Alexandra Jackson, Samantha Brunhaver, Cheryl Bodnar, Sanjeev Kavale, Adam Carberry, Prateek Shekhar
Background
Entrepreneurial mindset (EM) integration within engineering education is used as an approach to prepare students for the complexity of global challenges.
Purpose
The Kern Entrepreneurial Engineering Network (KEEN) 3Cs framework for EM has been used extensively to guide the integration of EM into engineering education. However, the limited literature supporting its components has presented challenges for both educators and researchers in its application and assessment.
Scope/Method
Three scoping reviews were conducted using three databases, with each review corresponding to one of the 3Cs: Curiosity, Creating Value, and Connections. We used detailed search strings for each of the Cs and screened and identified papers based on inclusion and exclusion criteria at both the abstract and full-text levels. The final set of articles were classified using four criteria: Definitions, Frameworks, Instruments, and Theories.
Findings
The study's findings are presented as high-level themes for each C. Curiosity emerged as an intrinsic motivation, a behavior, and a utility. Creating Value was characterized as a process leading to a positive outcome; a value-generating force for individuals, organizations, and society; and a spectrum of value types. Lastly, Connections encompassed those made between elements for a specific purpose, those demonstrated through modality (such as verbal, graphical, or experimental), and collaborations between people.
Conclusions
Our findings indicate that the practitioner-centered 3Cs framework is associated with well-established entrepreneurship theories. With this work, researchers and practitioners can support their 3Cs-based implementations and research studies, which will expand upon the existing scholarship in engineering entrepreneurship.
{"title":"Anchoring the 3Cs: A scoping literature review of curiosity, creating value, and connections in entrepreneurial mindset development","authors":"Alexandra Jackson, Samantha Brunhaver, Cheryl Bodnar, Sanjeev Kavale, Adam Carberry, Prateek Shekhar","doi":"10.1002/jee.70051","DOIUrl":"https://doi.org/10.1002/jee.70051","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Entrepreneurial mindset (EM) integration within engineering education is used as an approach to prepare students for the complexity of global challenges.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>The Kern Entrepreneurial Engineering Network (KEEN) 3Cs framework for EM has been used extensively to guide the integration of EM into engineering education. However, the limited literature supporting its components has presented challenges for both educators and researchers in its application and assessment.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Scope/Method</h3>\u0000 \u0000 <p>Three scoping reviews were conducted using three databases, with each review corresponding to one of the 3Cs: Curiosity, Creating Value, and Connections. We used detailed search strings for each of the Cs and screened and identified papers based on inclusion and exclusion criteria at both the abstract and full-text levels. The final set of articles were classified using four criteria: Definitions, Frameworks, Instruments, and Theories.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Findings</h3>\u0000 \u0000 <p>The study's findings are presented as high-level themes for each C. Curiosity emerged as an intrinsic motivation, a behavior, and a utility. Creating Value was characterized as a process leading to a positive outcome; a value-generating force for individuals, organizations, and society; and a spectrum of value types. Lastly, Connections encompassed those made between elements for a specific purpose, those demonstrated through modality (such as verbal, graphical, or experimental), and collaborations between people.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Our findings indicate that the practitioner-centered 3Cs framework is associated with well-established entrepreneurship theories. With this work, researchers and practitioners can support their 3Cs-based implementations and research studies, which will expand upon the existing scholarship in engineering entrepreneurship.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50206,"journal":{"name":"Journal of Engineering Education","volume":"115 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jee.70051","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146049408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kristen Wendell, Chelsea J. Andrews, Nicole Batrouny, Tejaswini Dalvi, Fatima Rahman, Tyrine Jamella Pangan
Background
Helping elementary students think carefully and collectively about the details of their engineering design prototypes is a shared goal of engineering educators and curriculum developers. Mechanistic reasoning is one kind of thinking that engineering students can share with their design teammates to make sense of a prototype's performance.
Purpose
By connecting existing frameworks from the science education literature to new data from classroom engineering activities, this article illustrates an approach to characterizing the mechanistic reasoning expressed in children's engineering design discourse.
Method
We analyzed third- and fourth-grade students' discussions of their engineering designs-in-progress in two different community-contextualized curriculum units: one on holding back earth materials with retaining walls and the other on building accessible playground structures.
Results
Across these disparate design challenge contexts, we show how elementary students' mechanistic reasoning discourse in engineering involved identifying the target performance of a design, naming entities that matter within a design and its environment, describing entity factors that are relevant to design performance, connecting factors to each other, and linking entities and factors back to the overall performance of the design as a whole.
