also a negative significant relationship between the abstract conceptualization method and critical thinking ( 4 ). But in another study that Mahmud has done in 2012, he did not find any significant relationship between learning style, critical thinking and students’ performance ( 6 ).

The results of this study showed that the students’ critical thinking skills of this university aren't acceptable. Also learning styles, critical thinking and academic performance have significant relationship with each other. Due to the important role of critical thinking in enhancing professional competence, it is recommend using teaching methods which are consistent with the learning styles.

Acknowledgment

This study is based on a research project that was approved in Research Deputy of Alborz University of Medical sciences. We sincerely appreciate all in Research Deputy of Alborz University of Medical sciences who supported us financially and morally and all students and colleagues who participated in this study.

Conflict of Interest: None declared.

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Critical thinking refers to deliberately scrutinizing and evaluating theories, concepts, or ideas using reasoned reflection and analysis. The act of thinking critically involves moving beyond simply understanding information, but rather, to question its source, its production, and its presentation in order to expose potential bias or researcher subjectivity [i.e., being influenced by personal opinions and feelings rather than by external determinants ] . Applying critical thinking to investigating a research problem involves actively challenging basic assumptions and questioning the choices and potential motives underpinning how the author designed the study, conducted the research, and arrived at particular conclusions or recommended courses of action.

Mintz, Steven. "How the Word "Critical" Came to Signify the Leading Edge of Cultural Analysis." Higher Ed Gamma Blog , Inside Higher Ed, February 13, 2024; Van Merriënboer, Jeroen JG and Paul A. Kirschner. Ten Steps to Complex Learning: A Systematic Approach to Four-component Instructional Design . New York: Routledge, 2017.

Thinking Critically

Applying Critical Thinking to Research and Writing

Professors like to use the term critical thinking; in fact, the idea of being a critical thinker permeates much of higher education writ large. In the classroom, the idea of thinking critically is often mentioned by professors when students ask how they should approach a research and writing assignment [other approaches your professor might mention include interdisciplinarity, comparative, gendered, global, etc.]. However, critical thinking is more than just an approach to research and writing. It is an acquired skill associated with becoming a complex learner capable of discerning important relationships among the elements of, as well as integrating multiple ways of understanding applied to, the research problem. Critical thinking is a lens through which you holistically interrogate a topic.

Given this, thinking critically encompasses a variety of inter-related connotations applied to writing a college-level research paper:

• Integrated and Multi-Dimensional . Critical thinking is not focused on any one element of research, but instead, is applied holistically throughout the process of identifying the research problem, reviewing the literature, applying methods of analysis, describing the results, discussing their implications, and, if appropriate, offering recommendations for further research. It permeates the entire research endeavor from contemplating what to write to proofreading the final product.
• Humanizes the Research . Thinking critically can help humanize what is being studied by extending the scope of your analysis beyond the traditional boundaries of prior research. This prior research could have involved, for example, sampling homogeneous populations, considering only certain factors related to the investigation of a phenomenon, or limiting the way authors framed or contextualized their study. Critical thinking creates opportunities to incorporate the experiences of others into the research process, leading to a more inclusive and representative examination of the topic.
• Non-Linear . This refers to analyzing a research problem in ways that do not rely on sequential decision-making or rational forms of reasoning. Creative thinking relies on intuitive judgement, flexibility, and unconventional approaches to investigating complex phenomena in order to discover new insights, connections, and potential solutions . This involves going back and modifying your thinking as new evidence emerges , perhaps multiple times throughout the research process, and drawing conclusions from multiple perspectives.
• Normative . This is the idea that critical thinking can be used to challenge prior assumptions in ways that advocate for social justice, equity, and inclusion and that can lead to research having a more transformative and expansive impact. In this respect, critical thinking can be viewed as a method for breaking away from dominant culture norms so as to produce research outcomes that illuminate previously hidden aspects of exploitation and injustice.
• Power Dynamics . Research in the social sciences often includes examining aspects of power and influence that shape social relations, organizations, institutions, and the production and maintenance of knowledge. These studies focus on how power operates, how it can be acquired, and how power and influence can be maintained. Critical thinking can reveal how societal structures perpetuate power and influence in ways that marginalizes and oppresses certain groups or communities within the contexts of history , politics, economics, culture, and other factors.
• Reflection . A key component of critical thinking is practicing reflexivity; the act of turning ideas and concepts back onto yourself in order to reveal and clarify your own beliefs, assumptions, and perspectives. Being critically reflexive is important because it can reveal hidden biases you may have that could unintentionally influence how you interpret and validate information. The more reflexive you are, the better able and more comfortable you are in opening yourself up to new modes of understanding.
• Rigorous Questioning . Thinking critically is guided by asking questions that lead to addressing complex concepts, principles, theories, or problems more effectively and, in so doing, help distinguish what is known from from what is not known [or that may be hidden]. Critical thinking involves deliberately framing inquiries not just as research questions, but as a way to apply systematic, disciplined,  in-depth forms of questioning concerning the research problem and your positionality as a researcher.
• Social Change . An overarching goal of critical thinking applied to research and writing is to seek to identify and challenge sources of inequality, exploitation, oppression, and marinalization that contributes to maintaining the status quo within institutions of society. This can include entities, such as, schools, courts, businesses, government agencies, or religious organizations, that have been created and maintained through certain ways of thinking within the dominant culture.

Although critical thinking permeates the entire research and writing process, it applies most directly to the literature review and discussion sections of your paper . In reviewing the literature, it is important to reflect upon specific aspects of a study, such as, determining if the research design effectively establishes cause and effect relationships or provides insight into explaining why certain phenomena do or do not occur, assessing whether the method of gathering data or information supports the objectives of the study, and evaluating if the assumptions used t o arrive at a specific conclusion are evidence-based and relevant to addressing the research problem. However, an assessment of whether a source is helpful to investigating the research problem also involves critically analyzing how the research challenges conventional approaches to investigations that perpetuate inequalities or hides the voices of others.

Critical thinking applies to the discussion section of your paper because this is where you internalize the results of your study and explain its significance. This involves more than summarizing findings and describing outcomes. It includes reflecting on their importance and providing reasoned explanations why your paper is important in filling a gap in the literature or expanding knowledge and understanding in ways that inform practice. Critical reflection helps you think introspectively about your own beliefs concerning the significance of the findings, but in ways that avoid biased judgment and decision making.

