Computer Applications in Engineering Education最新文献

{"title":"Research on the Concrete Teaching of Abstract Theories in the “Fundamentals of Mechanical Engineering Control” Course Empowered by Generative Artificial Intelligence","authors":"Teng Hu,&nbsp;Yue Wang,&nbsp;Jiaxin Wang","doi":"10.1002/cae.70184","DOIUrl":"https://doi.org/10.1002/cae.70184","url":null,"abstract":"<div>\u0000 \u0000 <p>The modules on “System Stability Criteria” and “Frequency Domain Characteristics” within the “Fundamentals of Mechanical Engineering Control” course constitute essential theoretical components. However, their highly abstract mathematical nature and disconnect from physical intuition present considerable teaching challenges. This study examines the potential of Generative Artificial Intelligence (GAI) to facilitate concrete representation of abstract theories in engineering education. Grounded in cognitive load theory and the SAMR model, this research systematically analyzes key pedagogical obstacles—including the separation between mathematical derivations and physical significance, the abstraction of graphical logic, and insufficient engineering case studies—and harnesses GAI's capabilities in text analysis, dynamic visualization, and case generation to develop an innovative pedagogical framework. Using “System Stability Criteria” and “Frequency Domain Characteristics” as primary examples, this paper demonstrates the integration of text generation models (such as ChatGPT), image and dynamic visualization tools (including Midjourney and Stable Diffusion), and code generation models (like GitHub Copilot) to transform abstract theories into intuitive, interactive learning experiences. Through the design and classroom implementation of this GAI-enhanced pedagogical framework, its feasibility and perceived utility are evaluated. Qualitative feedback from students and instructor observations indicate that the framework aids in reducing cognitive barriers, strengthening connections between theoretical concepts and engineering applications, and fostering more engaging learning experiences. This study offers a proof-of-concept, theoretical insights and practical guidance for reforming the teaching of “Fundamentals of Mechanical Engineering Control” while contributing novel perspectives on GAI's role in the digital transformation of engineering education.</p>\u0000 </div>","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":"34 3","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147683503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on the Concrete Teaching of Abstract Theories in the “Fundamentals of Mechanical Engineering Control” Course Empowered by Generative Artificial Intelligence","authors":"Teng Hu,&nbsp;Yue Wang,&nbsp;Jiaxin Wang","doi":"10.1002/cae.70184","DOIUrl":"https://doi.org/10.1002/cae.70184","url":null,"abstract":"<div>\u0000 \u0000 <p>The modules on “System Stability Criteria” and “Frequency Domain Characteristics” within the “Fundamentals of Mechanical Engineering Control” course constitute essential theoretical components. However, their highly abstract mathematical nature and disconnect from physical intuition present considerable teaching challenges. This study examines the potential of Generative Artificial Intelligence (GAI) to facilitate concrete representation of abstract theories in engineering education. Grounded in cognitive load theory and the SAMR model, this research systematically analyzes key pedagogical obstacles—including the separation between mathematical derivations and physical significance, the abstraction of graphical logic, and insufficient engineering case studies—and harnesses GAI's capabilities in text analysis, dynamic visualization, and case generation to develop an innovative pedagogical framework. Using “System Stability Criteria” and “Frequency Domain Characteristics” as primary examples, this paper demonstrates the integration of text generation models (such as ChatGPT), image and dynamic visualization tools (including Midjourney and Stable Diffusion), and code generation models (like GitHub Copilot) to transform abstract theories into intuitive, interactive learning experiences. Through the design and classroom implementation of this GAI-enhanced pedagogical framework, its feasibility and perceived utility are evaluated. Qualitative feedback from students and instructor observations indicate that the framework aids in reducing cognitive barriers, strengthening connections between theoretical concepts and engineering applications, and fostering more engaging learning experiences. This study offers a proof-of-concept, theoretical insights and practical guidance for reforming the teaching of “Fundamentals of Mechanical Engineering Control” while contributing novel perspectives on GAI's role in the digital transformation of engineering education.