Conclusions
We argue that attention to these elements of mechanistic reasoning in students' design discourse may help educators responsively support students' analysis of their prototype performance.
{"title":"Recognizing mechanistic reasoning in elementary students' engineering design discourse","authors":"Kristen Wendell, Chelsea J. Andrews, Nicole Batrouny, Tejaswini Dalvi, Fatima Rahman, Tyrine Jamella Pangan","doi":"10.1002/jee.70049","DOIUrl":"https://doi.org/10.1002/jee.70049","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Helping elementary students think carefully and collectively about the details of their engineering design prototypes is a shared goal of engineering educators and curriculum developers. Mechanistic reasoning is one kind of thinking that engineering students can share with their design teammates to make sense of a prototype's performance.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>By connecting existing frameworks from the science education literature to new data from classroom engineering activities, this article illustrates an approach to characterizing the mechanistic reasoning expressed in children's engineering design discourse.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>We analyzed third- and fourth-grade students' discussions of their engineering designs-in-progress in two different community-contextualized curriculum units: one on holding back earth materials with retaining walls and the other on building accessible playground structures.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Across these disparate design challenge contexts, we show how elementary students' mechanistic reasoning discourse in engineering involved <i>identifying the target performance</i> of a design, <i>naming entities</i> that matter within a design and its environment, <i>describing entity factors</i> that are relevant to design performance, <i>connecting factors</i> to each other, and <i>linking</i> entities and factors back to the overall performance of the design as a whole.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>We argue that attention to these elements of mechanistic reasoning in students' design discourse may help educators responsively support students' analysis of their prototype performance.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50206,"journal":{"name":"Journal of Engineering Education","volume":"115 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145969734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Woorin Hwang, Pauline Aguinalde, Kent J. Crippen, Jinnie Shin, Bruce F. Carroll
Background
Work-integrated learning (WIL) is a pedagogical approach combining classroom learning with practical experience. Despite its growing popularity, there is a need for a comprehensive understanding of the characteristics, trends, and outcomes of intervention studies.
Purpose
This systematic review explores the current research on WIL intervention studies, examining publication venues, research aims, methodologies, and outcomes, as well as the implications and challenges.
Design/Method
The structured and predefined PRISMA process was used to identify relevant studies published in peer-reviewed journals and conference proceedings, ensuring rigor, precision, and replicability. A typological framework was developed and applied to extract and categorize data from the included studies, which were then synthesized to identify patterns and trends.
Results
The review included 22 studies between 2014 and 2023. Although the study quality was generally low across journal articles and conference proceedings, the included studies nevertheless provided essential insights. International interest in this topic is evident, growing, and exploratory, with a potential bias toward individual factors and little consideration of institutional or cultural factors. The methodologies included various approaches, but the lack of unifying frameworks suggests that the outcomes are likely context-dependent. The benefits of WIL were numerous and multifaceted, while the challenges highlighted the complexity institutions and industry partners face.
Conclusions
This study provides a comprehensive overview of current research on WIL intervention studies. The findings highlight the importance of theoretical underpinnings, research aims, and methodologies in WIL research. The results have implications for educators, policymakers, and researchers seeking to improve the effectiveness of WIL programs.
{"title":"Systematic review of work-integrated learning in undergraduate engineering education","authors":"Woorin Hwang, Pauline Aguinalde, Kent J. Crippen, Jinnie Shin, Bruce F. Carroll","doi":"10.1002/jee.70050","DOIUrl":"https://doi.org/10.1002/jee.70050","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Work-integrated learning (WIL) is a pedagogical approach combining classroom learning with practical experience. Despite its growing popularity, there is a need for a comprehensive understanding of the characteristics, trends, and outcomes of intervention studies.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This systematic review explores the current research on WIL intervention studies, examining publication venues, research aims, methodologies, and outcomes, as well as the implications and challenges.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Design/Method</h3>\u0000 \u0000 <p>The structured and predefined PRISMA process was used to identify relevant studies published in peer-reviewed journals and conference proceedings, ensuring rigor, precision, and replicability. A typological framework was developed and applied to extract and categorize data from the included studies, which were then synthesized to identify patterns and trends.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The review included 22 studies between 2014 and 2023. Although the study quality was generally low across journal articles and conference proceedings, the included studies nevertheless provided essential insights. International interest in this topic is evident, growing, and exploratory, with a potential bias toward individual factors and little consideration of institutional or cultural factors. The methodologies included various approaches, but the lack of unifying frameworks suggests that the outcomes are likely context-dependent. The benefits of WIL were numerous and multifaceted, while the challenges highlighted the complexity institutions and industry partners face.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This study provides a comprehensive overview of current research on WIL intervention studies. The findings highlight the importance of theoretical underpinnings, research aims, and methodologies in WIL research. The results have implications for educators, policymakers, and researchers seeking to improve the effectiveness of WIL programs.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50206,"journal":{"name":"Journal of Engineering Education","volume":"115 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145964103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dina Verdín, Timothy Wells, Ulises Trujillo Garcia
Background
The ongoing underrepresentation of Latinx students, particularly girls, in engineering calls for new approaches to broaden participation. The present study aims to address this disparity by implementing a culturally responsive gamified engineering activity for migratory high school students, an often-overlooked subset of Latinx youth. The activity integrated principles of culturally responsive instruction and elements of gamification to teach the engineering design process and boost students' engineering interest and self-efficacy beliefs.
