IEEE Transactions on Learning Technologies最新文献

Empowering Preservice Teachers Through Textbook Design Activities With GAI-Based Chatbot 基于ai的聊天机器人通过教科书设计活动赋予职前教师权力

IF 4.9 3区教育学

IEEE Transactions on Learning Technologies Pub Date : 2025-09-05 DOI: 10.1109/TLT.2025.3606757

Jiutong Luo;Chunying Zhu;Lixin Hu;Meng Sun

引用次数: 0

Building Trust in AI Through Dialogues With Eastern Ethics: Toward Ethical Partnerships in Education 通过与东方伦理的对话建立对人工智能的信任：走向教育中的伦理伙伴关系

IF 4.9 3区教育学

IEEE Transactions on Learning Technologies Pub Date : 2025-09-01 DOI: 10.1109/TLT.2025.3604616

John Chi-Kin Lee;Chris Dede;Minjuan Wang;Xuefan Li

{"title":"Building Trust in AI Through Dialogues With Eastern Ethics: Toward Ethical Partnerships in Education","authors":"John Chi-Kin Lee;Chris Dede;Minjuan Wang;Xuefan Li","doi":"10.1109/TLT.2025.3604616","DOIUrl":"https://doi.org/10.1109/TLT.2025.3604616","url":null,"abstract":"This article proposes a novel framework for ethical human–artificial intelligence (AI) partnerships in education by integrating Eastern ethics (with Chinese ethics as an example), intelligence augmentation, and agentic AI design. Moving beyond the dominant Western paradigm, the study draws from Confucian and Daoist principles—such as relational trust, coagency, and moral cultivation—to envision AI as an ethical partner, not just a tool. It addresses two key questions: How can trust in AI be cultivated in education? and when can AI be ethically considered a collaborator? The authors introduce a triadic model combining normative grounding, cognitive scaffolding, and system-level design, operationalized through culturally sensitive platforms, pedagogy, and ethical interaction. They also propose a three-tiered evaluation system: learner trust metrics, educator audits, and AI reflexivity protocols. This interdisciplinary synthesis provides a scalable culturally rooted pathway for designing AI systems that are pedagogically meaningful, ethically adaptive, and co-constructive—contributing to more equitable and morally resonant educational futures.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"833-841"},"PeriodicalIF":4.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145141691","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

Quantum Algorithm Design and Its Implementation for Solving Test Sheet Composition Optimization Using a Quantum Annealing Approach 利用量子退火方法求解考卷组成优化的量子算法设计与实现

IF 4.9 3区教育学

IEEE Transactions on Learning Technologies Pub Date : 2025-09-01 DOI: 10.1109/TLT.2025.3604522

Chu-Fu Wang;Yih-Kai Lin;Ling Cheng

{"title":"Quantum Algorithm Design and Its Implementation for Solving Test Sheet Composition Optimization Using a Quantum Annealing Approach","authors":"Chu-Fu Wang;Yih-Kai Lin;Ling Cheng","doi":"10.1109/TLT.2025.3604522","DOIUrl":"https://doi.org/10.1109/TLT.2025.3604522","url":null,"abstract":"In testing systems, the item response theory is a widely used model for accurately synthesizing user response information. However, compared to classical test theory approaches, it imposes a higher computational burden and increases the system design complexity. Quantum computing has shown promise in alleviating these computational challenges. Currently, general-purpose quantum computers are still in a relatively early stage of development. However, special-purpose quantum computing architectures have been designed to solve combinatorial optimization problems, attracting significant attention across various fields. These systems enable researchers to tackle domain-specific optimization problems with reduced computational time. To the best of our knowledge, no applications of quantum computing have been proposed in the field of educational technology. This study, therefore, aimed to design a quantum quadratic unconstrained binary optimization formulation for optimizing test sheet composition. The proposed model can be implemented on practical quantum Ising machines (or digital quantum Ising machines for larger qubit usage) to evaluate system efficiency. Simulation results demonstrate that the proposed approach outperforms traditional methods, including the genetic algorithm and particle swarm optimization algorithm, in terms of computational efficiency.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"842-855"},"PeriodicalIF":4.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145141690","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

AI-Powered University Admission Counseling: A Use Case of Large Language Models in Student Guidance 基于人工智能的大学入学咨询：大型语言模型在学生指导中的用例

IF 4.9 3区教育学

IEEE Transactions on Learning Technologies Pub Date : 2025-08-29 DOI: 10.1109/TLT.2025.3604096

Nguyen Nang Hung Van;Phuc Hao Do;Van Nam Hoang;Truc Thi Kim Nguyen;Minh Tuan Pham