</p>\u0000 </div>","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":"34 3","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147683418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence and Responsible Adoption in Engineering Education: Evidence, Concerns, and a Constructive Path Forward","authors":"Magdy F. Iskander,&nbsp;Alejandra J. Magana","doi":"10.1002/cae.70186","DOIUrl":"https://doi.org/10.1002/cae.70186","url":null,"abstract":"&lt;p&gt;The rapid integration of generative artificial intelligence (AI) into educational practice has generated both enthusiasm and apprehension. For &lt;i&gt;Computer Applications in Engineering Education&lt;/i&gt; (CAE), a journal founded on the premise that computational technologies can enhance learning effectiveness, the present moment represents not a disruption of mission, but an inflection point. Among the most frequently expressed concerns is academic integrity, and consequently the potential erosion of critical thinking skills. Has generative AI fundamentally increased cheating, or has it primarily transformed the mechanisms through which academic misconduct may occur?&lt;/p&gt;&lt;p&gt;A balanced examination of available evidence suggests a more nuanced picture than public discourse often conveys.&lt;/p&gt;&lt;p&gt;Recent survey data confirm that generative AI use among students is widespread. The Higher Education Policy Institute [&lt;span&gt;1&lt;/span&gt;] reports that over 90% of surveyed UK students use generative AI tools for academic purposes. Similarly, the College Board [&lt;span&gt;2&lt;/span&gt;] reports that more than 80% of US high school students use generative AI for school-related work. Even adult learners have reported using AI for academic work [&lt;span&gt;3&lt;/span&gt;]. AI use is no longer peripheral; it is mainstream.&lt;/p&gt;&lt;p&gt;Large-scale submission analytics further demonstrate measurable AI integration into student work. Turnitin [&lt;span&gt;4&lt;/span&gt;] reports that approximately 17% of global submissions exhibit substantial AI-writing indicators. Yet adoption alone does not equate to misconduct.&lt;/p&gt;&lt;p&gt;Emerging empirical research suggests that academic dishonesty rates may not have dramatically increased following the release of large language models. A recent study in &lt;i&gt;Computers &amp; Education&lt;/i&gt; found that self-reported cheating behaviors among secondary students remained statistically comparable pre- and post-ChatGPT introduction, suggesting transformation rather than explosion of misconduct patterns (e.g., comparative analyses reported in 2024). Similarly, scholars writing in the &lt;i&gt;Journal of Engineering Education&lt;/i&gt; argue that generative AI challenges assessment design more than it fundamentally alters student ethics [&lt;span&gt;5, 6&lt;/span&gt;].&lt;/p&gt;&lt;p&gt;Educator concern nevertheless remains high. The 2025 AI Index Report from Stanford's Institute for Human-Centered AI identified academic integrity and misuse as primary concerns among teachers and administrators [&lt;span&gt;7&lt;/span&gt;]. The central tension is therefore not only uncertainty about AI use, but also uncertainty about assessment resilience.&lt;/p&gt;&lt;p&gt;On the other hand, recent studies indicate that students in higher education use AI tools, but lack structured support and formal training skills [&lt;span&gt;8&lt;/span&gt;]. Students want clearer institutional support, guidance, and preparation for responsible AI use and future careers. [&lt;span&gt;9&lt;/span&gt;]. In contrast, other studies have reported on students' feelings of guilt, shame, and fear of using generat","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":"34 3","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cae.70186","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147683006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence and Responsible Adoption in Engineering Education: Evidence, Concerns, and a Constructive Path Forward","authors":"Magdy F. Iskander,&nbsp;Alejandra J. Magana","doi":"10.1002/cae.70186","DOIUrl":"https://doi.org/10.1002/cae.70186","url":null,"abstract":"&lt;p&gt;The rapid integration of generative artificial intelligence (AI) into educational practice has generated both enthusiasm and apprehension. For &lt;i&gt;Computer Applications in Engineering Education&lt;/i&gt; (CAE), a journal founded on the premise that computational technologies can enhance learning effectiveness, the present moment represents not a disruption of mission, but an inflection point. Among the most frequently expressed concerns is academic integrity, and consequently the potential erosion of critical thinking skills. Has generative AI fundamentally increased cheating, or has it primarily transformed the mechanisms through which academic misconduct may occur?