Method
A repeated-measures mixed ANOVA was used to analyze pre- and post-survey measures from 144 Latinx migratory high school students who participated in a summer program.
Results
After engaging in the activity, Latinx migratory high school students had a significantly greater interest in engineering, and their overall confidence in their ability to academically excel in engineering increased. Students felt more efficacious in their abilities to tinker with devices (i.e., tinkering self-efficacy) and apply design principles to solve a real-world problem after the activity (i.e., design self-efficacy). Notably, the gender gap in general engineering self-efficacy narrowed considerably, with Latinas demonstrating a larger increase than Latinos. The gender difference in tinkering self-efficacy was nearly eliminated post the activity.
Conclusions
The study highlights the importance of creating culturally situated engineering experiences for Latinx migratory high school students that call on their desire to make a difference in their communities, while also making these opportunities accessible to students who have been invisible in the conversation to broaden participation. Approaches implemented in this study may be particularly effective in addressing gender disparities in engineering.
{"title":"Increasing Latinx migratory high school students' engineering interest and self-efficacy beliefs: A culturally responsive gamified engineering design activity","authors":"Dina Verdín, Timothy Wells, Ulises Trujillo Garcia","doi":"10.1002/jee.70048","DOIUrl":"https://doi.org/10.1002/jee.70048","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The ongoing underrepresentation of Latinx students, particularly girls, in engineering calls for new approaches to broaden participation. The present study aims to address this disparity by implementing a culturally responsive gamified engineering activity for migratory high school students, an often-overlooked subset of Latinx youth. The activity integrated principles of culturally responsive instruction and elements of gamification to teach the engineering design process and boost students' engineering interest and self-efficacy beliefs.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>A repeated-measures mixed ANOVA was used to analyze pre- and post-survey measures from 144 Latinx migratory high school students who participated in a summer program.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>After engaging in the activity, Latinx migratory high school students had a significantly greater interest in engineering, and their overall confidence in their ability to academically excel in engineering increased. Students felt more efficacious in their abilities to tinker with devices (i.e., tinkering self-efficacy) and apply design principles to solve a real-world problem after the activity (i.e., design self-efficacy). Notably, the gender gap in general engineering self-efficacy narrowed considerably, with Latinas demonstrating a larger increase than Latinos. The gender difference in tinkering self-efficacy was nearly eliminated post the activity.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The study highlights the importance of creating culturally situated engineering experiences for Latinx migratory high school students that call on their desire to make a difference in their communities, while also making these opportunities accessible to students who have been invisible in the conversation to broaden participation. Approaches implemented in this study may be particularly effective in addressing gender disparities in engineering.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50206,"journal":{"name":"Journal of Engineering Education","volume":"115 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jee.70048","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145739768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<p>Generative artificial Intelligence (AI) is a disruptive technology that is altering the way we live and work, and disruptions in academia are not the exception. Specifically in academia, generative AI is making its way into research, the classroom, and administrative workflows. For instance, recent work has documented how generative AI can support the creation of systematic literature reviews (Gwon et al., <span>2024</span>; Hossain, <span>2024</span>; Mozelius & Humble, <span>2024</span>; among others) and even automate scientific workflows (e.g., Ghafarollahi & Buehler, 2025). As generative AI becomes increasingly integrated into research processes, it is essential to demonstrate its efficacy and accuracy—while also safeguarding human agency (Watkins & Barak-Medina, 2024)—in mimicking human activities (Miao & Holmes, <span>2023</span>). In tandem, it is equally important to identify what are acceptable and perhaps controversial uses of generative AI in support of research workflows among discipline-specific research communities (Andersen et al., <span>2025</span>; Watkins, <span>2023</span>). For instance, a study identified that Danish researchers from varied disciplines hold both positive and negative views on the use of generative AI in support of research workflows. Although uses in data analysis may be viewed positively, uses for experimental design may be viewed as controversial (Andersen et al., <span>2025</span>). Furthermore, for the case of qualitative analysis, which is common in engineering education research, as you will read in this special issue, researchers may be exposed to a variety of ethical dilemmas, such as data ownership and rights, data privacy and transparency, interpretive sufficiency, and potential biases, among others (Davison et al., <span>2024</span>). As a response to this technological disruption, this special issue contributes new knowledge of how generative AI can be used and is being used in support of engineering education research workflows.