{"title":"AI-Powered University Admission Counseling: A Use Case of Large Language Models in Student Guidance","authors":"Nguyen Nang Hung Van;Phuc Hao Do;Van Nam Hoang;Truc Thi Kim Nguyen;Minh Tuan Pham","doi":"10.1109/TLT.2025.3604096","DOIUrl":"https://doi.org/10.1109/TLT.2025.3604096","url":null,"abstract":"This study investigates how technical advances in large language models (LLMs) translate into measurable educational benefit. University admission counseling plays a crucial role in helping prospective students make their higher education decisions. However, traditional advisory methods are constrained by issues, such as limited scalability, personalization, and the ability to handle large volumes of inquiries. With the growing need for real-time assistance, artificial intelligence (AI), particularly LLMs), presents a promising solution to these challenges. This article introduces an AI-driven university admission counseling system that automates routine inquiries, personalizes guidance, and improves accessibility. We develop a formal mathematical framework to represent the counseling task, using embedded and similarity metrics to assess the compatibility of student profiles with academic programs. The system incorporates a multistage workflow for efficient data processing, embedded generation, and AI-driven recommendation. We evaluated the performance of several LLMs, namely, eLLAMA, eGPT, and eDEEPSEEK, through retrieval-augmented generation, measuring output quality with natural language processing metrics, such as bilingual evaluation understudy, recall-oriented understudy for gisting evaluation, METEOR, and BERTScore. Our results demonstrate that LLMs can significantly improve the efficiency and quality of admission counseling, providing a scalable and adaptable solution that demonstrably enhances student confidence and decision quality.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"856-868"},"PeriodicalIF":4.9,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145141689","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

Reinforcement Learning-Driven Optimization of Picture Book Paths for Aesthetic Perception Enhancement 增强美感的绘本路径强化学习驱动优化

IF 4.9 3区教育学

IEEE Transactions on Learning Technologies Pub Date : 2025-08-19 DOI: 10.1109/TLT.2025.3600112

Ye Zhang;Mo Wang;Jinlong He;Yupeng Zhou;Hongping Wu;Zhaoyang Sun;Yujie Zhang;Minghao Yin

{"title":"Reinforcement Learning-Driven Optimization of Picture Book Paths for Aesthetic Perception Enhancement","authors":"Ye Zhang;Mo Wang;Jinlong He;Yupeng Zhou;Hongping Wu;Zhaoyang Sun;Yujie Zhang;Minghao Yin","doi":"10.1109/TLT.2025.3600112","DOIUrl":"https://doi.org/10.1109/TLT.2025.3600112","url":null,"abstract":"Aesthetic perception, as a core competence in art education, fosters students’ cultural sensibility, emotional expression, and critical thinking. However, existing approaches to cultivating aesthetic perception often lack systematic guidance and personalized developmental pathways, limiting their capacity to support sustained and individualized growth. Two central challenges remain unresolved: first, how to effectively model the dynamic, multidimensional progression of students’ aesthetic understanding, and second, how to construct coherent learning paths that guide students from basic perceptual awareness to more abstract artistic engagement. To address these issues, we propose AesthPath a reinforcement learning-based recommendation model that constructs personalized picture book learning paths to enhance aesthetic perception. Specifically, the model introduces a Markov decision process formulation that captures the evolving states of learners’ aesthetic competence across multiple dimensions. An actor–critic algorithm is then employed to generate adaptive learning trajectories by balancing exploration of new content with the reinforcement of effective materials, based on ongoing learner feedback. Unlike traditional static or rule-based recommendation methods, AesthPath supports fine-grained, feedback-driven optimization of learning trajectories, facilitating goal-oriented and personalized development of aesthetic perception. Experimental results on a real-world dataset demonstrate the effectiveness of AesthPath in enhancing students’ aesthetic understanding. This study offers new theoretical and methodological insights for intelligent learning path design and educational recommendations, highlighting the potential of reinforcement learning in adaptive learning scenarios.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"798-811"},"PeriodicalIF":4.9,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021155","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

ProgTutor: A Robotic-Based Framework to Support Teaching and Learning of Programming Fundamentals 一个基于机器人的框架来支持编程基础的教学和学习

IF 4.9 3区教育学

IEEE Transactions on Learning Technologies Pub Date : 2025-08-12 DOI: 10.1109/TLT.2025.3598041

Javier Ortega-Morla;Antonio Leis;Alma Mallo;Laura Morán-Fernández;Sara Guerreiro;Alejandro Paz-López;Beatriz Pérez-Sánchez;Noelia Sánchez-Maroño;Alejandro Rodríguez-Arias;Oscar Fontenla-Romero;Francisco Bellas

