IEEE Transactions on Learning Technologies最新文献

{"title":"An HRI Study of an LLM-Based Social Robot Delivering a University Lecture","authors":"S. Pashangpour;G. Nejat","doi":"10.1109/TLT.2026.3710170","DOIUrl":"https://doi.org/10.1109/TLT.2026.3710170","url":null,"abstract":"Social robots are being used in education to aid both students and instructors across a wide range of disciplines and student cohorts. In this article, we present a pilot human–robot interaction study with a social robot using a large language model to autonomously present a lecture to a group of undergraduate university students. We developed the LectureGPT architecture, which consists of the novel integration of GPT-4 and EmoPrompt for a social robot to interactively deliver lecture content using both verbal and nonverbal communication. We investigated the effectiveness of LectureGPT in generating and presenting lecture materials directly from instructor notes using varying robot expressions. Results showed that LectureGPT can facilitate informative lectures and maintain student engagement. This research provides insights into the future use of robotic assistance for education in order to leverage them as interactive educational supports for nonexpert users and expose students to state-of-the-art technology.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"19 ","pages":"737-751"},"PeriodicalIF":7.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148746917","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}

{"title":"Co-Designing Accessible Digital Musical Instruments With Young Children: Embodied Design Ideas From a Mixed-Ability Classroom","authors":"Lee Cheng;Chi Ying Lam;Enrico Bertelli;Yui Shikakura;Wing Yan Jasman Pang;Chi Hin Leung","doi":"10.1109/TLT.2026.3739111","DOIUrl":"https://doi.org/10.1109/TLT.2026.3739111","url":null,"abstract":"The use of accessible digital musical instruments (DMIs) has the potential to lower barriers to music-making, yet there remains a lack of empirical understanding of their design and implementation within mainstream schools for children with mixed abilities. This article presents a project that co-designed accessible DMIs with children to explore embodied approaches to musical interaction. The project involved five co-design workshops with a mixed-ability class of Year 1 primary school children (<italic>N</i> = 26), who contributed design ideas for a mobile app and a web app that translate physical gestures into musical outputs. Data were collected from worksheets completed by participating children and analyzed deductively, followed by human auditing and interpretive consensus. The findings indicate that children proposed natural bodily movements and intuitive gestures as interaction methods and used self-invented visual representations to articulate musical ideas. The study illustrates how child-centred co-design can elicit children’s embodied design ideas while highlighting the need for future research to evaluate how such interfaces affect children’s interaction, musical learning, and self-directed engagement.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"19 ","pages":"952-961"},"PeriodicalIF":7.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149188388","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}

{"title":"Knowledge Graph Augmented Generation for Examination Questions","authors":"Qian Li;Ziqing Zhao;Yongkang Liu;Ning Liu;Lizhen Cui","doi":"10.1109/TLT.2026.3682325","DOIUrl":"https://doi.org/10.1109/TLT.2026.3682325","url":null,"abstract":"Examination question generation (EQG) is a critical educational task that helps educators and learners acquire new knowledge. Traditional EQG approaches rely heavily on manually crafted rules and expert intervention, constraining their scalability and adaptability. Although large-language-model-based methods can harness extensive general knowledge to generate more varied questions, they remain prone to hallucinations stemming from inadequate domain-specific expertise and limited reasoning capabilities. In this article, we propose a unified framework for EQG tasks with <italic>methods</i>, <italic>domain-specific datasets</i>, <italic>evaluation metrics</i>, and <italic>an agent system</i>. Specifically, we first propose a keyword-guided method to construct a structured 2-D knowledge graph (KG), where keywords drive the extraction of meaningful triples and reinforce entity relations. Second, with the support of KG, we present a KG augmented generation (KAG) method, which generates examination questions and answers with external knowledge and graph reasoning. Third, we construct two new datasets and introduce novel metrics from both subjective and objective perspectives. Extensive experiments demonstrate that KAG significantly outperforms state-of-the-art baselines. Finally, we design an agent system that can automatically generate high-quality well-structured examination papers, easily adaptable to other subjects due to its training-free nature. We hope our proposed methods, datasets, metrics, and agent will facilitate and accelerate the adoption of EQG tasks.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"19 ","pages":"360-375"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147828799","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}