</p><p>In May of 2024, we invited the engineering education research community and other STEM education disciplines to contribute manuscripts that showcase ways in which generative AI can be used to support engineering education research workflows. In response, we received 43 extended abstracts with proposals. After an initial review by all three editors, 19 proposals were invited for manuscript submissions, and a total of 13 full manuscripts were received and sent out to peer review. The final collection of seven published manuscripts showcases how generative AI can be used for supporting data collection, data scoring, and data analysis processes, as shown in Figure 1. In each of the contributions, authors were asked to consider the validity and reliability of the AI-generated results, aspects of reproducibility and transparency, and overall benefits and limitations of the use of generative AI. Authors were also highly encouraged to explicitly address
{"title":"Recommendations for the integration of generative artificial intelligence in support of engineering education research workflows","authors":"Alejandra J. Magana, Ryan Watkins, Camilo Vieira","doi":"10.1002/jee.70043","DOIUrl":"https://doi.org/10.1002/jee.70043","url":null,"abstract":"<p>Generative artificial Intelligence (AI) is a disruptive technology that is altering the way we live and work, and disruptions in academia are not the exception. Specifically in academia, generative AI is making its way into research, the classroom, and administrative workflows. For instance, recent work has documented how generative AI can support the creation of systematic literature reviews (Gwon et al., <span>2024</span>; Hossain, <span>2024</span>; Mozelius & Humble, <span>2024</span>; among others) and even automate scientific workflows (e.g., Ghafarollahi & Buehler, 2025). As generative AI becomes increasingly integrated into research processes, it is essential to demonstrate its efficacy and accuracy—while also safeguarding human agency (Watkins & Barak-Medina, 2024)—in mimicking human activities (Miao & Holmes, <span>2023</span>). In tandem, it is equally important to identify what are acceptable and perhaps controversial uses of generative AI in support of research workflows among discipline-specific research communities (Andersen et al., <span>2025</span>; Watkins, <span>2023</span>). For instance, a study identified that Danish researchers from varied disciplines hold both positive and negative views on the use of generative AI in support of research workflows. Although uses in data analysis may be viewed positively, uses for experimental design may be viewed as controversial (Andersen et al., <span>2025</span>). Furthermore, for the case of qualitative analysis, which is common in engineering education research, as you will read in this special issue, researchers may be exposed to a variety of ethical dilemmas, such as data ownership and rights, data privacy and transparency, interpretive sufficiency, and potential biases, among others (Davison et al., <span>2024</span>). As a response to this technological disruption, this special issue contributes new knowledge of how generative AI can be used and is being used in support of engineering education research workflows.</p><p>In May of 2024, we invited the engineering education research community and other STEM education disciplines to contribute manuscripts that showcase ways in which generative AI can be used to support engineering education research workflows. In response, we received 43 extended abstracts with proposals. After an initial review by all three editors, 19 proposals were invited for manuscript submissions, and a total of 13 full manuscripts were received and sent out to peer review. The final collection of seven published manuscripts showcases how generative AI can be used for supporting data collection, data scoring, and data analysis processes, as shown in Figure 1. In each of the contributions, authors were asked to consider the validity and reliability of the AI-generated results, aspects of reproducibility and transparency, and overall benefits and limitations of the use of generative AI. Authors were also highly encouraged to explicitly address ","PeriodicalId":50206,"journal":{"name":"Journal of Engineering Education","volume":"115 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jee.70043","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145686176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Trevion S. Henderson, Allison Godwin, Adam Robert Carberry
<p>London et al. (<span>2021</span>) described engineering education as a “maturing field of scientific inquiry” that lacks the “signposts,” such as theories, techniques, instruments, and methods, characteristic of more mature fields. It is perhaps for this reason that engineering education research has historically relied on instruments developed by scholars outside of engineering education (e.g., psychology, sociology, and science education; Borrego & Streveler, <span>2015</span>; Malmi et al., <span>2018</span>). This history has created a body of extant literature with varying levels of consideration made to the unifying concept of validity and the process of validation as an ongoing argument for the fair use of an instrument with the population under study and in the context in which the study occurs. The sheer quantity of studies having overlooked such considerations can easily suggest to new scholars that the approaches of yesterday are still appropriate for today's engineering education research.