Behar-Horenstein, Linda S., and Lian Niu. “Teaching Critical Thinking Skills in Higher Education: A Review of the Literature.” Journal of College Teaching and Learning 8 (February 2011): 25-41; Bayou, Yemeserach and Tamene Kitila. "Exploring Instructors’ Beliefs about and Practices in Promoting Students’ Critical Thinking Skills in Writing Classes." GIST–Education and Learning Research Journal 26 (2023): 123-154; Butcher, Charity. "Using In-class Writing to Promote Critical Thinking and Application of Course Concepts." Journal of Political Science Education 18 (2022): 3-21; Loseke, Donileen R. Methodological Thinking: Basic Principles of Social Research Design. Thousand Oaks, CA: Sage, 2012; Mintz, Steven. "How the Word "Critical" Came to Signify the Leading Edge of Cultural Analysis." Higher Ed Gamma Blog , Inside Higher Ed, February 13, 2024; Hart, Claire et al. “Exploring Higher Education Students’ Critical Thinking Skills through Content Analysis.” Thinking Skills and Creativity 41 (September 2021): 100877; Lewis, Arthur and David Smith. "Defining Higher Order Thinking." Theory into Practice 32 (Summer 1993): 131-137; Sabrina, R., Emilda Sulasmi, and Mandra Saragih. "Student Critical Thinking Skills and Student Writing Ability: The Role of Teachers' Intellectual Skills and Student Learning." Cypriot Journal of Educational Sciences 17 (2022): 2493-2510. Suter, W. Newton. Introduction to Educational Research: A Critical Thinking Approach. 2nd edition. Thousand Oaks, CA: SAGE Publications, 2012; Van Merriënboer, Jeroen JG and Paul A. Kirschner. Ten Steps to Complex Learning: A Systematic Approach to Four-component Instructional Design. New York: Routledge, 2017; Vance, Charles M., et al. "Understanding and Measuring Linear–Nonlinear Thinking Style for Enhanced Management Education and Professional Practice." Academy of Management Learning and Education 6 (2007): 167-185; Yeh, Hui-Chin, Shih-hsien Yang, Jo Shan Fu, and Yen-Chen Shih. "Developing College Students’ Critical Thinking through Reflective Writing." Higher Education Research & Development 42 (2023): 244-259.

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Exploring Factors That Support Pre-service Teachers’ Engagement in Learning Artificial Intelligence

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• Published: 12 April 2024

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• Musa Adekunle Ayanwale   ORCID: orcid.org/0000-0001-7640-9898 1 ,
• Emmanuel Kwabena Frimpong 2 ,
• Oluwaseyi Aina Gbolade Opesemowo   ORCID: orcid.org/0000-0003-0242-7027 1 &
• Ismaila Temitayo Sanusi   ORCID: orcid.org/0000-0002-5705-6684 2  

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Artificial intelligence (AI) is becoming increasingly relevant, and students need to understand the concept. To design an effective AI program for schools, we need to find ways to expose students to AI knowledge, provide AI learning opportunities, and create engaging AI experiences. However, there is a lack of trained teachers who can facilitate students’ AI learning, so we need to focus on developing the capacity of pre-service teachers to teach AI. Since engagement is known to enhance learning, it is necessary to explore how pre-service teachers engage in learning AI. This study aimed to investigate pre-service teachers’ engagement with learning AI after a 4-week AI program at a university. Thirty-five participants took part in the study and reported their perception of engagement with learning AI on a 7-factor scale. The factors assessed in the survey included engagement (cognitive—critical thinking and creativity, behavioral, and social), attitude towards AI, anxiety towards AI, AI readiness, self-transcendent goals, and confidence in learning AI. We used a structural equation modeling approach to test the relationships in our hypothesized model using SmartPLS 4.0. The results of our study supported all our hypotheses, with attitude, anxiety, readiness, self-transcendent goals, and confidence being found to influence engagement. We discuss our findings and consider their implications for practice and policy.

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Introduction

Artificial intelligence (AI) is becoming increasingly relevant globally, integrated into various aspects of human life and sectors, including education (Long & Magerko, 2020 ). The growing importance of AI has led to a demand for its incorporation into school systems. While researchers, practitioners, and education policymakers have recognized the significance of teaching AI in K-12 systems (Ma et al., 2023, Touretzky et al., 2019), limited initiatives have been taken in the context of teacher education (Sanusi et al., 2022 ). Nevertheless, education stakeholders agree about the importance of AI education, as evidenced by the development of tools, curricula activities, and frameworks for effective implementation of AI as a subject or integrated throughout the curriculum (Casal-Otero et al., 2023 ; Mahipal et al., 2023 ; Sanusi, 2023 ). While these initiatives are crucial for promoting AI education in schools, focusing on teacher education is essential (Sanusi et al., 2023 ). Existing literature highlights a need for further work on teacher education programs for AI. Although there are a few initiatives for teacher education on AI, they are primarily conducted as professional development programs. However, to ensure the integration of AI within the K-12 system, future teachers must be prepared to facilitate AI, as it is now considered an essential skill for the future (Frimpong, 2022 ; Park et al., 2023 ).

As a new subject in schools and teacher education programs, learning AI requires new approaches to engage students with learning materials and activities. Engagement is crucial because studies have found a correlation between engagement and learning (Carroll et al., 2021 ; Fredricks et al., 2004 ; Poondej & Lerdpornkulrat, 2016 ). These studies suggest that more engaged students tend to have better learning outcomes. Bryson and Hand ( 2007 ) stated that engagement is key to student autonomy and improved learning overall. Given the importance of engagement, research has been conducted to understand how to increase students’ engagement in learning. For example, Kim et al. ( 2015 ) explored the use of robotics to promote STEM engagement in pre-service teachers, while Volet et al. ( 2019 ) examined engagement in collaborative science learning among pre-service teacher students. Although research on pre-service teachers and engagement in STEM learning continues to grow, there is currently a limited research on student engagement in AI education. Xia et al. ( 2022 ) discussed student engagement from the perspective of self-determination theory, but no study has investigated the factors that influence pre-service teachers’ engagement with AI. Therefore, this research aims to examine the factors that support students’ engagement with AI in the context of teacher education. The framework used in this research combines the theory of planned behavior (Ajzen, 2020 ) with other constructs, including engagement. By exploring the factors that support pre-service teachers’ engagement in learning AI, this study contributes to the limited literature on developing AI literacy within teacher education programs. The findings of this research will advance our knowledge of how to effectively engage students in learning AI.

To better understand the factors that impact student engagement with learning AI, we conducted an AI intervention for 35 pre-service teachers. We then collected their perspectives using a 7-factor scale, considering engagement (cognitive-critical thinking and creativity, behavioral, and social), intrinsic motivation, attitude towards AI, anxiety towards AI, AI readiness, self-transcendent goals, and confidence in learning AI. To analyze the participants’ data, we utilized SmartPLS 4.0 to perform a variance-based structural equation modeling and evaluate our proposed model. This study is organized as follows: first, we outline the aim of the study; then, we review related research, discuss the theoretical framework, and develop hypotheses in the “Review of Related Work” section. The “ Methodology ” section provides a detailed explanation of the data collection method, participants, and analytical approaches. In the “ Results ” section, we present the findings of the data analysis, followed by a discussion of the implications of the study in the “ Discussion ” section. Finally, we conclude with a discussion of the study’s limitations and suggestions for future research.

