IEEE Transactions on Learning Technologies最新文献_第2页

Capturing Activities and Interactions in Makerspaces Using Monocular Computer Vision 利用单目计算机视觉捕捉创客空间中的活动和互动

IF 2.9 3区教育学

IEEE Transactions on Learning Technologies Pub Date : 2025-04-18 DOI: 10.1109/TLT.2025.3562298

Sohail Ahmed Soomro;Halar Haleem;Bertrand Schneider;Georgi V. Georgiev

{"title":"Capturing Activities and Interactions in Makerspaces Using Monocular Computer Vision","authors":"Sohail Ahmed Soomro;Halar Haleem;Bertrand Schneider;Georgi V. Georgiev","doi":"10.1109/TLT.2025.3562298","DOIUrl":"https://doi.org/10.1109/TLT.2025.3562298","url":null,"abstract":"This study presents a monocular approach for capturing students' prototyping activities and interactions in digital-fabrication-based makerspaces. The proposed method uses images from a single camera and applies object reidentification, tracking, and depth estimation algorithms to track and uniquely label participants in the space, extracting both spatial and temporal information. A case study was conducted by recording a lab session in a digital-fabrication-based makerspace where students from a university undergraduate program turned their product ideas into tangible prototypes using digital fabrication. Moreover, a creativity test was conducted to assess individual creative competence. The findings reveal that the monocular approach effectively captures interactions among team members and instructors. It also identifies prototyping activities at individual and team levels. Furthermore, results demonstrate that the students with high and low creativity scores exhibit distinct patterns of interaction with instructors and teammates. Those with high creativity scores worked more independently and less collaboratively. Students with low creativity scores worked more collaboratively and less independently. The proposed monocular approach can be used in formal educational settings for student evaluation and prototyping activities. In addition, instructors can use this approach to assess and tailor teaching methods by promptly intervening and providing structures and scaffolding support to assist struggling students.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"470-483"},"PeriodicalIF":2.9,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10970091","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Empowering Instructors With AI: Evaluating the Impact of an AI-Driven Feedback Tool in Learning Analytics 用人工智能授权教师：评估人工智能驱动的反馈工具在学习分析中的影响

IF 2.9 3区教育学

IEEE Transactions on Learning Technologies Pub Date : 2025-04-18 DOI: 10.1109/TLT.2025.3562379

Cleon Xavier;Luiz Rodrigues;Newarney Costa;Rodrigues Neto;Gabriel Alves;Taciana Pontual Falcão;Dragan Gašević;Rafael Ferreira Mello

{"title":"Empowering Instructors With AI: Evaluating the Impact of an AI-Driven Feedback Tool in Learning Analytics","authors":"Cleon Xavier;Luiz Rodrigues;Newarney Costa;Rodrigues Neto;Gabriel Alves;Taciana Pontual Falcão;Dragan Gašević;Rafael Ferreira Mello","doi":"10.1109/TLT.2025.3562379","DOIUrl":"https://doi.org/10.1109/TLT.2025.3562379","url":null,"abstract":"Providing timely and personalized feedback on open-ended student responses is a challenge in education due to the increased workloads and time constraints educators face. While existing research has explored how learning analytic approaches can support feedback provision, previous studies have not sufficiently investigated educators' perspectives of how these strategies affect the assessment process. This article reports on the findings of a study that aimed to evaluate the impact of an artificial intelligence (AI)-driven platform designed to assist educators in the assessment and feedback process. Leveraging large language models and learning analytics, the platform supports educators by offering tag-based recommendations and AI-generated feedback to enhance the quality and efficiency of open-response evaluations. A controlled experiment involving 65 higher education instructors assessed the platform's effectiveness in real-world environments. Using the technology acceptance model, this study investigated the platform's usefulness and relevance from the instructors' perspectives. Moreover, we collected data from the platform's usage to identify partners in instructors' behavior for different scenarios. Results indicate that AI-driven feedback significantly improved instructors' ability to provide detailed personalized feedback in less time. This study contributes to the growing research on AI applications in educational assessment and highlights key considerations for adopting AI-driven tools in instructional settings.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"498-512"},"PeriodicalIF":2.9,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10970108","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EduPlanner: LLM-Based Multiagent Systems for Customized and Intelligent Instructional Design EduPlanner：基于法学硕士的定制智能教学设计多代理系统

IF 2.9 3区教育学

IEEE Transactions on Learning Technologies Pub Date : 2025-04-16 DOI: 10.1109/TLT.2025.3561332

