IEEE Transactions on Learning Technologies最新文献

{"title":"Multimodal Conversational Emotion Recognition in Education With Uncertain Missing Modalities: A Graph Fusion Network Approach","authors":"Juan Yang;Ruinan Shi;Xu Du;Yiqian Xie;Weiwei Wang","doi":"10.1109/TLT.2026.3691830","DOIUrl":"https://doi.org/10.1109/TLT.2026.3691830","url":null,"abstract":"Multimodal emotion recognition in conversations (MERC) under uncertain missing modalities remains a critical challenge, especially in educational contexts. Significant research efforts have been dedicated to developing novel solutions to tackle this problem. Nevertheless, existing methods are difficult to fully utilize available information for high-quality data reconstruction, and fail to learn discriminative multimodal representations for handling uncertain missing modalities. Furthermore, the generalization of such methods in educational contexts remains largely overlooked. To address these challenges, this study proposes a novel graph fusion network approach for achieving MERC tasks in education with uncertain missing modalities. Our method first reconstructs missing modalities by using adaptive fusion weights to aggregate information from the most similar neighbors in the available modalities. The reconstructed data are processed via a mask-aware graph structure to explicitly exploit masked cues to enhance the robustness of intramodal representations. Initial multimodal fusion representations derived from single-modal features are employed to build a dual-graph structure for further refining the multimodal representation learning of each utterance. During the training process, our method also introduces the boundary-aware contrastive learning to enable the accurate identification of samples near decision boundaries. Experiments on three multimodal datasets, including one self-collected educational dataset, CMU-MOSI, and CMU-MOSEI, demonstrates its superiority, effectiveness, and generalizability.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"19 ","pages":"492-504"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148171737","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":"IAEAgent: LLM-Based Educational Agent for Instructional Alignment Evaluation in University Courses","authors":"Junqi Liao;Minjuan Wang;Xiaofei Wu;Yizhi Wang;Xin Wei","doi":"10.1109/TLT.2026.3695576","DOIUrl":"https://doi.org/10.1109/TLT.2026.3695576","url":null,"abstract":"In university courses, instructional alignment evaluation reflects the degree of alignment between the intended teaching requirements and the actual content delivered by teachers. It can not only provide university administrative departments with evidence about actual teaching performance but also facilitate teachers’ targeted reflection and continuous instructional improvement. However, there is still a lack of a theory-grounded, course-aware, and quantitatively interpretable instructional alignment evaluator. To address this issue, in this article, we propose a new evaluation paradigm—a large language model (LLM)-based educational agent (IAEAgent)—for instructional alignment evaluation in university courses. First, by injecting teaching materials such as textbooks using parameter-efficient fine-tuning, a course-specific LLM with in-depth disciplinary comprehension is constructed. Then, based on this LLM and employing prompt engineering, the intended and actual instructional knowledge graphs (KGs) can be, respectively, extracted from teaching requirement documents and teachers’ actual audio transcripts of a lesson. Subsequently, guided by teaching quality evaluation theories, a quantitative analysis of instructional alignment is conducted for these two KGs across three crucial dimensions: knowledge coverage completeness, knowledge depth consistency, and knowledge structure coherence. Finally, based on the obtained analytical results, a teaching quality evaluation report containing teaching diagnostics and improvement suggestions is generated. Extensive experiments demonstrate that the IAEAgent excels in comprehensive and fine-grained instructional alignment evaluation. Furthermore, ablation studies validate the effectiveness of each step within the IAEAgent.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"19 ","pages":"505-519"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148236173","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":"Improving Automated Feedback Systems for Tutor Training in Low-Resource Scenarios Through Data Augmentation","authors":"Chentianye Xu;Jionghao Lin;Yujing Zhang;Tongshuang Wu;Vincent Aleven;Kenneth R. Koedinger","doi":"10.1109/TLT.2026.3665740","DOIUrl":"https://doi.org/10.1109/TLT.2026.3665740","url":null,"abstract":"Tutoring is an effective instructional method for enhancing student learning, yet its success relies on the skill and experience of the tutors. This reliance presents challenges for the widespread implementation of tutoring, particularly in training novice tutors. To support tutor training programs, real-time automated feedback systems are essential for efficiently training large numbers of tutors. A recent study employed generative pretrained transformers (GPTs) for sequence labeling to identify desirable and undesirable praise components in a tutor training dataset, providing