{"title":"TeenChat: A Human-Calibrated Mental Health LLM With Intention-Aware Dialogue and Contrastive Counseling Regularization","authors":"Qi Lang;Minjuan Wang;Shuli Hu;Dongjie Wang;Miao Li;Jianan Wang;Minghao Yin","doi":"10.1109/TLT.2026.3666907","DOIUrl":"https://doi.org/10.1109/TLT.2026.3666907","url":null,"abstract":"Generative large language models (LLMs) possess considerable potential for mitigating psychological distress among adolescents and advancing mental health education. Existing mental health-oriented large models primarily concentrate on acquiring empathetic capabilities, yet they still struggle to produce counseling responses that combine professional guidance with clear objectives for each dialogue turn. In addition, a significant gap exists between the linguistic expressions used by psychology experts and those of the general population, and this discrepancy directly affects users’ willingness to express emotions. To tackle these challenges, this article introduces an iterative training framework that integrates dual perspectives from both counselors and adolescent clients. The framework first trains the model to identify core user needs and establish phased counseling goals based on user input. It then employs contrastive learning techniques to ensure the generated content maintains internal consistency in both linguistic style and counseling objectives. The study also provides an in-depth examination of the dynamic psychological evolution of adolescents after receiving multiple rounds of large model-based counseling. Experimental results demonstrate that the proposed framework achieves excellent performance across multiple key metrics, including credibility, empathy, respectfulness, and depth of guidance. Human–machine dialogue mental health LLMs are undergoing a paradigm shift from passive empathy to active goal-directed intervention.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"19 ","pages":"155-165"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147558021","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}

{"title":"The Transition From Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions","authors":"Maryam Ebrahimi;Rajeev Sahay;Seyyedali Hosseinalipour;Bita Akram","doi":"10.1109/TLT.2026.3685099","DOIUrl":"https://doi.org/10.1109/TLT.2026.3685099","url":null,"abstract":"Research has increasingly explored the application of artificial intelligence (AI) and machine learning (ML) within the mental health domain to enhance both patient care and healthcare provider efficiency. Given that mental health challenges frequently emerge during early adolescence—the critical years of high school and college—investigating AI/ML-driven mental health solutions within the education domain is of paramount importance. Nevertheless, conventional AI/ML techniques follow a centralized model training architecture, which poses privacy risks due to the need for transferring students' sensitive data from institutions, universities, and clinics to central servers. Federated learning (FL) has emerged as a solution to address these risks by enabling distributed model training while maintaining data privacy. Despite its potential, research on applying FL to analyze students' mental health remains limited. In this article, we aim to address this limitation by proposing a roadmap for integrating FL into mental health data analysis within educational settings. We begin by providing an overview of mental health issues among students and reviewing existing studies where ML has been applied to address these challenges. Next, we examine broader applications of FL in the mental health domain to emphasize the lack of focus on educational contexts. Finally, we propose promising research directions focused on using FL to address mental health issues in the education sector, which entails discussing the synergies between the proposed directions with broader human-centered domains. By categorizing the proposed research directions into short- and long-term strategies and highlighting the unique challenges at each stage, we aim to encourage the development of privacy-conscious AI/ML-driven mental health solutions.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"19 ","pages":"341-359"},"PeriodicalIF":7.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148504406","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}