Xueqiao Zhang;Chao Zhang;Jianwen Sun;Jun Xiao;Yi Yang;Yawei Luo

{"title":"EduPlanner: LLM-Based Multiagent Systems for Customized and Intelligent Instructional Design","authors":"Xueqiao Zhang;Chao Zhang;Jianwen Sun;Jun Xiao;Yi Yang;Yawei Luo","doi":"10.1109/TLT.2025.3561332","DOIUrl":"https://doi.org/10.1109/TLT.2025.3561332","url":null,"abstract":"Large language models (LLMs) have significantly advanced smart education in the artificial general intelligence era. A promising application lies in the automatic generalization of instructional design for curriculum and learning activities, focusing on two key aspects: 1) <italic>customized generation: generating niche-targeted teaching content based on students' varying learning abilities and states and 2) <italic>intelligent optimization: iteratively optimizing content based on feedback from learning effectiveness or test scores. Currently, a single large LLM cannot effectively manage the entire process, posing a challenge for designing intelligent teaching plans. To address these issues, we developed EduPlanner, an LLM-based multiagent system comprising an evaluator agent, an optimizer agent, and a question analyst, working in adversarial collaboration to generate customized and intelligent instructional design for curriculum and learning activities. Taking mathematics lessons as our example, EduPlanner employs a novel Skill-Tree structure to accurately model the background mathematics knowledge of student groups, personalizing instructional design for curriculum and learning activities according to students' knowledge levels and learning abilities. In addition, we introduce the CIDDP, an LLM-based 5-D evaluation module encompassing <bold>Clarity, <bold>Integrity, <bold>Depth, <bold>Practicality, and <bold>Pertinence, to comprehensively assess mathematics lesson plan quality and bootstrap intelligent optimization. Experiments conducted on the GSM8K and Algebra datasets demonstrate that EduPlanner excels in evaluating and optimizing instructional design for curriculum and learning activities. Ablation studies further validate the significance and effectiveness of each component within the framework.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"416-427"},"PeriodicalIF":2.9,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

How Digital Teacher Appearance Anthropomorphism Impacts Digital Learning Satisfaction and Intention to Use: Interaction With Knowledge Type 数字教师外表拟人化如何影响数字学习满意度和使用意愿：与知识类型的互动

IF 2.9 3区教育学

IEEE Transactions on Learning Technologies Pub Date : 2025-04-11 DOI: 10.1109/TLT.2025.3560032

Biao Gao;Jun Yan;Ronghui Zhong

{"title":"How Digital Teacher Appearance Anthropomorphism Impacts Digital Learning Satisfaction and Intention to Use: Interaction With Knowledge Type","authors":"Biao Gao;Jun Yan;Ronghui Zhong","doi":"10.1109/TLT.2025.3560032","DOIUrl":"https://doi.org/10.1109/TLT.2025.3560032","url":null,"abstract":"Digital teachers represent an innovative fusion of media and artificial intelligence (AI) within online educational environments. However, the specific ways in which the appearance anthropomorphism of digital teachers influences the delivery of different knowledge types remain insufficiently understood. Drawing on Embodied Learning Theory and Parasocial Interaction Theory, this study investigates how digital teachers' appearance (cartoonish versus realistic) interacts with knowledge types (explicit versus tacit) to affect digital learning satisfaction and usage intention, exploring the mediating roles of physical and social presence. Initially, we implemented a 2 × 2 experimental design using a large language model application, collecting data from 475 participants to empirically test our hypotheses. Subsequently, in-depth interviews were conducted with 21 Chinese university students to further validate and clarify the underlying mechanisms behind these interactions. The results indicate that digital teachers with a cartoonish appearance are more effective for delivering explicit knowledge, whereas digital teachers with a realistic appearance excel in conveying tacit knowledge. Both physical presence and social presence were found to significantly mediate these effects. This research enriches our understanding of AI-enhanced online education by highlighting the alignment effect between digital teacher appearance and the type of knowledge delivered and by uncovering the underlying psychological mechanisms. In addition, it offers practical insights for the design of digital human appearances in educational interfaces and broader AI–human interaction scenarios.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"438-457"},"PeriodicalIF":2.9,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

OrientaTree: A Mobile Tool for Geolocated Educational Orienteering

IF 2.9 3区教育学

IEEE Transactions on Learning Technologies Pub Date : 2025-04-10 DOI: 10.1109/TLT.2025.3559623