explanatory feedback. However, this approach requires a significant amount of labeled data for fine-tuning, which is both labor-intensive and dependent on expert input. To address the challenges associated with extensive data labeling, the current study explores the use of prompting more advanced GPT models such as GPT-4o to generate synthetic datasets for augmenting labeled response data, followed by fine-tuning a GPT-3.5 model. Our results show that fine-tuning GPT-3.5 on a GPT-4o-generated augmented dataset of 520 instances yields statistically meaningful improvements in the identification of praise components. Specifically, the <inline-formula><tex-math>$F_{2}$</tex-math></inline-formula> score increases by 20.5% for effort-based praise and by 17.3% for outcome-based praise relative to the same model fine-tuned without data augmentation. Moreover, the proposed data augmentation approach exhibits effective generalizability to the identification of additional praise categories, specifically person-based praise. When fine-tuned on an augmented dataset of 520 instances, the model achieves a 16.5% improvement in the <inline-formula><tex-math>$F_{2}$</tex-math></inline-formula> score for person-based praise, accompanied by increases of 19.9% in modified intersection over union score and 21.6% in intersection over union score, relative to the same model fine-tuned without data augmentation. These findings suggest that for data-intensive tasks, synthetic data generated through GPT model prompting can substantially enhance fine-tuned model performance in low-resource scenarios. This approach offers a scalable and cost-effective solution to alleviate the reliance on extensive expert-labeled data, enabling broader adoption of automated tutor training systems and facilitating the rapid development of skilled tutors at scale.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"19 ","pages":"166-181"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557988","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":"Xmobot: Enabling Rapid Build-and-Train Robotics Education With Agentic AI","authors":"Yizhi Wang;Minjuan Wang;Degang Xu;John Chi-Kin Lee;Yongfang Xie","doi":"10.1109/TLT.2026.3681123","DOIUrl":"https://doi.org/10.1109/TLT.2026.3681123","url":null,"abstract":"Robotics education increasingly requires learners to connect design choices to measurable task outcomes under realistic engineering constraints. In practice, however, educational robotics laboratories often face limited reusability of build-and-train workflows when robot configurations or task contexts change, while deep reinforcement learning (DRL) further raises the barrier through unstable training dynamics and difficult-to-interpret process traces. This article presents Xmobot, a configurable mobile robot platform and reusable build-and-train workflow for educational robotics, together with a tool-grounded agentic learning-support system for iterative DRL development. The proposed system retrieves standardized evidence from training logs, assesses run quality through an interpretable fuzzy-logic model, uses a constrained large language model (LLM) to generate structured experiment plans and learner-facing explanations, and applies deterministic constraint validation to ensure traceable and recoverable reward-design iteration. It also provides instructor-facing analytics that aggregate run-level evidence and intervention histories into compact diagnostic views for monitoring and feedback. In an IsaacLab mobile navigation study over a fixed ten-run horizon, the full tool-grounded system achieved zero constraint violations, a valid-plan rate of 1.000, reached the predefined task-success criterion by run 9, and obtained the highest final success rate (0.659), outperforming Manual (0.339), LLM-Chat (0.277), C-noFuzzy (0.370), and C-noValidate (0.612) in this experimental setting. These results suggest that the proposed framework supports more dependable and interpretable DRL iteration while also making robotics education more actionable for learner reflection, instructor monitoring, and formative feedback.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"19 ","pages":"376-387"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147828789","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":"Toward Fair and Efficient Intelligent Learning: A Generative Cognitive Diagnosis Approach","authors":"Jiatong Li;Yingying Ni;Minjuan Wang;John Chi-Kin Lee;Qi Liu;Mengxiao Zhu;Zhenya Huang;Shijin Wang;Enhong Chen","doi":"10.1109/TLT.2026.3674350","DOIUrl":"https://doi.org/10.1109/TLT.2026.3674350","url":null,"abstract":"Intelligent learning (iLearning) represents a holistic approach to e-learning that dynamically aligns instructional materials with individual learner needs. Within the iLearning framework, the cognitive diagnosis model (CDM) serves as a cornerstone technology by inferring human cognitive states from diagnostic test response logs. Traditional CDMs typically treat cognitive states as learnable parameters, optimizing them through a score prediction-driven paradigm. However, this approach falls short in the nonidentifiability problem of diagnostic outputs and lacks efficiency in instant diagnosis for incoming learners. These limitations pose a significant challenge to the