</p><p>Douglas and Purzer (<span>2015</span>) brought to the forefront how validity was conceptualized in their editorial written a decade ago. They highlighted the 2014 <i>Standards for Educational and Psychological Testing</i> by the American Psychological Association, American Educational Research Association, and the National Council for Measurement in Education, presented five misconceptions about validity, suggested clarifications to the community to improve assessment-based research, and shared several exemplar assessment instruments for the community to consider. New examples of exemplar assessment instruments have been published in engineering education since, but patterns in publications and submissions wherein authors fail to provide a robust description of the validation process persist. This pattern calls into questions instrument validity, trust in the associated findings, and the ability of scholars to adopt or adapt an instrument for fair use in context for subsequent studies.</p><p>The discussion for this editorial emerged from experiences across the Editorial Board of the <i>Journal of Engineering Education</i> (JEE). Many submissions continue to omit or overlook modern definitions of validity; in particular, they fail to engage in validation as both a <i>process</i> and an <i>argument</i>. The result is major revisions or rejections for otherwise promising submissions. As an author team, we represent different faculty ranks and roles within JEE, including assistant professor and Assistant Editor, Trevion Henderson; associate professor and Associate Editor, Allison Godwin; and professor, department chair, and Deputy Editor, Adam Carberry. Each of us has experience with instrument development, the editorial process for shepherding manuscripts through the review process, and publishing our own work in this space. We acknowledge that the discussion provided reflects our positionality as US-based scholars and that there are other
{"title":"Supporting fair use in context of instruments in engineering education research: A renewed conversation about validity","authors":"Trevion S. Henderson, Allison Godwin, Adam Robert Carberry","doi":"10.1002/jee.70041","DOIUrl":"https://doi.org/10.1002/jee.70041","url":null,"abstract":"<p>London et al. (<span>2021</span>) described engineering education as a “maturing field of scientific inquiry” that lacks the “signposts,” such as theories, techniques, instruments, and methods, characteristic of more mature fields. It is perhaps for this reason that engineering education research has historically relied on instruments developed by scholars outside of engineering education (e.g., psychology, sociology, and science education; Borrego & Streveler, <span>2015</span>; Malmi et al., <span>2018</span>). This history has created a body of extant literature with varying levels of consideration made to the unifying concept of validity and the process of validation as an ongoing argument for the fair use of an instrument with the population under study and in the context in which the study occurs. The sheer quantity of studies having overlooked such considerations can easily suggest to new scholars that the approaches of yesterday are still appropriate for today's engineering education research.</p><p>Douglas and Purzer (<span>2015</span>) brought to the forefront how validity was conceptualized in their editorial written a decade ago. They highlighted the 2014 <i>Standards for Educational and Psychological Testing</i> by the American Psychological Association, American Educational Research Association, and the National Council for Measurement in Education, presented five misconceptions about validity, suggested clarifications to the community to improve assessment-based research, and shared several exemplar assessment instruments for the community to consider. New examples of exemplar assessment instruments have been published in engineering education since, but patterns in publications and submissions wherein authors fail to provide a robust description of the validation process persist. This pattern calls into questions instrument validity, trust in the associated findings, and the ability of scholars to adopt or adapt an instrument for fair use in context for subsequent studies.</p><p>The discussion for this editorial emerged from experiences across the Editorial Board of the <i>Journal of Engineering Education</i> (JEE). Many submissions continue to omit or overlook modern definitions of validity; in particular, they fail to engage in validation as both a <i>process</i> and an <i>argument</i>. The result is major revisions or rejections for otherwise promising submissions. As an author team, we represent different faculty ranks and roles within JEE, including assistant professor and Assistant Editor, Trevion Henderson; associate professor and Associate Editor, Allison Godwin; and professor, department chair, and Deputy Editor, Adam Carberry. Each of us has experience with instrument development, the editorial process for shepherding manuscripts through the review process, and publishing our own work in this space. We acknowledge that the discussion provided reflects our positionality as US-based scholars and that there are other","PeriodicalId":50206,"journal":{"name":"Journal of Engineering Education","volume":"115 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jee.70041","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145686177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jeanne Sanders, John Mobley IV, Isabel Miller, Nicola W. Sochacka, Paul A. Jensen, Karin J. Jensen
Background
Generative artificial intelligence (AI) large-language models (LLMs) have significant potential as research tools. However, the broader implications of using these tools are still emerging. Few studies have explored using LLMs to generate data for qualitative engineering education research.