Juan A. Muñoz-Cristóbal;Vanesa Gallego-Lema;Higinio F. Arribas-Cubero;Gabriel Rodríguez-González;Felipe Hermida-Arias;Alejandra Martínez-Monés

{"title":"OrientaTree: A Mobile Tool for Geolocated Educational Orienteering","authors":"Juan A. Muñoz-Cristóbal;Vanesa Gallego-Lema;Higinio F. Arribas-Cubero;Gabriel Rodríguez-González;Felipe Hermida-Arias;Alejandra Martínez-Monés","doi":"10.1109/TLT.2025.3559623","DOIUrl":"https://doi.org/10.1109/TLT.2025.3559623","url":null,"abstract":"Orienteering has long been used in physical education due to its recognized benefits for perceptual-motor capacity, as a tool for safe and efficient movement and as a recreational activity. It also helps in the acquisition of skills in multiple domains besides physical education, such as geography, mathematics, or biology. Many teachers use this interdisciplinary nature of orienteering, complementing it with educational tasks at each control point, and using geolocation and mobile devices to avoid the cumbersome tasks related to the setting up and dismantling of physical circuits. However, the systems that allow this kind of geolocated educational orienteering activities have some limitations in their implementation of the elements of orienteering or in the educational possibilities for teachers to configure and monitor learning situations that can adapt to their learning goals. To address these challenges, this article proposes a set of design requirements to create geolocated educational orienteering systems and a mobile tool, OrientaTree, created following the said requirements. A prototype of OrientaTree has been evaluated by means of a feature analysis and a pilot study involving five teachers and 115 students. The results of the evaluation provide evidence that OrientaTree overcomes the limitations of alternative reviewed approaches to conduct geolocated educational orienteering activities. However, it could be improved to allow more configuration capabilities to permit teachers to better adapt activities to their learning goals.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"484-497"},"PeriodicalIF":2.9,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10960751","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Preparing Student Teachers for Professional Development: Mentoring Generative Artificial Intelligence (AI) Learners in Mathematical Problem Solving 培养学生教师的专业发展：指导生成式人工智能（AI）学习者解决数学问题

IF 2.9 3区教育学

IEEE Transactions on Learning Technologies Pub Date : 2025-04-03 DOI: 10.1109/TLT.2025.3557037

Xiuling He;Ruijie Zhou;Qiong Fan;Xiong Xiao;Ying Yu;Zhonghua Yan

{"title":"Preparing Student Teachers for Professional Development: Mentoring Generative Artificial Intelligence (AI) Learners in Mathematical Problem Solving","authors":"Xiuling He;Ruijie Zhou;Qiong Fan;Xiong Xiao;Ying Yu;Zhonghua Yan","doi":"10.1109/TLT.2025.3557037","DOIUrl":"https://doi.org/10.1109/TLT.2025.3557037","url":null,"abstract":"Rapid technological advancements are reshaping pedagogical expertise development, offering novel pathways to equip educators with 21st-century professional competencies. This study proposes an innovative artificial intelligence (AI)-driven professional development approach and investigates its impact on student teachers’ competence development. In total, 28 third-year student teachers participated in tasks to mentor AI learners, applying mentor-acquired knowledge and skills. Task performance and task processes were used to delineate teacher knowledge and teaching practices, respectively, while data from professional development surveys were thoroughly analyzed to gain in-depth insights into teacher perspectives. Findings reveal that AI teaching practice significantly enhanced participants’ knowledge acquisition. Notably, high-performance groups demonstrated complex mentoring patterns emphasizing procedural mentoring. Conversely, the low-performance group preferred a more directive and factual approach, whose behavioral patterns appeared less significant. Furthermore, AI teaching practice also had a positive effect on student teachers’ perspectives toward professional knowledge and AI literacy. The findings of this study contribute to the theoretical and practical understanding of integrating AI-based learning activities into teacher education.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"458-469"},"PeriodicalIF":2.9,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluating the Impact of Lightboard Videos on College Students' Performance in a Mathematical Optimization Course 评价光板视频对大学生数学优化课程学习成绩的影响

IF 2.9 3区教育学

IEEE Transactions on Learning Technologies Pub Date : 2025-03-31 DOI: 10.1109/TLT.2025.3556527