individual fairness and utility of cognitive assessments. In this study, we introduce a novel generative diagnosis paradigm designed to resolve these limitations. By enabling the inductive inference of cognitive states without the need for parameter reoptimization, our generative approach ensures strict identifiability and addresses the low efficiency of optimization-based diagnosis. We present two streamlined yet powerful instantiations of this paradigm: generative item response theory and the generative neural cognitive diagnosis model. Extensive experiments on real-world datasets confirm that our methodology significantly outperforms traditional models, offering superior scalability and reliability. Ultimately, this generative framework provides a robust foundation for the development of next-generation iLearning environments.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"19 ","pages":"388-403"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11434980","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147937627","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":"SimuNEX: A 3-D Simulation Framework for Teaching Control Systems","authors":"Lee Bissessar;Craig J. Ramlal;Arvind Singh","doi":"10.1109/TLT.2026.3684021","DOIUrl":"https://doi.org/10.1109/TLT.2026.3684021","url":null,"abstract":"Applying project-based learningfor teaching control systems often involves significant complexities related to hardware costs, safety, and preparation. SimuNEX is an open-source and interoperable simulation framework designed to facilitate the teaching and learning of controls engineering in a safe, engaging, and cost-effective manner. SimuNEX provides a 3-D environment that enhances students’ understanding of spatial dynamics and system interactions, often not apparent from current signal and scope-based visual tools. This allows for a quicker and more intuitive debugging and iterative development of control algorithms. The platform supports realistic scenario-based learning and offers immediate visual feedback. This article discusses the design and implementation of SimuNEX, along with two application examples in an educational course on the modern control theory.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"19 ","pages":"418-432"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147937628","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":"SocioMathLLM: A Multimodal Large Language Model Framework for Generating Authentic Mathematical Word Problems Driving Socialization","authors":"Wu-Yuin Hwang;Ika Qutsiati Utami","doi":"10.1109/TLT.2026.3677266","DOIUrl":"https://doi.org/10.1109/TLT.2026.3677266","url":null,"abstract":"Large language models (LLMs) have demonstrated significant advancements in automatic question generation (AQG), contributing to enhanced learning and teaching efficiency by facilitating the creation of human-like educational content and assessment materials. However, in mathematics education, where generating extensive sets of math word problems (MWPs) requires significant time and effort, the use of LLMs to design tasks that foster socialization competencies such as group collaboration, cooperation, negotiation, and ideas-sharing among students remains unexplored. This study makes the first attempt to develop a specialized multimodal LLM framework, SocioMathLLM, which integrates an innovative prompt engineering strategy to incorporate multimodal authentic contextual information (text and image), group background information, and socialization criteria (i.e., group size, task nature, social goal, social frequency, social-quality, interaction quantity, and social interdependency) to generate MWPs aimed at fostering group socialization. SocioMathLLM was evaluated through automatic metrics, human evaluation, and LLM-based evaluation. The automatic evaluation result demonstrates good performance in generating MWPs that promote socialization with an average precision of 0.906, recall of 0.943, and F1 score of 0.925. A total of 100 evaluation cases by humans and LLM also validated the robustness of SocioMathLLM, highlighting its potential for application in collaborative mathematics learning and assessment contexts.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"19 ","pages":"271-285"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147665343","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 Systematic Literature Review on Artificial Intelligence Use in Personalized Learning","authors":"Antun Drobnjak;Barbi Svetec;Blazenka Divjak;Ivica Boticki","doi":"10.1109/TLT.2026.3711814","DOIUrl":"https://doi.org/10.1109/TLT.2026.3711814","url":null,"abstract":"Personalized learning is nowadays frequently associated with the use of artificial intelligence (AI). This articlepresents a systematic literature review focused on personalized learning supported by AI-based technologies, discussed through the lenses of the Activity Theory. The dataset combines manually selected studies until December 2024 with a supplementary set extending to October 2025. The article explores the dialectic relationship between the subject and the object of an activity mediated by artifacts. The purpose of the research is to analyze current trends, technologies, theoretical approaches, and applications of AI in personalized learning. The review is focused on methodologically sound studies that use AI methods and algorithms to support learners, including evaluation procedures with concrete end-users and reasonable sample sizes. The discussion addresses the key aims of personalized learning supported by AI, the paradigmatic shift in personalization with the rapid development of AI, its role in ensuring flexible learning paths, and the main areas of application. It also covers challenges related to data quality, pedagogical soundness, and technology-user interaction. This technological transformation is shifting traditional educational approaches toward more adaptive and learner-driven models.