Purpose/Hypothesis
We explore the following questions: (i) What are the affordances and limitations of using LLMs to generate qualitative data in engineering education, and (ii) in what ways might these data reproduce and reinforce dominant cultural narratives in engineering education, including narratives of high stress?
Design/Methods
We analyzed similarities and differences between LLM-generated conversational data (ChatGPT) and qualitative interviews with engineering faculty and undergraduate engineering students from multiple institutions. We identified patterns, affordances, limitations, and underlying biases in generated data.
Results
LLM-generated content contained similar responses to interview content. Varying the prompt persona (e.g., demographic information) increased the response variety. When prompted for ways to decrease stress in engineering education, LLM responses more readily described opportunities for structural change, while participants' responses more often described personal changes. LLM data more frequently stereotyped a response than participants did, meaning that LLM responses lacked the nuance and variation that naturally occurs in interviews.
Conclusions
LLMs may be a useful tool in brainstorming, for example, during protocol development and refinement. However, the bias present in the data indicates that care must be taken when engaging with LLMs to generate data. Specially trained LLMs that are based only on data from engineering education hold promise for future research.
{"title":"Affordances and limitations of using large language models to generate qualitative data about mental health perceptions in engineering","authors":"Jeanne Sanders, John Mobley IV, Isabel Miller, Nicola W. Sochacka, Paul A. Jensen, Karin J. Jensen","doi":"10.1002/jee.70037","DOIUrl":"https://doi.org/10.1002/jee.70037","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Generative artificial intelligence (AI) large-language models (LLMs) have significant potential as research tools. However, the broader implications of using these tools are still emerging. Few studies have explored using LLMs to generate data for qualitative engineering education research.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose/Hypothesis</h3>\u0000 \u0000 <p>We explore the following questions: (i) What are the affordances and limitations of using LLMs to generate qualitative data in engineering education, and (ii) in what ways might these data reproduce and reinforce dominant cultural narratives in engineering education, including narratives of high stress?</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Design/Methods</h3>\u0000 \u0000 <p>We analyzed similarities and differences between LLM-generated conversational data (ChatGPT) and qualitative interviews with engineering faculty and undergraduate engineering students from multiple institutions. We identified patterns, affordances, limitations, and underlying biases in generated data.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>LLM-generated content contained similar responses to interview content. Varying the prompt persona (e.g., demographic information) increased the response variety. When prompted for ways to decrease stress in engineering education, LLM responses more readily described opportunities for structural change, while participants' responses more often described personal changes. LLM data more frequently stereotyped a response than participants did, meaning that LLM responses lacked the nuance and variation that naturally occurs in interviews.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>LLMs may be a useful tool in brainstorming, for example, during protocol development and refinement. However, the bias present in the data indicates that care must be taken when engaging with LLMs to generate data. Specially trained LLMs that are based only on data from engineering education hold promise for future research.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50206,"journal":{"name":"Journal of Engineering Education","volume":"115 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jee.70037","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145695211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
There is increasing focus on developing resilient engineering students, this being linked to the perceived difficulty of degrees and the rate of technological change. Simultaneously, there continue to be issues with diversity and retention, and concerns regarding student mental health, both of which have been linked to resilience. Despite this, beliefs that engineering students hold about resilience and what it means to be resilient remain largely unexamined.
Purpose/Hypothesis
We explore the role of underlying structures and contextual factors in shaping engineering students' perceptions and experience of resilience within engineering education and practice.
Design/Method
We conducted semi-structured interviews with 23 engineering students in a UK-based institution. Transcripts were analyzed using reflexive thematic analysis (RTA).
Results
Students recognized resilience as important within engineering education and practice, but highlighted the detrimental impact that a focus on resilience can have on mental health. Whether resilience was necessary, as well as what constituted a resilient response, appeared to vary depending on the situation and required some level of judgment. Students appeared to believe that the university environment provided the adversity required to develop resilience but played a less significant role in providing the support necessary for positive adaptation.
Conclusions
This work acts to surface implications of situating resilience as a desirable student or graduate attribute without consideration for wider structural inequalities. Failing to expose assumptions regarding the situations which require a resilient response risks further perpetuating the very issues around diversity, retention, and progression, which the development of resilience has been proposed to overcome.