Jingjing Chen;Rao Muhammad Aqib Hassan;Shuai Sun;Yilin Mo;Dan Zhang

{"title":"Evaluating the Impact of Lightboard Videos on College Students' Performance in a Mathematical Optimization Course","authors":"Jingjing Chen;Rao Muhammad Aqib Hassan;Shuai Sun;Yilin Mo;Dan Zhang","doi":"10.1109/TLT.2025.3556527","DOIUrl":"https://doi.org/10.1109/TLT.2025.3556527","url":null,"abstract":"The lightboard, an affordable and readily accessible tool, has become a promising approach for enhancing engagement in instructional videos. Despite its potential, previous studies have primarily highlighted the benefits of lightboard videos by evaluating learners' subjective experiences, with limited empirical research examining their impact on learning outcomes. Moreover, the psychological factors underlying the potential advantages of lightboard videos have remained largely unexplored. To address these gaps, the present study conducted an online learning task in a mathematical optimization course, randomly assigning 78 college students to three groups: lightboard, whiteboard, and no-instructor. Learning outcomes and experiences during the learning process were measured and analyzed. The results showed that the lightboard group experienced significantly lower cognitive load while achieving learning outcomes comparable to the other two groups, suggesting that lightboard videos can reduce students' cognitive load without compromising learning outcomes. Further analysis of the psychological factors revealed that cognitive load played a more critical role than perceived social presence or learning motivation in explaining learning outcomes. These findings underscore the positive impact of lightboard videos on online learning, provide insights into the underlying psychological mechanisms, and offer implications for their integration into educational practices.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"428-437"},"PeriodicalIF":2.9,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multimodality of AI for Education: Toward Artificial General Intelligence 教育领域人工智能的多模态：走向通用人工智能

IF 2.9 3区教育学

IEEE Transactions on Learning Technologies Pub Date : 2025-03-28 DOI: 10.1109/TLT.2025.3574466

Gyeonggeon Lee;Lehong Shi;Ehsan Latif;Yizhu Gao;Arne Bewersdorff;Matthew Nyaaba;Shuchen Guo;Zhengliang Liu;Gengchen Mai;Tianming Liu;Xiaoming Zhai

{"title":"Multimodality of AI for Education: Toward Artificial General Intelligence","authors":"Gyeonggeon Lee;Lehong Shi;Ehsan Latif;Yizhu Gao;Arne Bewersdorff;Matthew Nyaaba;Shuchen Guo;Zhengliang Liu;Gengchen Mai;Tianming Liu;Xiaoming Zhai","doi":"10.1109/TLT.2025.3574466","DOIUrl":"https://doi.org/10.1109/TLT.2025.3574466","url":null,"abstract":"This article addresses the growing importance of understanding how multimodal artificial general intelligence (AGI) can be integrated into educational practices. We first reviewed the theoretical foundations of multimodality in human learning, encompassing its concept and history, dual coding theory and multimedia theory, VARK multimodality, and multimodal assessment (see Section II-A). After that, we revisited the essential components of AGI, particularly focusing on the multimodal nature of AGI that distinguished it from artificial narrow intelligence. Based on its conversational functionality, multimodal AGI is considered an educational agent already tested in various educational situations (see Section II-B). How significant text, image, audio, and video modalities are for education, the technological backgrounds of AGI for analyzing and generating them, and educational applications of artificial intelligence (AI) for each modality were thoroughly reviewed (Sections III–VI). Finally, we comprehensively investigated the ethics of AGI in education, originating from the ethics of AI and specified in three strands: first, data privacy and ethical integrity, second, explainability, transparency, and fairness, and third, responsibility and decision-making. Practical implementation of ethical AGI frameworks in education was reviewed (see Section VII). This article also discusses the implications for learning theories, derived operational design principles, current research gaps, practical constraints and institutional readiness, and future directions (see Section VIII). This exploration aims to provide an advanced understanding of the intersection between AI, multimodality, and education, setting a foundation for future research and development.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"666-683"},"PeriodicalIF":2.9,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144581641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Annotation Guideline-Based Knowledge Augmentation: Toward Enhancing Large Language Models for Educational Text Classification 基于标注指南的知识增强：面向教育文本分类的大型语言模型

IF 2.9 3区教育学

IEEE Transactions on Learning Technologies Pub Date : 2025-03-26 DOI: 10.1109/TLT.2025.3570775