Review of Related Work

In this section, we reviewed the related works and developed the study hypothesis. We specifically discussed the research that has explored pre-service teachers’ engagement within the STEM (science, technology, engineering, and mathematics) education context. We further explained the theoretical framework that inspired our research, highlighted why exploring engagement in learning AI is essential, and proposed a set of hypotheses based on Fig.  1 .

Research conceptual framework. Note: AT attitude towards AI, AN anxiety towards AI, AR AI readiness, SG self-transcendent goals, CL confidence in learning AI, ENG student engagement in the AI program, CECT cognitive engagement—critical thinking, CEC cognitive engagement—creativity, BESL behavioral engagement—self-directed learning, SOE social engagement

Engagement in the STEM Teacher Education Program

Engagement in STEM teacher education programs is crucial for improving student outcomes in STEM subjects. Research has shown that active engagement in STEM education leads to higher-order thinking skills, increased motivation, and improved achievement in learning activities (Kamarrudin et al., 2023 ). Engagement in STEM learning has been recognized as beneficial for preparing students to address real-world problems (Dong et al., 2019 ; Kim et al., 2015 ). Challenges in implementing integrated STEM curricula in schools due to the lack of teachers’ experience have also been reported in the literature (e.g., Hamad et al., 2022 ). Several studies (Aydeniz & Bilican, 2018 ; Dong et al., 2019 ) investigated the relationship among different variables and engagement. However, few empirical evidence exists on what predicts pre-service teachers’ engagement in STEM education programs.

Furthermore, there is a paucity of research that focuses on the factors that influence pre-service teachers’ engagement in learning AI. As AI is considered a STEM-related concept, this study aims to fill this gap by investigating the factors influencing pre-service teachers’ engagement in learning AI. Understanding these factors will provide valuable insights into how to effectively prepare pre-service teachers to integrate AI into their teaching practices. This study will also contribute to developing strategies and interventions to enhance pre-service teachers’ learning experiences in AI. In addition, findings from this study will have practical implications for teacher education programs and curriculum development. By identifying the specific factors such as attitude towards AI, anxiety towards AI, AI readiness, self-transcendent goals, and confidence in learning AI that influence pre-service teachers’ engagement in learning AI, pre-service teachers can tailor their pedagogical approach to meet their students’ needs and interests better. Ultimately, the goal is to equip pre-service teachers with the necessary knowledge and skills to integrate AI effectively into their classrooms, ensuring they are prepared adequately for the ever-evolving technological landscape of education.

Extensive research indicates the importance of engagement in learning (Fredricks et al., 2004 ; Tarantino et al., 2013 ). Student engagement has also been referred to as a crucial means of fostering and enhancing student learning (Renninger & Bachrach, 2015 ; Sanusi et al., 2023 ). Engagement is characterized by the behavioral intensity and emotional quality of a person’s active involvement in a task (Sun et al., 2019 ). Without engagement, meaningful learning remains elusive (Kim et al., 2015 ) and cannot accurately determine the extent to which a person has grasped a concept. Within the context of teacher education, particularly in STEM-related programs, we have identified literature that emphasizes the significance of engagement in promoting increased learning (Lange et al., 2022 ; Ryu et al., 2019 ). Previous research (e.g., Grimble, 2019 ; McClure et al., 2017 ) suggests that pre-service teachers’ engagement with learning materials fosters a deep mastery of the subject matter and effective pedagogical practices that can stimulate their students' interest in STEM.

Teacher education programs can equip pre-service teachers with the skills and knowledge necessary to cultivate STEM literacy in the next generation by immersing them in hands-on experiences, encouraging them to explore real-world applications, and supporting collaborative learning (Suryadi et al., 2023 ). In this way, pre-service teachers become more than just conveyors of information; they also foster curiosity, problem-solving, and innovation in their classrooms, cultivating a lifelong interest in STEM disciplines. Moreover, integrating STEM courses into teacher education programs helps pre-service teachers develop a growth mindset and adaptability (Griful-Freixenet et al., 2021 ; Jones et al., 2017 ; Rowston et al., 2020 ), both of which are necessary for navigating the ever-changing landscape of science and technology. Pre-service teachers’ engagement and learning of STEM subjects in teacher education programs are vital for their future performance in the classroom (Berisha & Vula, 2021; Bosica et al., 2021 ). These programs should emphasize acquiring topic knowledge and developing teaching practices that encourage students’ active participation. By implementing active participation in STEM education programs, pre-service teachers better understand STEM concepts and learn how to create dynamic and interactive learning environments (Billington, 2023 ; Yllana-Prieto et al., 2023 ). Exposure to various teaching methods (Bin Abdulrahman et al., 2021 ) and the integration of technology provide students with the necessary capabilities to meet the evolving needs of STEM education. Similarly, encouraging pre-service teachers’ engagement in STEM courses goes beyond the transfer of knowledge (Huang et al., 2022 ; Manasia et al., 2020 ). It instills a passion for these disciplines, inspires them to develop a growth mindset, and cultivates lifelong learners.

Berisha and Vula (2021) stated that the engagement and learning of pre-service teachers in STEM subjects are crucial for their professional development and the success of their future students. Teacher education programs strive to equip pre-service teachers with the knowledge and skills to effectively teach STEM subjects to their future students (Yang & Ball, 2022). It is important to foster their curiosity and enhance their problem-solving abilities. By actively engaging in STEM learning during these programs, educators become well-prepared to inspire the future of innovation and scientific discovery, ensuring a brighter future for STEM education. Encouraging pre-service teachers’ interest in and learning of STEM courses helps build their confidence and competence in making these subjects accessible and enjoyable for their future students. As pre-service teachers become more adept in using STEM teaching methods, they are better equipped to address the challenges and misconceptions that often discourage students from pursuing STEM careers (Akaygun & Aslan-Tutak, 2020; Çinar et al., 2016; Delello, 2014). Ultimately, the success of STEM teacher education programs hinges on their ability to instill a genuine passion for these subjects in pre-service teachers, while providing them with the knowledge and skills to inspire the next generation of problem-solvers, critical thinkers, and innovators.

Theoretical Background

This study is based on the theory of planned behavior (Ajzen, 2020 ) and incorporates other relevant constructs. In the field of AI education, this theory has primarily been used to examine the intentions of various stakeholders in terms of learning (Chai et al., 2020a , 2020b ; Sing et al., 2022 ) or teaching AI (Ayanwale & Sanusi, 2023; Ayanwale et al., 2022 ). These constructs have previously been used as predictors of behavioral intention. However, we have not found any studies that specifically utilize these constructs as predictors of engagement in the context of AI education, particularly in teacher education programs. Nonetheless, we briefly mention some instances where the variables examined in this study are related to engagement in similar fields.