&lt;/p&gt;&lt;p&gt;A balanced examination of available evidence suggests a more nuanced picture than public discourse often conveys.&lt;/p&gt;&lt;p&gt;Recent survey data confirm that generative AI use among students is widespread. The Higher Education Policy Institute [&lt;span&gt;1&lt;/span&gt;] reports that over 90% of surveyed UK students use generative AI tools for academic purposes. Similarly, the College Board [&lt;span&gt;2&lt;/span&gt;] reports that more than 80% of US high school students use generative AI for school-related work. Even adult learners have reported using AI for academic work [&lt;span&gt;3&lt;/span&gt;]. AI use is no longer peripheral; it is mainstream.&lt;/p&gt;&lt;p&gt;Large-scale submission analytics further demonstrate measurable AI integration into student work. Turnitin [&lt;span&gt;4&lt;/span&gt;] reports that approximately 17% of global submissions exhibit substantial AI-writing indicators. Yet adoption alone does not equate to misconduct.&lt;/p&gt;&lt;p&gt;Emerging empirical research suggests that academic dishonesty rates may not have dramatically increased following the release of large language models. A recent study in &lt;i&gt;Computers &amp; Education&lt;/i&gt; found that self-reported cheating behaviors among secondary students remained statistically comparable pre- and post-ChatGPT introduction, suggesting transformation rather than explosion of misconduct patterns (e.g., comparative analyses reported in 2024). Similarly, scholars writing in the &lt;i&gt;Journal of Engineering Education&lt;/i&gt; argue that generative AI challenges assessment design more than it fundamentally alters student ethics [&lt;span&gt;5, 6&lt;/span&gt;].&lt;/p&gt;&lt;p&gt;Educator concern nevertheless remains high. The 2025 AI Index Report from Stanford's Institute for Human-Centered AI identified academic integrity and misuse as primary concerns among teachers and administrators [&lt;span&gt;7&lt;/span&gt;]. The central tension is therefore not only uncertainty about AI use, but also uncertainty about assessment resilience.&lt;/p&gt;&lt;p&gt;On the other hand, recent studies indicate that students in higher education use AI tools, but lack structured support and formal training skills [&lt;span&gt;8&lt;/span&gt;]. Students want clearer institutional support, guidance, and preparation for responsible AI use and future careers. [&lt;span&gt;9&lt;/span&gt;]. In contrast, other studies have reported on students' feelings of guilt, shame, and fear of using generat","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":"34 3","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cae.70186","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147683119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards the Integration of Remote Laboratories in Massive Open Online Courses: Insights from Usage Logs and Student Feedback","authors":"Manuel J. Gomez,&nbsp;Mariano Albaladejo-González,&nbsp;José A. Ruipérez-Valiente,&nbsp;Carlos Rejón,&nbsp;Félix García-Loro,&nbsp;Antonio Robles-Gómez,&nbsp;Sergio Martin","doi":"10.1002/cae.70185","DOIUrl":"https://doi.org/10.1002/cae.70185","url":null,"abstract":"<p>Massive Open Online Courses (MOOCs) have established a new paradigm in education, enabling asynchronous, remote learning. Although MOOCs offer diverse educational content to students, comprehensive and realistic education requires hands-on training. Therefore, we present our integration of remote laboratories within a MOOC focused on Industry 4.0, together with a mixed-methods analysis combining demographic data, questionnaire responses, platform logs, and remote laboratory server records. The course incorporated seven distinct laboratories in two categories: remote Arduino-based laboratories, which connected physical devices to the MOOC, and a virtual infrastructure based on JupyterHub to support practical activities. Our findings indicate that the majority of participants in our MOOC were actively employed, with a mean age of 46.9 years, and males representing 88%. The students were mainly motivated by personal and professional growth, and 66% of these learners had no prior remote laboratory experience. Learners reported generally positive perceptions of the laboratory experience, including high levels of interest, usefulness, and self-efficacy, although engagement varied substantially across participants. Behavioral analyses revealed that students who ultimately pursued certification showed markedly higher participation and involvement in the remote laboratories than those who did not. At the same time, the course exhibited the substantial dropout patterns commonly reported in MOOCs, indicating that the inclusion of remote laboratories does not by itself eliminate persistence challenges. Our work provides an illustrative example of integrating remote laboratories into a MOOC and offers an analysis of enrolled students, delivering valuable findings for future researchers.