{"title":"ProgTutor: A Robotic-Based Framework to Support Teaching and Learning of Programming Fundamentals","authors":"Javier Ortega-Morla;Antonio Leis;Alma Mallo;Laura Morán-Fernández;Sara Guerreiro;Alejandro Paz-López;Beatriz Pérez-Sánchez;Noelia Sánchez-Maroño;Alejandro Rodríguez-Arias;Oscar Fontenla-Romero;Francisco Bellas","doi":"10.1109/TLT.2025.3598041","DOIUrl":"https://doi.org/10.1109/TLT.2025.3598041","url":null,"abstract":"The initial version of ProgTutor, a learning framework designed to teach the fundamentals of computer programming in a personalized and applied manner, is presented here. The main contribution of this tool is the integration of an adaptive learning system with a 3-D robotic simulator, used to face realistic challenges in a user-friendly fashion. ProgTutor provides automated evaluations and feedback on coding errors, ensuring that learners receive the support they need to progress effectively. In addition, it features dynamic learning paths tailored to each student’s pace, offloading tasks such as automated evaluation and adaptive sequencing to the tool so that students and teachers can concentrate on judgment. ProgTutor also enhances the teachers’ capacities as educators, as they can focus their attention on those students with more learning difficulties. Therefore, it functions as intelligence augmentation rather than automation, with teachers remaining in the decision loop. This article introduces the conceptual and functional design of ProgTutor, and it includes piloting results with high school students during the academic course 2023–2024, focused on their acceptability of the tool and on the analysis of the real impact that this type of system could have over the formal educational landscape in the future.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"783-797"},"PeriodicalIF":4.9,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11122894","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144909334","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

From Hesitant Beginners to Confident Experts: Profiles and Predictors of AI Literacy Among Preschool Teachers in Guangxi, China 从犹豫不决的初学者到自信的专家：广西幼儿教师的人工智能素养概况和预测因素

IF 4.9 3区教育学

IEEE Transactions on Learning Technologies Pub Date : 2025-08-05 DOI: 10.1109/TLT.2025.3596125

Xi Bei Xiong;Simin Cao;Tianhang Gao;Xiya Feng;Hui Li

{"title":"From Hesitant Beginners to Confident Experts: Profiles and Predictors of AI Literacy Among Preschool Teachers in Guangxi, China","authors":"Xi Bei Xiong;Simin Cao;Tianhang Gao;Xiya Feng;Hui Li","doi":"10.1109/TLT.2025.3596125","DOIUrl":"https://doi.org/10.1109/TLT.2025.3596125","url":null,"abstract":"This study examined early childhood teachers’ artificial intelligence (AI) literacy in Guangxi, China. Utilizing data from 1522 kindergarten teachers, we developed and validated a culturally adapted AI literacy scale through factor analyses, confirming a three-construct structure: Safety, Attitude, and Capability. Latent profile analysis identified three distinct teacher profiles: “Hesitant Beginners” (9.6%), “Enthusiastic Practitioners” (64.2%), and “Confident Experts” (26.2%), revealing significant heterogeneity. Teachers generally exhibited positive attitudes toward AI but lower safety awareness and capability levels. Regression analyses indicated that education level, working experience (negatively associated), kindergarten type, and geographic location (urban/rural) significantly influence AI literacy levels and profile membership. These findings underscore the need for context-specific assessment tools and tailored teacher education programs to enhance their digital literacy and promote equitable AI integration in Chinese early childhood education.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"812-821"},"PeriodicalIF":4.9,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145078678","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

Enhancing IV Needle Insertion Training With a Bimanual Haptic VR Simulator: Development, Usability, and Learning Impact 增强静脉针插入训练与双手触觉VR模拟器：发展，可用性和学习的影响

IF 4.9 3区教育学

IEEE Transactions on Learning Technologies Pub Date : 2025-07-24 DOI: 10.1109/TLT.2025.3592579

Jarrett E. Woo;Jin Woo Kim;Kwangtaek Kim;Jeremy M. Jarzembak;Ann James;Jennifer Biggs;John Dunlosky;Robert Clements