Attitude Towards AI

In STEM programs, attitudes towards AI education play a crucial role in determining pre-service teachers’ readiness for the evolving educational landscape. A positive attitude towards AI encourages acceptance of its value as a tool to enhance STEM instruction (Papadakis et al., 2021 ), while negative attitudes can lead to resistance and limited adoption (Balakrishnan et al., 2021 ). Pre-service teachers must develop an open-minded attitude towards AI, enabling them to leverage its potential for personalized learning and innovative teaching. This will also ensure that AI becomes a valuable tool in their future STEM classrooms. The engagement of pre-service teachers in AI education is grounded in educational theories and pedagogical principles (Celik, 2023 ). Constructivist theories emphasize the significance of active engagement, collaboration, and hands-on experiences in learning (Kaufman, 1996 ). AI education for pre-service teachers aligns with these theories, advocating for immersive and experiential learning opportunities. Furthermore, the literature (Celik, 2023 ; Shelman, 1987; Yau et al., 2023 ) draws upon the principles of technological pedagogical content knowledge (TPACK), suggesting that effective AI education involves the integration of technological knowledge, pedagogical skills, and subject matter expertise. Theoretical perspectives often emphasize the importance of pre-service teachers developing a positive attitude (Opesemowo et al., 2022 ) and a deep understanding of AI concepts and their applications in educational settings. However, studies (Al Darayseh, 2023 ; Kelly et al., 2023 ; Zhang et al., 2023 ) have demonstrated that attitude is a critical factor that influences teachers’ acceptance or rejection of the use of AI. Some individuals hold a positive attitude towards AI technologies and recognize their potential, even if they do not fully comprehend the essence of these technologies (Yadrovskaia et al., 2023 ). Kaya et al. ( 2024 ) observed that personality traits, AI anxiety, and demographics significantly shape attitudes towards AI. The use of AI in the STEM context is an ongoing topic of public discourse, and there is a need for reliable measures to assess pre-service teachers' attitudes towards AI in STEM programs.

Anxiety Towards AI

Anxiety towards AI refers to the fear of using computers or technophobia, which is a term used to describe fear or aversion towards technology in general (Li & Huang, 2020 ; Wang & Wang, 2022 ). Various perspectives on anxiety towards AI and pre-service teachers’ education in STEM programs have been proposed. Some argue that anxiety towards AI stems from a lack of understanding and fear of the unknown (Hopcan et al., 2023 ; Zhan et al., 2023 ). They suggest that pre-service teachers can better understand and overcome their anxiety by receiving comprehensive education in AI technologies. Others believe that anxiety towards AI among pre-service teachers is justified because they feel threatened by AI advancements’ potential job market implications. Anxiety towards AI education in STEM programs can hinder pre-service teachers’ acceptance of technology-driven teaching techniques. This apprehension may stem from concerns about their technological skills or anxieties that AI may replace traditional instructional responsibilities. Pre-service instructors can build confidence in AI tools by addressing these concerns through training and assistance (Jones et al., 2017 ). It is crucial to foster an environment that encourages experimentation while highlighting the complementary role of AI in improving STEM education, reducing anxiety, and promoting its beneficial integration. Kaya et al. ( 2024 ) noted that anxiety about learning AI significantly predicted positive and negative attitudes towards AI. According to Terzi ( 2020 ) and Wang and Wang ( 2022 ), anxiety about learning AI is the fear of being unable to acquire specific knowledge and skills about AI. Several studies have been conducted on anxiety towards AI, but few or none has explored the engagement of pre-service teachers, as used in this study. The relationship between anxiety towards AI and pre-service teachers’ engagement with AI in STEM education is a crucial aspect that requires exploration. Pre-service teachers who experience anxiety towards AI may be less likely to embrace AI tools in their teaching practices (Chocarro et al., 2023 ; Wang et al., 2021). Therefore, we propose that anxiety towards AI can inversely affect student engagement in the AI program.

AI Readiness

AI readiness refers to the preparedness of pre-service teachers, individuals, organizations, and countries to adopt and utilize AI technologies effectively. It can be seen as the eagerness to use AI technological innovations (Garg & Kumar, 2017 ). The AI readiness of pre-service teachers in STEM programs demonstrates their willingness to use AI as an instructional resource. AI readiness entails technical proficiency and a proactive attitude towards incorporating AI technologies into instruction. It necessitates knowledge of AI-driven systems and a dedication to remaining current on AI breakthroughs. Educators who are well-prepared for the AI-infused future can exploit AI’s potential (Hsu et al., 2019 ) to improve STEM instruction, adapt to changing educational demands, and give students creative and individualized learning experiences. Several studies have explored AI readiness in different contexts. Xuan et al. ( 2023 ) conducted a survey to evaluate medical AI readiness among undergraduate medical students and found that most participants had moderate readiness. Palade and Carutasu ( 2021 ) emphasized the need for organizations to adopt AI technologies to keep up with innovation. They suggested that AI readiness adoption can be normalized under an existing model for digitization. Baguma et al. ( 2023 ) proposed an AI readiness index specifically tailored to the needs of African countries, highlighting dimensions such as vision, governance and ethics, digital capacity, and research and development. Taskiran ( 2023 ) reported that an AI course in the nursing curriculum positively affected students’ readiness for medical AI. These studies highlight the importance of assessing and enhancing AI readiness in various domains and contexts. Still, a drought of studies focused on the AI readiness of pre-service teachers to engage with STEM programs.

Self-transcendent Goals

Self-transcendent goals involve looking beyond oneself and adopting a larger perspective, including concern for others (Ge & Yang, 2023 ). Self-transcendence is a multifaceted psychological phenomenon that includes acts of kindness, philanthropy, and community service as individuals strive to go beyond their individual needs and desires to make a positive impact on the lives of others. It has been shown that self-transcendence is linked to mental health and nursing (Haugan et al., 2013 ; Nygren et al., 2005 ), spirituality (Bovero et al., 2023 ; Suliman et al., 2022 ), and performance in learning and motivation (Reeves et al., 2021 ; Yeager et al., 2014 ), social activism (Barton & Hart, 2023 ) among other fields. The self-transcendent aspirations of pre-service teachers in STEM programs encompass their desire to go beyond personal accomplishments (Naftzger, 2018 ) and contribute more significantly to the welfare of society through STEM education. These objectives frequently include instilling a love of STEM in their pupils, promoting diversity and inclusivity, and addressing real-world issues through STEM education (Okundaye et al., 2022 ). Embracing self-transcendent aspirations inspires pre-service teachers to consistently enhance their STEM topic knowledge, pedagogical abilities, and empathy, driving them to become inspirational educators who inspire future generations to engage profoundly with STEM and promote positive social change. With self-transcendence, pre-service teachers are motivated to continuously adapt and evolve their teaching practices, seeking innovative ways to integrate AI tools and resources into their lessons. By embracing the new trend of teaching and learning AI, pre-service teachers are preparing their students for the future and actively shaping the future of education. To the best of our knowledge, few studies (Ge & Yang, 2023 ; Sanusi et al., 2024a , 2024b ; Yeager et al., 2014 ) have been conducted to examine whether pre-service teachers with a self-transcendent goal for engaging AI are more motivated to learn AI.