</p>","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":"34 3","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cae.70185","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147682995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards the Integration of Remote Laboratories in Massive Open Online Courses: Insights from Usage Logs and Student Feedback","authors":"Manuel J. Gomez,&nbsp;Mariano Albaladejo-González,&nbsp;José A. Ruipérez-Valiente,&nbsp;Carlos Rejón,&nbsp;Félix García-Loro,&nbsp;Antonio Robles-Gómez,&nbsp;Sergio Martin","doi":"10.1002/cae.70185","DOIUrl":"https://doi.org/10.1002/cae.70185","url":null,"abstract":"<p>Massive Open Online Courses (MOOCs) have established a new paradigm in education, enabling asynchronous, remote learning. Although MOOCs offer diverse educational content to students, comprehensive and realistic education requires hands-on training. Therefore, we present our integration of remote laboratories within a MOOC focused on Industry 4.0, together with a mixed-methods analysis combining demographic data, questionnaire responses, platform logs, and remote laboratory server records. The course incorporated seven distinct laboratories in two categories: remote Arduino-based laboratories, which connected physical devices to the MOOC, and a virtual infrastructure based on JupyterHub to support practical activities. Our findings indicate that the majority of participants in our MOOC were actively employed, with a mean age of 46.9 years, and males representing 88%. The students were mainly motivated by personal and professional growth, and 66% of these learners had no prior remote laboratory experience. Learners reported generally positive perceptions of the laboratory experience, including high levels of interest, usefulness, and self-efficacy, although engagement varied substantially across participants. Behavioral analyses revealed that students who ultimately pursued certification showed markedly higher participation and involvement in the remote laboratories than those who did not. At the same time, the course exhibited the substantial dropout patterns commonly reported in MOOCs, indicating that the inclusion of remote laboratories does not by itself eliminate persistence challenges. Our work provides an illustrative example of integrating remote laboratories into a MOOC and offers an analysis of enrolled students, delivering valuable findings for future researchers.</p>","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":"34 3","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cae.70185","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147683062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of an Interactive Web-Based Tool for 2D Truss Analysis Using the Direct Stiffness Method","authors":"Daipayan Mandal","doi":"10.1002/cae.70183","DOIUrl":"https://doi.org/10.1002/cae.70183","url":null,"abstract":"<div>\u0000 \u0000 <p>The integration of computational tools in structural engineering education often relies on “black-box” commercial software, which can obscure the fundamental mechanics of the direct stiffness method (DSM) from students. This paper presents the development of the “Professional Truss Suite,” an interactive, web-based application designed to provide a true “glass-box” environment for the analysis of 2D trusses. Built using Python and the Streamlit framework, the tool not only automates the assembly and partitioning of global stiffness matrices but also explicitly exposes the intermediate mathematical steps—including element stiffness matrices, global assembly, and local force extraction—to the user. To enhance model building, explicit node numbering and dynamic unit scaling have been integrated into the graphical overlay. The software's pedagogical efficacy and accuracy were verified through a comprehensive case study of a 9-member Pratt truss under combined loading, yielding results with 0.00% numerical error against theoretical benchmarks. By bridging the gap between manual matrix calculations, stability validation, and professional reporting automation, this tool provides a robust platform for classroom instruction.