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"19 ","pages":"711-727"},"PeriodicalIF":7.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11601059","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148626706","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":"Semantic Alignment With an AI-Derived Reference in Relation to Children’s Scientific Creativity","authors":"Zheng Wang;Minjuan Wang;Xuefan Li;Weiping Hu","doi":"10.1109/TLT.2026.3728788","DOIUrl":"https://doi.org/10.1109/TLT.2026.3728788","url":null,"abstract":"As generative artificial intelligence (AI) becomes increasingly visible in education, an important question concerns how AI-related knowledge structures are associated with children’s scientific creativity. Prior studies have mainly examined this issue through performance outcomes. In contrast, this study focused on knowledge representation. We examined whether children’s scientific creativity was related to the similarity between their semantic organization and an AI-derived semantic reference. A total of 125 sixth-grade students completed a scientific questioning task and a word-association task. In parallel, we generated responses from 120 AI agents based on six contemporary large language models to construct an aggregated AI semantic reference. Individual semantic networks were derived from the word-association data, and representational similarity analysis was used to estimate each child’s alignment with the AI reference. The results showed a nonlinear relationship. Originality followed an inverted-U pattern, whereas the composite creativity index showed a peak followed by a plateau. In both cases, the highest performance was observed at intermediate levels of alignment. As a contextual comparison, AI agents showed higher originality but lower flexibility than children, and their semantic networks were sparser and more modular. These findings indicate that scientific creativity was highest at intermediate levels of alignment with the AI-derived semantic reference. At higher levels of alignment, originality declined, whereas composite creativity plateaued. The study provides a representational perspective on the nonlinear association between children’s semantic organization and scientific creativity.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"19 ","pages":"890-906"},"PeriodicalIF":7.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133791","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 Dynamic Eye-Movement Alignment Method for Assessing Context-Based Mathematical Problem-Solving","authors":"Sijia Hao;Huanghe Pan;Pei Sun;Jingjing Chen;Dan Zhang","doi":"10.1109/TLT.2026.3688724","DOIUrl":"https://doi.org/10.1109/TLT.2026.3688724","url":null,"abstract":"Assessing how students solve context-based mathematical problems is challenging because outcome measures, such as problem scores, provide limited insight into the cognitive processes involved. Although methods such as think-aloud protocols and retrospective reports can provide insight into students’ problem-solving strategies, they are subjective and only provide a broad overview of the temporal dynamics. This limitation underscores the need for more objective and fine-grained process measures. To address this gap, the present study introduces <italic>intersubject eye-movement trajectory alignment</i>, the similarity between a student’s gaze sequence and those of high-performing students, as a process-oriented indicator of problem-solving performance. To validate this approach, 46 undergraduates solved six context-based mathematical problems with paper and pencil while wearing eye-tracking glasses. Higher alignment was related to better immediate problem-solving performance and remained significantly associated with general mathematical ability even after controlling for immediate performance. Temporal analyses further showed that alignment captures different cognitive processes across phases of problem-solving: early-phase alignment showed the strongest association with immediate problem-solving performance, whereas midphase alignment was more strongly related to general mathematical ability. Overall, the proposed alignment measure offers an objective and temporally sensitive way to characterize students’ self-paced problem-solving processes. These findings suggest that the alignment measure has potential value for developing process-oriented educational interventions.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"19 ","pages":"447-461"},"PeriodicalIF":4.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148003462","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}