Confidence in Learning AI

Pre-service teachers’ confidence in learning AI is a significant component of their readiness to integrate AI into STEM education (Roy et al., 2022 ). Confidence here refers to their belief in their ability to effectively learn AI-related knowledge and skills (Lin et al., 2023 ). When pre-service teachers feel confident in their ability to master AI, they are more likely to participate in AI-related professional development, investigate AI applications in their teaching practices, and adapt to the changing educational landscape. Building this confidence through professional development training is critical for equipping pre-service teachers to use AI as a beneficial resource for improving STEM instruction and preparing students for an AI-driven future. This study attempts to validate existing research (Sanusi et al., 2024a , 2024b ) by investigating whether confidence in learning AI influences student engagement in an AI program.

Engagement in AI Learning

Engagement sparks curiosity and motivates individuals to actively participate in and absorb new information. When learners are engaged, they are more likely to ask questions, seek additional resources, and apply the material to their own experiences. According to Martin ( 2012 ), motivation is the basis of engagement, so AI can be used as a tool to engage pre-service teachers in integrated STEM learning and teaching (Kim et al., 2015 ). Exploring engagement in AI learning is essential, as it establishes a relationship between engagement and learning. Since there are indications that students engaged in learning activities benefit from increased learning, it is imperative to explore this relationship. This investigation is crucial because AI learning is a new initiative, and strategies must be examined to effectively communicate the concepts to students and teachers. Based on the description by Fredricks et al. ( 2004 ), engagement is a multidimensional construct that encompasses behavior, emotion, and cognition. We will briefly describe each engagement type (in relation to AI learning) highlighted below.

Cognitive Engagement—Critical Thinking: Cognitive (Looking at the Focused Effort Students Give to What Is Being Taught)

Learning and mastering artificial intelligence (AI) require critical thinking (Benvenuti et al., 2023 ), particularly in cognitive engagement. The CE details how students process information (Schnitzler et al., 2021 ). AI requires deep cognitive engagement from learners because of its complex algorithms (Jaiswal & Arun, 2021 ), diverse applications, and ethical implications. Critical thinking in this context involves analyzing data sources for potential biases, evaluating the ethical implications of AI decisions, and challenging the assumptions that underpin AI decisions. Additionally, it requires learners to explore and evaluate different approaches and methods to solve real-world problems using AI techniques. Developing critical thinking skills with cognitive engagement helps individuals understand AI concepts and provides them with the tools to innovate effectively and navigate the rapidly changing AI landscape. In addition, cognitive engagement through critical thinking catalyzes innovation in the fast-expanding field of AI. Cognitive engagement and critical thinking are important aspects of pre-service teachers’ engagement in STEM education. Research has shown that active engagement in STEM education leads to higher-order thinking skills, increased motivation, and improved learning outcomes (Kamarrudin et al., 2023 ). In STEM education, pre-service teachers employ cognitive engagement via critical thinking skills to successfully teach STEM and achieve meaningful learning experiences for their students (HacioĞLu, 2021 ). Recently, Yıldız-Feyzioğlu and Kıran ( 2022 ) showed that collaborative group investigation (CGI) learning and self-efficacy have also been found to positively impact the critical thinking skills of pre-service science teachers. Therefore, cognitive engagement and critical thinking play a crucial role in pre-service teachers’ engagement in STEM education, leading to improved learning outcomes and the development of effective instructional strategies.

Cognitive Engagement—Creativity

Cognitive engagement via creativity is a dynamic and necessary part of learning AI. While AI is founded on mathematical and computational concepts, encouraging creativity in AI education is crucial for several reasons (Lin et al., 2023 ). Creativity enables students to conceive unique AI applications, leading to novel healthcare, economics, and entertainment solutions. Cognitive engagement for pre-service teachers in STEM education involves their continuous intellectual involvement, the design of stimulating instructional strategies, effective use of technology, and the promotion of a growth mindset (Kim et al., 2015 ). These cognitive aspects contribute to a dynamic and enriching STEM learning experience, preparing students to think critically, adapt to new challenges, and thrive in a knowledge-based society. Patar ( 2023 ) reveals that active engagement activities, such as exploration, sharing knowledge, and assessment, can enhance pre-service teachers’ cognitive engagement. Pre-service teachers should champion the integration of digital tools and resources to enhance the learning experience, providing students with opportunities to explore, experiment, and apply their cognitive skills in a technology-driven world. This integration also supports the development of digital literacy skills, which is essential for successful STEM disciplines. Whether cognitive engagement through creative thinking will significantly affect pre-service teachers in STEM education remains to be investigated.

Behavioral Engagement—Self-directed Learning: Behavioral (Measuring Attendance and Participation)

Behavior engagement refers to measuring academic performance and participation in educational activities (Bowden et al., 2021 ). It is critical to understand the discipline of AI, particularly in the context of self-directed learning (Nazari et al., 2021 ). Pre-service teachers must consider behavioral engagement as an important aspect of STEM education. When pre-service teachers actively engage students in hands-on activities, discussions, and problem-solving tasks, students are more likely to understand STEM concepts better. However, taking the initiative indicates a high level of behavioral engagement (Kim et al., 2015 ). STEM education differs from conventional teaching, which treats students as passive listeners. To implement STEM innovations in the classroom, teachers must design inquiry activities and learning contexts to engage students in authentic problem-solving (Dong et al., 2019 ). Kim et al. ( 2015 ) found that using technology (robotics) significantly impacted students’ behavioral engagement. Thus, this study supports behavioral engagement in STEM education for pre-service teachers.

Social Engagement

Social interaction can be referred to as social interaction, which is an essential component of learning (Okita, 2012 ). It entails working with peers, experts, and AI communities to exchange ideas, share knowledge, and get diverse viewpoints, ultimately improving the learning experience and driving creativity. Social engagement for pre-service teachers in STEM education involves building positive relationships within the school community, integrating collaborative learning experiences, actively participating in professional networks, and instilling a sense of social responsibility in students. These social aspects contribute to a holistic STEM education experience, fostering a collaborative and purpose-driven approach that prepares students for success in both academic and real-world STEM contexts. Ishmuradova et al. ( 2023 ) reported that pre-service science teachers have shown high awareness of social responsibility in human welfare, safety, and a sustainable environment. However, their awareness related to practice and participation is relatively low. To our knowledge, there is apparently no study on social engagement among pre-service teachers in STEM education.

Research Hypotheses

H1: Attitude towards AI will significantly positively influence student engagement in the AI program.

H2: Anxiety towards AI will significantly negatively influence student engagement in the AI program.

H3: AI readiness will significantly positively influence student engagement in the AI program.

H4: Self-transcendent goals will significantly positively influence student engagement in the AI program.

H5: Confidence in learning AI will significantly positively influence student engagement in the AI program.

Methodology

Research context and participants.