</p>\u0000 </div>","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":"34 3","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147684289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of an Interactive Web-Based Tool for 2D Truss Analysis Using the Direct Stiffness Method","authors":"Daipayan Mandal","doi":"10.1002/cae.70183","DOIUrl":"https://doi.org/10.1002/cae.70183","url":null,"abstract":"<div>\u0000 \u0000 <p>The integration of computational tools in structural engineering education often relies on “black-box” commercial software, which can obscure the fundamental mechanics of the direct stiffness method (DSM) from students. This paper presents the development of the “Professional Truss Suite,” an interactive, web-based application designed to provide a true “glass-box” environment for the analysis of 2D trusses. Built using Python and the Streamlit framework, the tool not only automates the assembly and partitioning of global stiffness matrices but also explicitly exposes the intermediate mathematical steps—including element stiffness matrices, global assembly, and local force extraction—to the user. To enhance model building, explicit node numbering and dynamic unit scaling have been integrated into the graphical overlay. The software's pedagogical efficacy and accuracy were verified through a comprehensive case study of a 9-member Pratt truss under combined loading, yielding results with 0.00% numerical error against theoretical benchmarks. By bridging the gap between manual matrix calculations, stability validation, and professional reporting automation, this tool provides a robust platform for classroom instruction.</p>\u0000 </div>","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":"34 3","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147684291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Immersive Virtual Experimental Teaching Platform for Control Engineering: Design, Implementation, and Empirical Validation","authors":"Yulong Bai,&nbsp;Xiang Jia,&nbsp;Xiaoxin Yue,&nbsp;Xinyue Zhang,&nbsp;Zean Jin,&nbsp;Xianbao Tan","doi":"10.1002/cae.70175","DOIUrl":"https://doi.org/10.1002/cae.70175","url":null,"abstract":"<div>\u0000 \u0000 <p>Traditional control-engineering laboratories face cost, safety, and access constraints. Screen-based simulations offer limited realism and feedback. This study introduces an immersive Virtual Experimental Teaching Platform for Control Engineering (VETP-CE). The VETP-CE enables students to construct circuits, adjust control parameters, and observe real-time system responses within an interactive three-dimensional environment. A quasi-experimental study with 63 undergraduates compared the VR platform with a traditional laboratory. Learning outcomes were measured by pre/post-tests. Cognitive load was assessed in both groups, and user engagement was surveyed in the VR group. Results show larger post-test gains for the VR group and lower cognitive load than the control group <i>(t</i> =<i> −2.313, p</i> = <i>0.026)</i>. Within the VR group, user-engagement dimensions were negatively correlated with mental load and mental effort <i>(r</i> =<i> −0.75 to</i> − <i>0.89, p</i> &lt; <i>0.01)</i>. These findings indicate that well-designed immersive interaction supports cognitive regulation and improves learning efficiency in control-engineering laboratories. The work offers a replicable approach and design guidance for deploying VR laboratories in engineering curricula.</p>\u0000 </div>","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":"34 2","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147564335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Immersive Virtual Experimental Teaching Platform for Control Engineering: Design, Implementation, and Empirical Validation","authors":"Yulong Bai,&nbsp;Xiang Jia,&nbsp;Xiaoxin Yue,&nbsp;Xinyue Zhang,&nbsp;Zean Jin,&nbsp;Xianbao Tan","doi":"10.1002/cae.70175","DOIUrl":"https://doi.org/10.1002/cae.70175","url":null,"abstract":"<div>\u0000 \u0000 <p>Traditional control-engineering laboratories face cost, safety, and access constraints. Screen-based simulations offer limited realism and feedback. This study introduces an immersive Virtual Experimental Teaching Platform for Control Engineering (VETP-CE). The VETP-CE enables students to construct circuits, adjust control parameters, and observe real-time system responses within an interactive three-dimensional environment. A quasi-experimental study with 63 undergraduates compared the VR platform with a traditional laboratory. Learning outcomes were measured by pre/post-tests. Cognitive load was assessed in both groups, and user engagement was surveyed in the VR group. Results show larger post-test gains for the VR group and lower cognitive load than the control group <i>(t</i> =<i> −2.313, p</i> = <i>0.026)</i>. Within the VR group, user-engagement dimensions were negatively correlated with mental load and mental effort <i>(r</i> =<i> −0.75 to</i> − <i>0.89, p</i> &lt; <i>0.01)</i>. These findings indicate that well-designed immersive interaction supports cognitive regulation and improves learning efficiency in control-engineering laboratories. The work offers a replicable approach and design guidance for deploying VR laboratories in engineering curricula.</p>\u0000 </div>","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":"34 2","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147564336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}