Shiqi Liu;Sannyuya Liu;Lele Sha;Zijie Zeng;Dragan Gašević;Zhi Liu

{"title":"Annotation Guideline-Based Knowledge Augmentation: Toward Enhancing Large Language Models for Educational Text Classification","authors":"Shiqi Liu;Sannyuya Liu;Lele Sha;Zijie Zeng;Dragan Gašević;Zhi Liu","doi":"10.1109/TLT.2025.3570775","DOIUrl":"https://doi.org/10.1109/TLT.2025.3570775","url":null,"abstract":"Automated classification of learner-generated text to identify behavior, emotion, and cognition indicators, collectively known as learning engagement classification (LEC), has received considerable attention in fields such as natural language processing(NLP), learning analytics, and educational data mining. Recently, large language models (LLMs), such as ChatGPT, which are considered promising technologies for artificial general intelligence, have demonstrated remarkable performance in various NLP tasks. However, their capabilities in LEC tasks still lack comprehensive evaluation and improvement approaches. This study introduces a novel benchmark for LEC, encompassing six datasets that cover behavior classification (question and urgency level), emotion classification (binary and epistemic emotion), and cognition classification (opinion and cognitive presence). In addition, we propose the annotation guideline-based knowledge augmentation (AGKA) approach, which leverages GPT-4.0 to recognize and extract label definitions from annotation guidelines and applies random undersampling to select a representative set of examples. Experimental results demonstrate the following: AGKA enhances LLM performance compared to vanilla prompts, particularly for GPT-4.0 and Llama-3 70B; GPT-4.0 and Llama-3 70B with AGKA are comparable to fully fine-tuned models such as BERT and RoBERTa on simple binary classification tasks; for multiclass tasks requiring complex semantic understanding, GPT-4.0 and Llama-3 70B outperform the fine-tuned models in the few-shot setting but fall short of the fully fine-tuned models; Llama-3 70B with AGKA shows comparable performance to GPT-4.0, demonstrating the viability of these open-source alternatives; and the ablation study highlights the importance of customizing and evaluating knowledge augmentation strategies for each specific LLM architecture and task.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"619-634"},"PeriodicalIF":2.9,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Human–Machine Cocreation: The Effects of ChatGPT on Students’ Learning Performance, AI Awareness, Critical Thinking, and Cognitive Load in a STEM Course Toward Entrepreneurship 人机共同创造：ChatGPT对STEM创业课程中学生学习表现、人工智能意识、批判性思维和认知负荷的影响

IF 2.9 3区教育学

IEEE Transactions on Learning Technologies Pub Date : 2025-03-26 DOI: 10.1109/TLT.2025.3554584

Yu Ji;Zehui Zhan;Tingting Li;Xuanxuan Zou;Siyuan Lyu

{"title":"Human–Machine Cocreation: The Effects of ChatGPT on Students’ Learning Performance, AI Awareness, Critical Thinking, and Cognitive Load in a STEM Course Toward Entrepreneurship","authors":"Yu Ji;Zehui Zhan;Tingting Li;Xuanxuan Zou;Siyuan Lyu","doi":"10.1109/TLT.2025.3554584","DOIUrl":"https://doi.org/10.1109/TLT.2025.3554584","url":null,"abstract":"The advent of generative artificial intelligence (GAI), exemplified by ChatGPT, has introduced both new opportunities and challenges in science, technology, engineering, and mathematics (STEM) and entrepreneurship education. This exploratory quasi-experimental study examined the effects of ChatGPT-assisted collaborative learning (CCL) on students’ learning performance, artificial intelligence (AI) awareness, critical thinking, and cognitive load. A total of 36 sophomore undergraduates participated in an eight-week instructional experiment, dedicating 3 h per week to applying STEM and entrepreneurship knowledge in the creation of cultural products. The experimental group (<italic>N = 21) participated in CCL, while the control group (<italic>N = 15) engaged in non-ChatGPT-assisted collaborative learning (NCCL). The results indicated that the CCL group outperformed the NCCL group in terms of learning performance, AI awareness, and cognitive load, while the NCCL group excelled in critical thinking. The findings confirm that ChatGPT offers significant potential and advantages in addressing complex problems within group collaboration and stimulating group creativity, providing new insights into fostering students’ entrepreneurial spirit and skills. However, overreliance on and misuse of ChatGPT may hinder student learning outcomes. Future research should focus on the cocreative problem-solving mechanisms between humans and machines in entrepreneurial education, particularly the interplay of knowledge, thinking, emotions, and actions in collaborative processes involving GAI.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"402-415"},"PeriodicalIF":2.9,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143877647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0