This study was conducted at a public university of education in Ghana, specifically focusing on the students enrolled in the Information Communication and Technology (ICT) Education program. It is important to note that the student teachers had not completed any courses in AI. As shown in Table  1 , 35 pre-service teachers participated in our research, with a majority being male and aged between 19 and 25 years. Most of the participants (57.1%) were in their second year of the teacher training program. For this research, we utilized a simple random sampling approach. We extended an invitation to all the students in the ICT department to participate in our study, and their involvement was based on informed consent. We also assured the participants of their anonymity and the ability to withdraw from the project at any time.

Data Collection Procedure

The data utilized for this study was gathered through an online survey shortly after a 4-week AI short course program organized between September and October 2022. The course was designed to expose pre-service teachers to AI knowledge and its ethical implications. The program is designed as an intervention of 2 h 30 min weekly, including assignments, and comprises four different learning sessions and five different topics. The topics include Introduction to AI and Ethical Dilemmas, Image Recognition, Algorithms and Bias, Convolution Neural Networks, k-Nearest Neighbor, and Decision Trees. We used different plugged and unplugged activities to demystify the topics to the study participants (Ma et al., 2023 ). We used AI tools like Google Teachable Machine (plugged activities) during the learning session, including a series of paper-based activities (unplugged) that support collaborative learning (Frimpong, Sanusi, Ayanwale, et al., n.d) After the sessions, the pre-service teachers filled out a survey to gather their perspectives about their learning.

Instrumentation

Our research instrument was adapted from different sources in the research literature (see “Appendix”). We modified some terms slightly to fit our research context. We adapted the items for engagement from the studies of Bowden et al. ( 2021 ), Reeve and Tseng ( 2011 ), and Sun et al. ( 2019 ). Confidence in learning AI scale was adapted from Xia et al. ( 2022 ). Finally, the scales for attitude towards AI, anxiety towards AI, AI readiness, and self-transcendent goals were derived from the study of Sanusi et al. ( 2023 ). A 6-point Likert scale ranging from “strongly disagree” to “strongly agree” was used to retrieve all the items’ responses. We decided to use a 6-point Likert scale since it provides opportunities for more choice and may measure the participants’ evaluation more accurately (Taherdoost, 2019 ).

Analytical Approach

In this study, we employed a variance-based structural equation modeling (VB-SEM) approach to assess our proposed model. This methodology allowed us to estimate both the measurement and structural models simultaneously. We chose VB-SEM over covariance-based structural equation modeling (CB-SEM) due to its suitability for our study’s specific characteristics. These include dealing with small sample sizes, not having strict distribution requirements for the data, explaining variance, and managing a complex hierarchical component model. This complexity is evident in our study, which focuses on student engagement in the AI program (Benitez et al., 2020 ; Hair et al., 2014 ). To conduct our data analysis, we utilized SmartPLS software version 4.0.9.6 (Ringle et al., 2022 ). More so, various parameters were considered when estimating our model in partial least squares (PLS), including the use of the path weighting scheme as the estimation method, raw data for data metric, and default settings of the initial weight PLS-SEM algorithm (Hair et al., 2017 ). To validate our model, we employed the two-stage disjoint approach for higher-order constructs (Sarstedt et al., 2019 ) since the variable “engagement” is indeed a higher-order construct consisting of four lower-order constructs: cognitive engagement—critical thinking (CECT), cognitive engagement—creativity (CEC), behavioral engagement—self-directed learning (BESL), and social engagement (SOE).

In addition, our analysis process involved assessing the goodness of model fit for the measurement model, which was based on the saturated model, and for the structural model, which was based on the estimated model. We evaluated these models using various parameters, including the standardized residual mean square root (SRMR) and other fit indices like normed fit index (NFI), the distance of unweighted least squares (d ULS ), and the geodesic distance (d G ) to ensure adequate model fit (Benitez et al., 2020 ; Hair et al., 2017 ). In the evaluation of the measurement model, both first- and second-order constructs were examined for reliability and validity, looking at factors such as item factor loadings (FL ≥ 0.60), construct reliability (i.e., Cronbach alpha and composite reliability indices—CA ≥ 0.70; CR ≥ 0.70), convergent validity (average variance extracted—AVE ≥ 0.5), and discriminant validity (i.e., heterotrait-monotrait correlation—HTMT < 0.85 or HTMT < 0.90) (Ayanwale & Ndlovu, 2024 ; Hair et al., 2017, 2019, 2022; Henseler et al., 2015 ; Ringle et al., 2023 ; Sarstedt et al., 2019 ). Items with factor loadings below 0.60 and constructs with average variance extracted (AVE) below 0.50 were removed, and the models were subsequently refined. To test the hypotheses proposed in our study, we analyzed the relationships between constructs in the structural model using bootstrapping with 10,000 subsamples in PLS. We assessed the magnitude and statistical significance of direct effects to understand the relative importance of constructs in explaining others in the structural model (Amusa & Ayanwale, 2021 ; Hair et al., 2018 ; Hock et al., 2010 ; Ringle & Sarstedt, 2016 ). We also estimated the predictive power within the sample using the coefficient of determination ( R 2 ), which should exceed 0.1 ( R 2  > 0.1), and the predictive power outside the sample through the PLSpredict ( Q 2 predict ) obtained by comparing the RMSE (root mean square error) or MAE (mean absolute error) values of all the indicators in the PLS-SEM analysis to those of the LM (linear model) benchmark. When most of these indicators yield lower RMSE or MAE values than the LM benchmark, it demonstrates a moderate level of predictive power. On the other hand, if only a minority of the indicators exhibit lower prediction errors compared to the LM benchmark, the model’s predictive capability is low. If none of the indicators shows lower prediction errors than the LM benchmark, the model lacks predictive power (Sanusi et al., 2023 ; Shmueli & Koppius, 2011 ; Shmueli et al., 2019 ).

This section presents the results of the analysis. Thus, Table  2 evaluates the overall model fit for the measurement and structural models. This analysis indicates that the SRMR value falls below the recommended threshold (SRMR < 0.08), and the SRMR, NFI, d ULS , and d G values are all below the 95% quantile (HI95) of their reference distribution. These findings collectively suggest that the measurement model demonstrates an acceptable fit, and there is empirical evidence supporting the validity of the estimated model (Molefi & Ayanwale, 2023 ; Quintana & Maxwell, 1999 ).

In the measurement model, we conducted an evaluation of reliability and validity for both the lower-order constructs (LOC) and higher-order constructs (HOC). The results, as depicted in Table  3 , indicate that the factor loadings for LOC range from 0.648 to 0.975, composite reliability (CR) values for LOC range from 0.826 to 0.980, Cronbach’s alpha (α) values for LOC range from 0.783 to 0.962, and average variance extracted (AVE) values for LOC range from 0.541 to 0.923. Furthermore, the factor loadings for HOC range from 0.784 to 0.846, with a CR value for HOC of 0.888, a Cronbach’s α value for HOC of 0.834, and an AVE value for HOC of 0.664.