{"title":"ProtoSim: Interactive Learning Environment for High School Students to Learn the TCP/IP Protocol Suite in Computer Science Education","authors":"Yuki Kitamura;Hiroyasu Ide;Hiroyuki Nagataki;Susumu Kanemune;Shizuka Shirai","doi":"10.1109/TLT.2026.3699503","DOIUrl":"https://doi.org/10.1109/TLT.2026.3699503","url":null,"abstract":"This study proposes ProtoSim, an interactive learning environment designed to provide experiential learning of network mechanisms with a simulator and autonomous learning support features. The simulator in ProtoSim treats the browsing scene on a webpage on a smartphone, enabling users to observe the flow of information through layers of the TCP/IP protocol suite in a visualized manner. In addition, it features missions (short-answer questions) that utilize the simulator, with automated correctness evaluation to facilitate autonomous learning. To evaluate the effects of ProtoSim, an educational practice using ProtoSim was conducted with 211 high school students. Analysis of the learning logs revealed that students engaged with the exercises by adjusting the content and methods of their learning according to their individual circumstances. Furthermore, the increase in the correct answer rate from pre- to posttests indicates that ProtoSim effectively promoted a structural understanding of the mechanisms behind the computer network, particularly those based on the communication protocol layers.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"19 ","pages":"598-608"},"PeriodicalIF":7.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442504","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}

{"title":"A Vision-Based System for Automated Sign Language Teaching","authors":"Kostas Blekos;Dimitrios Kosmopoulos;Klimis Antzakas","doi":"10.1109/TLT.2026.3670474","DOIUrl":"https://doi.org/10.1109/TLT.2026.3670474","url":null,"abstract":"Sign languages are used by millions of individuals who are deaf or hard-of-hearing worldwide, yet learning them can be challenging due to limited access to qualified instructors and real-time feedback. Sign language learning is crucial for inclusion and accessible communication; however, existing e-learning solutions often fail to provide detailed or explainable feedback. To address this gap, we present a novel computer-assisted system that leverages computer vision and machine learning to teach sign languages. In our approach, students follow prerecorded videos of an instructor performing signs, and the system captures the students’ own sign production through a conventional webcam. By analyzing both static handshapes and dynamic trajectories, our method provides detailed and explainable feedback at the level of keyframes, pointing out specific misalignments in hand, wrist, and motion path. This differs from many existing tools that offer only binary correct/incorrect evaluations without guidance for improvement. We implemented the system as a prototype with a full curriculum and conducted a pilot usability evaluation with 43 participants, ranging from beginners to skilled signers. Results indicate that users found the platform intuitive, the feedback mechanism informative, and the overall experience motivating for continued practice. The pilot assessed user perceptions of usability and feedback quality; learning outcomes were not measured in this initial evaluation. Notably, our design is language-agnostic, enabling broad application across different sign language communities. By offering instant fine-grained feedback, our system aims to improve self-guided learning of sign languages and promote greater accessibility, ultimately bridging communication gaps and fostering inclusion for the Deaf community.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"19 ","pages":"182-195"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11421524","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147558046","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}

{"title":"The Guideline for Reporting Generative Artificial Intelligence Use in Educational Research: Implications From a Systematic Review","authors":"Jiutong Luo;Siqi Li;Minjuan Wang;Xiaoming Cao;Hui Li","doi":"10.1109/TLT.2026.3708984","DOIUrl":"https://doi.org/10.1109/TLT.2026.3708984","url":null,"abstract":"Generative artificial intelligence (GenAI) is profoundly transforming the research field, with many researchers embracing its potential to enhance their work. However, the integration of GenAI into research practices still presents non-negligible challenges. To address these critical issues and better understand the current state of GenAI use in research, we conducted a systematic review focusing on GenAI-assisted research in the social sciences area and screened 8 831 relevant articles, of which 126 were retained for in-depth analysis. Our findings reveal that GenAI has been widely adopted in empirical studies across various social science domains, demonstrating its value in supporting research processes, such as conceptualization and research design, data collection and analysis, and writing and editing. Drawing on insights from existing practices, we developed a guideline for the academic community to support the development of a broader consensus on GenAI-assisted research practices, particularly in the field of education. This guideline aims to better regulate and leverage the potential of GenAI in research, ensuring its ethical and effective application.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"19 ","pages":"694-705"},"PeriodicalIF":7.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148627396","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}