{"title":"How do engineering students understand and conceptualize individual resilience?","authors":"Natalie Wint, Inês Direito","doi":"10.1002/jee.70047","DOIUrl":"https://doi.org/10.1002/jee.70047","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>There is increasing focus on developing resilient engineering students, this being linked to the perceived difficulty of degrees and the rate of technological change. Simultaneously, there continue to be issues with diversity and retention, and concerns regarding student mental health, both of which have been linked to resilience. Despite this, beliefs that engineering students hold about resilience and what it means to be resilient remain largely unexamined.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose/Hypothesis</h3>\u0000 \u0000 <p>We explore the role of underlying structures and contextual factors in shaping engineering students' perceptions and experience of resilience within engineering education and practice.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Design/Method</h3>\u0000 \u0000 <p>We conducted semi-structured interviews with 23 engineering students in a UK-based institution. Transcripts were analyzed using reflexive thematic analysis (RTA).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Students recognized resilience as important within engineering education and practice, but highlighted the detrimental impact that a focus on resilience can have on mental health. Whether resilience was necessary, as well as what constituted a resilient response, appeared to vary depending on the situation and required some level of judgment. Students appeared to believe that the university environment provided the adversity required to develop resilience but played a less significant role in providing the support necessary for positive adaptation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This work acts to surface implications of situating resilience as a desirable student or graduate attribute without consideration for wider structural inequalities. Failing to expose assumptions regarding the situations which require a resilient response risks further perpetuating the very issues around diversity, retention, and progression, which the development of resilience has been proposed to overcome.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50206,"journal":{"name":"Journal of Engineering Education","volume":"115 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jee.70047","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145619010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Anne M. McAlister, Juan C. Garibay, Lindsay Wheeler, Jessica L. McDermott
Background
There exists a misconception that social justice does not have a role in engineering. Engineering identity frameworks typically do not integrate concerns for social justice. However, supporting engineering students to incorporate social justice into their engineering identity might empower them to effect social change.
Purpose
We examined undergraduate engineering students' engineering identity, social identity, and identification with social justice, and the relationships between these identities, with the goal of empowering students to engage in social justice as it aligns with their views of themselves and engineering.
Method
We explored counternarratives of four undergraduate engineering students with multiple marginalized social identities after participating in an engineering course focused on social justice via artifacts of coursework and transcripts of semi-structured interviews.
Results
The participants shared how their marginalized social identities made it challenging to see themselves as an engineer. Participants criticized the role of engineering in social oppression and described how separating technical and social aspects of engineering reinforces racism within and through engineering. Social justice was fundamental to participants' views of themselves in relation to engineering, pointing to the existence of a “social justice engineering identity.” All participants believed that engineers need to learn both technical and social aspects of engineering.
Conclusions
Sociotechnical integration is essential in engineering, as devaluing social aspects of engineering does not support students who cannot ignore social justice issues. Further, opportunities to engage in such sociotechnical thinking help students to develop a “social justice engineering identity,” and may support more students to see themselves as engineers.
{"title":"Undergraduate engineering students' perspectives on the intersection of engineering identity, marginalized social identities, and identification with social justice","authors":"Anne M. McAlister, Juan C. Garibay, Lindsay Wheeler, Jessica L. McDermott","doi":"10.1002/jee.70045","DOIUrl":"https://doi.org/10.1002/jee.70045","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>There exists a misconception that social justice does not have a role in engineering. Engineering identity frameworks typically do not integrate concerns for social justice. However, supporting engineering students to incorporate social justice into their engineering identity might empower them to effect social change.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>We examined undergraduate engineering students' engineering identity, social identity, and identification with social justice, and the relationships between these identities, with the goal of empowering students to engage in social justice as it aligns with their views of themselves and engineering.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>We explored counternarratives of four undergraduate engineering students with multiple marginalized social identities after participating in an engineering course focused on social justice via artifacts of coursework and transcripts of semi-structured interviews.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The participants shared how their marginalized social identities made it challenging to see themselves as an engineer. Participants criticized the role of engineering in social oppression and described how separating technical and social aspects of engineering reinforces racism within and through engineering. Social justice was fundamental to participants' views of themselves in relation to engineering, pointing to the existence of a “social justice engineering identity.” All participants believed that engineers need to learn both technical and social aspects of engineering.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Sociotechnical integration is essential in engineering, as devaluing social aspects of engineering does not support students who cannot ignore social justice issues. Further, opportunities to engage in such sociotechnical thinking help students to develop a “social justice engineering identity,” and may support more students to see themselves as engineers.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50206,"journal":{"name":"Journal of Engineering Education","volume":"115 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jee.70045","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145581305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Graduate-level education is gaining attention in engineering education scholarship. While “socialization” is a key term in doctoral literature, little is known about how socialization occurs over time. One common assumption asserts that socialization increases over time, encompassing factors such as belongingness, research ability, and advisor relationship as students acclimate to the norms and values of their advisors, departments, universities, and disciplines. We investigate engineering doctoral student socialization trends: students likely to complete their degrees and those who have questioned whether to persist in their programs. Understanding these trends is essential, as many students consider leaving their programs.