Significantly, all these values surpass the recommended thresholds, signifying that the lower-order and higher-order constructs exhibit strong validity, reliability, and internal consistency. Additionally, we confirmed discriminant validity, as indicated in Table  4 , demonstrating that each reflective construct shows more robust associations with its indicators than any other construct within the PLS path model. In other words, the constructs are distinguishable from one another, with correlation values well below the suggested threshold. This underscores the effectiveness of the measurement model in establishing good discriminant validity (Ayanwale & Oladele, 2021 ; Hair et al., 2022 ).

The findings from the structural model are illustrated in Table  4 and Fig.  2 . Following the results, attitude towards AI has a significant positive effect on student engagement in the AI program ( β  = 0.262, t  = 3.814, p  < 0.05), supporting H1. Anxiety towards AI is found to exert a negative influence on student engagement in the AI program ( β  =  − 0.257, t  =  − 3.438, p  < 0.05), validating H2. AI readiness positively influences student engagement in the AI program ( β  = 0.265, t  = 4.420, p  < 0.05), so H3 is supported. Self-transcendent goals positively impact student engagement in the AI program (β = 0.232, t = 4.171, p < 0.05), thus supporting H4. At the same time, confidence in learning AI is positively associated with student engagement in the AI program ( β  = 0.386, t  = 6.037, p  < 0.05), supporting H5. Attitude towards AI, anxiety towards AI, AI readiness, self-transcendent goals, and confidence in learning AI jointly explain 63.1% of the variance in student engagement in the AI program. Hence, the model’s ability to explain variance within the sample is deemed adequate, as the coefficient of determination ( R 2 ) values surpass the threshold of 0.10 (Ayanwale & Molefi, 2024 ; Falk & Miller, 1992 ; Molefi & Ayanwale, 2023 ).

Structural model result

In addition, the effect size ( f 2 ) was calculated to assess how much removing each exogenous variable from the model influences the model’s ability to explain variance. The f 2 values were interpreted according to Cohen ( 1988 )’s guidelines, which classify effect sizes as small ( f 2  >  = 0.02), medium ( f 2  ≥ 0.15), or large ( f 2  ≥ 0.35). The effect sizes for the different exogenous variables, as shown in Table  5 , revealed that AT ( f 2  = 0.292) had a substantial effect size. This means that removing variable AT from the model would significantly reduce the model’s ability to explain variance. Therefore, variable AT plays a crucial role in explaining variance in the model, and its inclusion is essential for an accurate model. Variable CL ( f 2  = 0.214) also had a notable effect size, indicating its substantial contribution to the model’s explanatory power. Its removal would significantly diminish the model’s capacity to explain variance. Also, AR ( f 2  = 0.179) had a moderate effect size. Removing variable AR would moderately decrease the model’s ability to explain the variance, underlining its importance in the model, and AN ( f 2  = 0.042) and SG ( f 2  = 0.031) had relatively smaller effect sizes. While these variables contribute to the model’s ability to explain the variance, their removal would have a minor impact on its overall performance. Prioritizing and retaining variables AT and CL are crucial to maintaining the model’s accuracy and explanatory power. Although not as influential as AT and CL, variable AR still plays a moderate role in explaining variance and should be retained in the analysis.

Furthermore, when examining the results of Q 2 predict (see Table  6 ), we noticed that all the metrics associated with the endogenous construct (student engagement in the AI program) exhibited lower values for RMSE (root mean square error) and MAE (mean absolute error) in comparison to a simple linear model benchmark that was based on the means of the indicators from the training sample. These metrics yielded Q 2 predict values that exceeded 0. This suggests that the indicators used in our PLS-SEM analysis produced fewer prediction errors when compared to the linear model benchmark, thereby indicating a strong predictive capability for our model.

While previous research has explored constructs such as AT, CL, AR, AN, and SG and their links to behavioral intention in the context of AI and education (Ayanwale et al., 2022 ; Chai et al., 2021, 2020a, 2020b), this study contributes to the existing literature by investigating how these constructs affect pre-service teacher engagement with AI. The novelty of this research lies in its examination of the relationship between these constructs and the engagement of pre-service teachers, addressing a gap in literature. This paper adopts a holistic approach to measuring pre-service teacher engagement in AI programs, which includes four dimensions: cognitive engagement (critical thinking and creativity), behavioral engagement (self-directed learning), and social engagement. Additionally, composite-based structural equation modeling is employed to unravel the intricate interrelationships among student engagement with AI learning, attitude towards AI, anxiety towards AI, self-transcendent goals, AI readiness, and confidence in learning AI.

The findings affirm the validity of all proposed hypotheses (H1–H5) as antecedents to pre-service teachers’ engagement with AI content. Collectively, these constructs account for 63.1% of the observed variance in teachers’ engagement with AI. Among the predictor variables, confidence in learning AI emerges as the most influential predictor of pre-service teachers’ engagement, followed by AI readiness, attitude towards AI, and self-transcendent goals. These findings resonate with the previous research (e.g., Ayanwale, 2023 ; Lin et al., 2023 ; Papadakis et al., 2021 ; Roy et al., 2022 ). Confidence in one’s ability to learn AI and use technology has been a recurring theme in technology adoption literature. Bandura’s theory (1977) underscores the significance of self-efficacy in adopting and effectively using new technologies. Thus, confidence in learning AI plays a pivotal role in driving engagement with AI activities. These findings align with Chen et al. ( 2018 ), which found that undergraduate students’ confidence in their ability to grasp AI significantly predicted their intention to learn AI. Consistent with our findings, Sun et al. ( 2019 ) asserted that confidence, as one of the intrinsic motivation components, significantly predicts students’ engagement in MOOC courses. When students perceive learning in MOOCs as enjoyable and are confident in their abilities, they are more motivated and engaged in their studies. Therefore, it is imperative to prioritize building confidence in pre-service teachers concerning their capacity to learn AI and to create supportive learning environments and practical training to enhance their engagement in AI programs.

As the second most influential variable, AI readiness has been identified as critical in enhancing student engagement in learning AI (Tang & Chen, 2018 ). While existing studies have primarily explored the relationship between AI readiness and intention (Ayanwale et al., 2022 ; Chai et al., 2020a , 2020b ), this study delves into how individuals’ preparedness and willingness to engage with and adapt to AI influence engagement with AI learning materials. It examines whether their comfort level with AI technology contributes to their active involvement in AI-related educational programs, including attendance, coursework engagement, and participation in AI-related projects (Dai et al., 2020 ; Hsu et al., 2019 ; Sun et al., 2019 ). The positive coefficient uncovered in our findings indicates that higher AI readiness positively correlates with increased engagement in learning AI. This suggests that pre-service teachers are more likely to engage in AI-related activities when they feel prepared and willing to embrace AI. Therefore, it emphasizes the importance of adequately preparing pre-service teachers to work with AI. AI readiness is critical in teacher training to enhance engagement and effectiveness in AI education.