{"title":"Redesigning Math Assessment Items as Video Stories is Associated With Reduced Physiological Stress","authors":"Fettah Kiran;Paul T. Cirino;Ioannis Pavlidis","doi":"10.1109/TLT.2026.3715735","DOIUrl":"https://doi.org/10.1109/TLT.2026.3715735","url":null,"abstract":"Foundational mathematics assessments can trigger anxiety and acute stress that discourage persistence in quantitative pathways. We introduce a video-story item format that places skill-matched mathematical problems inside short, relatable narrative videos. This video format represents a total-format design, combining relatable narrative, enacted video delivery, and temporal presentation; therefore, its observed effects reflect this combined design rather than any individual component in isolation. We evaluate whether this redesigned test experience is associated with lower physiological stress during problem solving. In a controlled study of lower division undergraduates (<inline-formula><tex-math>$N=50$</tex-math></inline-formula>), participants completed a 36-item instrument designed around 12 skill-matched triplets—Video (<inline-formula><tex-math>$V$</tex-math></inline-formula>), Word (<inline-formula><tex-math>$W$</tex-math></inline-formula>), and Abstract (<inline-formula><tex-math>$A$</tex-math></inline-formula>)—while we continuously recorded multimodal affective signals during problem solving. We operationalize acute test stress as elevated autonomic arousal. We, therefore, analyze baseline-normalized cardiac dynamics [heart rate and interbeat interval (IBI/RR)-derived timing] and facial perspiration, and use facial expressions to characterize displayed valence. Cross-classified mixed-effects models accounting for participant differences and item difficulty show convergent reductions in physiological arousal across cardiac and facial-perspiration channels for the video-story format relative to conventional formats (<inline-formula><tex-math>$A$</tex-math></inline-formula>, <inline-formula><tex-math>$W$</tex-math></inline-formula>). Separate valence models show a shift toward more positive displayed valence. These affective advantages remain after controlling for time-on-item and occur without measurable loss in overall test performance. Crucially, we provide a practical procedure to derive participant-relative stress labels directly from baseline-normalized multimodal physiology, avoiding per-item self-report and enabling scalable dataset construction. Data, code, and materials are openly shared.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"19 ","pages":"818-835"},"PeriodicalIF":7.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11615123","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148836848","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}

{"title":"Design and Implementation Framework of Game Activity Generators for Declarative Knowledge Training","authors":"Bérénice Lemoine;Pierre Laforcade;Sébastien George","doi":"10.1109/TLT.2025.3646366","DOIUrl":"https://doi.org/10.1109/TLT.2025.3646366","url":null,"abstract":"The long- and short-term memorization of declarative knowledge (DK) requires repetition, which can quickly lead to boredom for learners. Serious games are often developed to support this training by offering a better motivational vector. However, a key design challenge is providing learners with experiences that are consistently varied and adapted. Generating adapted activities automatically is a technique that is little discussed in the field of technology-enhanced learning. This work addresses the challenge of designing generators for adapted and varied activities within serious games dedicated to DK training. The core contribution is the proposal of a comprehensive software framework aimed at facilitating the design and implementation of these generators. This framework is built upon the theories and practices of model-driven engineering (MDE), utilizing models and metamodels to ensure formal specification, abstraction, and reusability. The framework is designed to be extensible to different didactic domains. It enables the production of training activities that are simultaneously varied and adapted, considering adaptation from both the educational perspective (learner's level and progress within a training path defined by teachers) and the gaming perspective (player's preferences about gameplay). The activities produced by generators developed using this framework take the form of Roguelite-oriented dungeon levels, a genre chosen because its inherent procedural generation provides the necessary variety, repetition, and progression characteristics for DK acquisition. This article presents the framework, including its conceptual models and its MDE-based software infrastructure. We demonstrate its ability to express different didactic domains through its application to distinct educational contexts. Finally, this article details the evaluation from an engineering point of view, including the use of automated system tests to verify that the generated activities satisfy the properties of adaptation and variety.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"19 ","pages":"1-20"},"PeriodicalIF":4.9,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145963441","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}