Purpose/Hypothesis
This paper aims to understand how socialization processes occur over several years in engineering students who questioned leaving their PhD programs.
Design/Method
We present longitudinal survey data collected from two cohorts (NA = 113 and NB = 355) of engineering doctoral students at R1 universities in the United States. Data were collected over 2 years through SMS surveys with participants receiving text messages three times per week. We analyzed data using descriptive and time series analysis methods.
Results
Both cohorts showed lower levels of belongingness over time, reported declining advisor relationships, and experienced higher levels of stress. Students later in their programs also reported deteriorating overall social relationships. These findings contradict canonical socialization theory, which expects socialization to naturally improve over time.
Conclusion
While many assume socialization occurs passively and students acculturate into their department and research team over time, our results show students who question whether to persist are de-socializing from graduate school.
研究生阶段的教育是工程教育学术界关注的热点。虽然“社会化”是博士文献中的一个关键术语，但人们对社会化是如何随着时间的推移而发生的知之甚少。一种常见的假设认为社会化会随着时间的推移而增加，包括归属感、研究能力和导师关系等因素，因为学生会适应他们的导师、院系、大学和学科的规范和价值观。我们调查了工程博士生的社会化趋势：可能完成学位的学生和那些质疑是否坚持他们的课程的学生。了解这些趋势是至关重要的，因为许多学生考虑离开他们的专业。目的/假设本论文旨在了解社会化过程是如何发生在那些质疑离开博士课程的工科学生身上的。设计/方法本研究收集了来自美国R1所大学工程博士研究生的两组纵向调查数据（NA = 113, NB = 355）。数据是在两年多的时间里通过短信调查收集的，参与者每周收到三次短信。我们使用描述性和时间序列分析方法分析数据。结果随着时间的推移，两组人的归属感水平都有所下降，与导师的关系也有所下降，压力水平也有所提高。在课程后期学习的学生也报告了整体社会关系的恶化。这些发现与规范的社会化理论相矛盾，该理论认为社会化会随着时间的推移而自然改善。虽然许多人认为社会化是被动的，学生随着时间的推移会适应他们的部门和研究团队，但我们的研究结果表明，质疑是否坚持的学生在研究生院是去社会化的。
{"title":"Witnessing “De-Socialization”: Investigating unexpected longitudinal trends in engineering doctoral socialization","authors":"Kyeonghun Jwa, Catherine G. P. Berdanier","doi":"10.1002/jee.70040","DOIUrl":"https://doi.org/10.1002/jee.70040","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Graduate-level education is gaining attention in engineering education scholarship. While “socialization” is a key term in doctoral literature, little is known about how socialization occurs over time. One common assumption asserts that socialization increases over time, encompassing factors such as belongingness, research ability, and advisor relationship as students acclimate to the norms and values of their advisors, departments, universities, and disciplines. We investigate engineering doctoral student socialization trends: students likely to complete their degrees and those who have questioned whether to persist in their programs. Understanding these trends is essential, as many students consider leaving their programs.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose/Hypothesis</h3>\u0000 \u0000 <p>This paper aims to understand how socialization processes occur over several years in engineering students who questioned leaving their PhD programs.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Design/Method</h3>\u0000 \u0000 <p>We present longitudinal survey data collected from two cohorts (<i>N</i><sub>A</sub> = 113 and <i>N</i><sub>B</sub> = 355) of engineering doctoral students at R1 universities in the United States. Data were collected over 2 years through SMS surveys with participants receiving text messages three times per week. We analyzed data using descriptive and time series analysis methods.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Both cohorts showed lower levels of belongingness over time, reported declining advisor relationships, and experienced higher levels of stress. Students later in their programs also reported deteriorating overall social relationships. These findings contradict canonical socialization theory, which expects socialization to naturally improve over time.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>While many assume socialization occurs passively and students acculturate into their department and research team over time, our results show students who question whether to persist are de-socializing from graduate school.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50206,"journal":{"name":"Journal of Engineering Education","volume":"115 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jee.70040","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145572511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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