In addition, previous research (Ayanwale et al., 2022 ; Kumar & Mantri, 2021 ; Weng et al., 2018 ) has consistently highlighted the significance of one’s attitude in predicting the intention to learn AI. Our study also observes a substantial positive relationship between a positive attitude towards AI and pre-service teacher engagement with AI. This finding aligns with the work of Papadakis et al. ( 2021 ), emphasizing that a positive attitude towards AI promotes its acceptance as a valuable tool for enhancing STEM instruction and increasing engagement. It further corroborates the findings of Kim and Park ( 2019 ), who reported that individuals with more positive attitudes towards AI were more likely to plan the use of AI-based technologies. Ayanwale ( 2023 ) and Ng and Chu ( 2021 ) also underscore the importance of a positive attitude, as students with such an attitude were more inclined to learn AI. Our results indicate that pre-service teachers are more likely to actively participate in AI-related educational activities when they view AI more favorably. This underscores the critical role of instilling positive attitudes and perceptions about AI in teacher training programs, urging educators and institutions to prioritize this aspect to enhance engagement with AI-related content.

We also examine the impact of self-transcendent goals, encompassing objectives beyond personal well-being. Our results reveal a significant positive coefficient, indicating that having self-transcendent goals positively correlates with pre-service teacher engagement in learning AI. This outcome aligns with the findings of Naftzger ( 2018 ) and Okundaye et al. ( 2022 ), who found that pre-service teachers in STEM programs often harbor aspirations to make a broader societal impact, transcending personal accomplishments. In practical terms, their engagement increases when teachers are motivated by goals benefiting their students, including the society. Therefore, emphasizing self-transcendent goals in pre-service teachers may enhance their commitment to AI-related education and its potential impact on students.

In addition to previous studies that explore the relationship between anxiety and intention (Ayanwale et al., 2022 ; Chai et al., 2020a , b ), our study delves into how self-perceived fear and discomfort concerning AI tools affect engagement in AI programs. The results support our hypothesis, showing a negative coefficient, indicating that anxiety towards AI is negatively associated with pre-service teacher engagement in learning AI. This finding resonates with the work of Katsarou (2021) and Kin (2020), which also found a significant negative relationship between anxiety and intention regarding AI. Jones et al. ( 2017 ) also note that apprehension might arise from concerns about technological skills or fears that AI might replace traditional instructional roles. To address this anxiety, pre-service instructors can build confidence in AI tools through training and support. Creating an environment that encourages experimentation and emphasizes AI’s complementary role in improving STEM education is crucial. Reducing anxiety and promoting AI’s beneficial integration is essential for encouraging engagement. While some scholars find anxiety less predictive of behavioral intention, our study suggests that anxiety towards AI significantly impacts pre-service teacher engagement with learning AI. This insight underscores the importance of recognizing and addressing AI-related anxiety among pre-service teachers. It highlights the need for strategies to reduce anxiety and enhance comfort with AI to promote engagement in AI education programs. Notably, while our study specifically targets pre-service teachers, we recognize the importance of exploring how these findings could be replicated across various academic disciplines. By discussing the relevance of our results to broader educational contexts, we provide insights into potential variations that might arise in different settings. This discussion facilitates a more comprehensive understanding of the generalizability and applicability of our findings.

Implication for Practice and Policy

Understanding the factors influencing pre-service teachers’ engagement with AI has significant implications for both educational practices and policy development. Based on this study’s findings, we recommend that educational institutions and policymakers prioritize integrating AI-related content within pre-service teacher education programs. This integration will facilitate the development of essential AI literacy and skills, equipping teachers to incorporate AI technologies into their teaching methods effectively. To ensure a well-rounded and practical approach, schools should offer opportunities for teachers to engage in ongoing professional development focused on AI. Additionally, we emphasize the importance of exposing pre-service teachers to various AI-powered teaching tools and methodologies. This exposure will empower them to create more engaging and personalized learning experiences for their students. Consequently, policies should encourage the adoption of AI tools that can cater to the unique needs of each student, fostering more inclusive and accommodating learning environments.

Furthermore, pre-service teachers must comprehend the ethical implications associated with AI technologies. They should be well-prepared to guide their students in the responsible utilization of AI. Policymakers can contribute by allocating school resources to acquire AI technologies and providing teachers with the necessary tools and training. This includes investments in AI software, hardware, and technical support to ensure teachers can effectively integrate AI into their classrooms. Robust policies should be established to safeguard student data when employing AI tools. Pre-service teachers should be well-versed in data privacy and security measures and adhere to regulations when incorporating AI technologies into their teaching practices.

Promoting cross-disciplinary learning that incorporates AI concepts is also crucial. Pre-service teachers should be primed to teach AI not only as a standalone subject but also as a complementary tool in various disciplines. Policies can foster collaboration among pre-service teachers, experienced educators, and AI experts. Such interactions can yield valuable insights and drive innovation in AI education. Encouraging pre-service teachers to engage in action research to assess the impact of AI on student learning and their teaching practices can be pivotal. This research can inform best practices and contribute to a growing knowledge of AI in education. On the policy front, both policymakers and educators should strive to ensure that AI resources and training are accessible to all, regardless of a student’s socioeconomic background or geographical location. This may entail initiatives aimed at bridging the digital divide and promoting equitable access to AI education. The policy framework should also account for ongoing support and professional development for teachers as AI technologies evolve. Teachers must possess the skills to adapt to changes and stay current with developments in AI in education. Also, our study offers practical recommendations for practitioners. Emphasizing the critical role of building confidence in pre-service teachers, enhancing AI readiness in teacher training, fostering positive attitudes towards AI, and incorporating self-transcendent goals, we provide actionable steps for educators and institutions. These recommendations offer a roadmap for creating supportive learning environments and practical training to enhance pre-service teacher engagement in AI programs.

Limitation and Future Work

Some limitations should be noted despite the valuable results this study generates. First, the selection of study participants is restricted to the ICT education department at a university in Ghana. Hence, it is necessary to consider subjects across different disciplines within the teacher education program as well as other regions to understand students’ engagement from a broader perspective. Second, our sample size may limit the generalizability of our results. Future research should consider a relatively large sample size across different contexts. Third, using only a quantitative approach limits the insight we may generate from students’ explanations during the learning process. To this end, a qualitative or mixed-method approach should be considered for triangulation purposes. Lastly, the AI program in this study spans over a few weeks. Future research should investigate student engagement across an academic session and a longitudinal study of the candidates.

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Ayanwale, M.A., Frimpong, E.K., Opesemowo, O.A.G. et al. Exploring Factors That Support Pre-service Teachers’ Engagement in Learning Artificial Intelligence. Journal for STEM Educ Res (2024). https://doi.org/10.1007/s41979-024-00121-4

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Accepted : 25 March 2024

Published : 12 April 2024

DOI : https://doi.org/10.1007/s41979-024-00121-4

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