{"title":"Enhancing IV Needle Insertion Training With a Bimanual Haptic VR Simulator: Development, Usability, and Learning Impact","authors":"Jarrett E. Woo;Jin Woo Kim;Kwangtaek Kim;Jeremy M. Jarzembak;Ann James;Jennifer Biggs;John Dunlosky;Robert Clements","doi":"10.1109/TLT.2025.3592579","DOIUrl":"https://doi.org/10.1109/TLT.2025.3592579","url":null,"abstract":"Training healthcare professionals in intravenous (IV) needle insertion is a critical component of medical education, traditionally relying on manikin-based simulations and real-life practice. However, the advent of haptic virtual reality (HVR) technologies offers a transformative approach to this training, enhancing safety and potential efficiency. This study explores the development of an IV needle insertion simulator using two different haptic devices integrated into a VR system on the Unity platform and assesses its impact on learning through a three-week experiment. The simulator is designed to create a realistic and immersive training environment by incorporating detailed anatomical models, physics-based hand interactions, and real-time haptic feedback. The virtual environment replicates a clinical setting, featuring a patient arm model and an IV needle. The haptic feedback is programmed to offer realistic feelings of needle insertion and hand grasping, improving the user’s accuracy. Learning impact and usability testing with 41 students indicate a promising improvement in skill acquisition and confidence. Specifically, participants showed a 55% increase in success rates and a significant boost in confidence. This high-fidelity HVR simulator represents a significant step forward in medical training technologies, offering a scalable and repeatable training tool adaptable to various educational needs and skill levels.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"769-782"},"PeriodicalIF":4.9,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144868433","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

Educational Psychology-Empowered Personalized Learning Path Generation Strategy 教育心理学授权的个性化学习路径生成策略

IF 4.9 3区教育学

IEEE Transactions on Learning Technologies Pub Date : 2025-07-18 DOI: 10.1109/TLT.2025.3590602

Xin Wei;Wenrui Han;Shiyun Sun;Junhao Shan;Liang Zhou

{"title":"Educational Psychology-Empowered Personalized Learning Path Generation Strategy","authors":"Xin Wei;Wenrui Han;Shiyun Sun;Junhao Shan;Liang Zhou","doi":"10.1109/TLT.2025.3590602","DOIUrl":"https://doi.org/10.1109/TLT.2025.3590602","url":null,"abstract":"In e-learning, high-quality learning path generation can meet learners’ personalized demands and solve their cognitive disorientation dilemma. However, existing learning path generation schemes still have challenges, such as focusing solely on one aspect of the learner’s characteristics or the structure of learning content, difficulty in describing the variation in a learner’s knowledge level, and a lack of interpretability. To address these issues, in this article, we propose an educational psychology-empowered personalized learning path generation strategy. First, inspired by Brown’s decay theory of immediate memory, we design the decay attentive knowledge tracing approach for assessing a learner’s knowledge level. Then, motivated by Bruner’s cognitive structure learning theory, we present search space optimization for selecting the learning content candidate set. Finally, enlightened by Posner’s conceptual change model, we impose multiple rule constraints on the matching process of the learner’s knowledge level and the learning content in the candidate set, gradually forming the personalized learning path. Experimental results demonstrate the performance of the proposed strategy for guaranteeing the reasonableness of learning content organization and enhancing the learner’s knowledge level. Moreover, the actual utilization of the proposed strategy in higher education course instruction shows its effectiveness in improving learning outcomes, motivation, and engagement.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"741-756"},"PeriodicalIF":4.9,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144739961","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

Collaborative Human–AI Research Practices: Identifying Critical Touchpoints for Human Intervention in Educational Research 人机协作研究实践：确定教育研究中人类干预的关键接触点

IF 2.9 3区教育学

IEEE Transactions on Learning Technologies Pub Date : 2025-07-10 DOI: 10.1109/TLT.2025.3587488

Ecem Kopuz;Galip Kartal

{"title":"Collaborative Human–AI Research Practices: Identifying Critical Touchpoints for Human Intervention in Educational Research","authors":"Ecem Kopuz;Galip Kartal","doi":"10.1109/TLT.2025.3587488","DOIUrl":"https://doi.org/10.1109/TLT.2025.3587488","url":null,"abstract":"This study investigates how educational researchers integrate artificial intelligence (AI) tools into their workflows, with a focus on balancing automation and human judgment. The study, which provides a mixed method approach with a survey and interview questions, utilized an international sample of 65 educational research fields. The findings reveal that AI-supported tools help reduce the burden while carrying out research processes, so that more time can be spent on basic and innovative activities. In addition, ethical and practical guidelines have emerged on how to optimize human–AI collaboration. It has been determined which tools researchers use and how. This study attempts to explain how AI can be effectively integrated with human intelligence. Considering this, it emphasizes the need to create strong policies and standards on the use of AI, to raise awareness of users about technology use, and to ensure that ethical practices are observed. This article offers a roadmap outlining which AI tools can be used and in what ways. It also makes significant contributions to the literature in this field by emphasizing the indispensable importance of human intervention in intelligence-supported education research.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"732-740"},"PeriodicalIF":2.9,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680846","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