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Ronak R. Mohanty; Peter Selly; Lindsey Brenner; Shantanu Vyas; Cassidy R. Nelson; Jason B. Moats; Joseph L. Gabbard; Ranjana K. Mehta – IEEE Transactions on Learning Technologies, 2025
Immersive extended reality (XR) technologies, including augmented reality (AR), virtual reality, and mixed reality, are transforming the landscape of education and training through experiences that promote skill acquisition and enhance memory retention. These technologies have notably improved decision making and situational awareness in public…
Descriptors: Technology Integration, Artificial Intelligence, Safety Education, Instructional Design
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Sheng Bi; Zeyi Miao; Qizhi Min – IEEE Transactions on Learning Technologies, 2025
The objective of question generation from knowledge graphs (KGQG) is to create coherent and answerable questions from a given subgraph and a specified answer entity. KGQG has garnered significant attention due to its pivotal role in enhancing online education. Encoder-decoder architectures have advanced traditional KGQG approaches. However, these…
Descriptors: Grammar, Models, Questioning Techniques, Graphs
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Nicolas Pope; Juho Kahila; Henriikka Vartiainen; Matti Tedre – IEEE Transactions on Learning Technologies, 2025
The rapid advancement of artificial intelligence and its increasing societal impacts have turned many computing educators' focus toward early education in machine learning (ML). Limited options for educational tools for teaching novice learners about the mechanisms of ML and data-driven systems presents a recognized challenge in K-12 computing…
Descriptors: Artificial Intelligence, Computer Oriented Programs, Computer Science Education, Grade 4
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Xueqiao Zhang; Chao Zhang; Jianwen Sun; Jun Xiao; Yi Yang; Yawei Luo – IEEE Transactions on Learning Technologies, 2025
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) customized generation: generating niche-targeted teaching content based on…
Descriptors: Artificial Intelligence, Instructional Design, Technology Uses in Education, Cognitive Ability
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Nan Xie; Zhengxu Li; Haipeng Lu; Wei Pang; Jiayin Song; Beier Lu – IEEE Transactions on Learning Technologies, 2025
Classroom engagement is a critical factor for evaluating students' learning outcomes and teachers' instructional strategies. Traditional methods for detecting classroom engagement, such as coding and questionnaires, are often limited by delays, subjectivity, and external interference. While some neural network models have been proposed to detect…
Descriptors: Learner Engagement, Artificial Intelligence, Technology Uses in Education, Educational Technology
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Deliang Wang; Yaqian Zheng; Jinjiang Li; Gaowei Chen – IEEE Transactions on Learning Technologies, 2025
Researchers have increasingly utilized artificial intelligence to automatically analyze classroom dialogue, aiming to provide timely feedback to teachers due to its educational significance. However, traditional machine learning and deep learning models face challenges, such as limited performance and lack of generalizability, across various…
Descriptors: Classroom Communication, Computational Linguistics, Cues, Generalization
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Ahmad Chaddad; Yuchen Jiang – IEEE Transactions on Learning Technologies, 2025
The concept of the Metaverse, viewed as the ultimate manifestation of the Internet, has gained significant attention due to rapid advances in technologies such as the Internet of Things (IoT) and blockchain. Acting as a bridge between the physical and virtual worlds, the Metaverse has the potential to offer remarkable experiences to its users.…
Descriptors: Internet, Medical Education, Instructional Effectiveness, Artificial Intelligence
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Sonsoles Lopez-Pernas; Kamila Misiejuk; Rogers Kaliisa; Mohammed Saqr – IEEE Transactions on Learning Technologies, 2025
Despite the growing use of large language models (LLMs) in educational contexts, there is no evidence on how these can be operationalized by students to generate custom datasets suitable for teaching and learning. Moreover, in the context of network science, little is known about whether LLMs can replicate real-life network properties. This study…
Descriptors: Students, Artificial Intelligence, Man Machine Systems, Interaction
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Weijiao Huang; Khe Foon Hew – IEEE Transactions on Learning Technologies, 2025
In an online learning environment, both instruction and assessments take place virtually where students are primarily responsible for managing their own learning. This requires a high level of self-regulation from students. Many online students, however, lack self-regulation skills and are ill-prepared for autonomous learning, which can cause…
Descriptors: Independent Study, Interpersonal Relationship, Electronic Learning, Computer Software
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Kaitlyn Tracy; Ourania Spantidi – IEEE Transactions on Learning Technologies, 2025
Virtual reality (VR) has emerged as a transformative educational tool, enabling immersive learning environments that promote student engagement and understanding of complex concepts. However, despite the growing adoption of VR in education, there remains a significant gap in research exploring how generative artificial intelligence (AI), such as…
Descriptors: Artificial Intelligence, Computer Assisted Instruction, Computer Simulation, Educational Technology
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Xiuling He; Ruijie Zhou; Qiong Fan; Xiong Xiao; Ying Yu; Zhonghua Yan – IEEE Transactions on Learning Technologies, 2025
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…
Descriptors: Student Teachers, Student Teaching, Professional Development, Mentors
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Liuying Gong; Jingyuan Chen; Fei Wu – IEEE Transactions on Learning Technologies, 2025
The capabilities of large language models (LLMs) in language comprehension, conversational interaction, and content generation have led to their widespread adoption across various educational stages and contexts. Given the fundamental role of education, concerns are rising about whether LLMs can serve as competent teachers. To address the…
Descriptors: Artificial Intelligence, Computer Software, Computational Linguistics, Comparative Analysis
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Xuefan Li; Marco Zappatore; Tingsong Li; Weiwei Zhang; Sining Tao; Xiaoqing Wei; Xiaoxu Zhou; Naiqing Guan; Anny Chan – IEEE Transactions on Learning Technologies, 2025
The integration of generative artificial intelligence (GAI) into educational settings offers unprecedented opportunities to enhance the efficiency of teaching and the effectiveness of learning, particularly within online platforms. This study evaluates the development and application of a customized GAI-powered teaching assistant, trained…
Descriptors: Artificial Intelligence, Technology Uses in Education, Student Evaluation, Academic Achievement
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Jialun Pan; Zhanzhan Zhao; Dongkun Han – IEEE Transactions on Learning Technologies, 2025
Properly predicting students' academic performance is crucial for elevating educational outcomes in various disciplines. Through precise performance prediction, schools can quickly pinpoint students facing challenges and provide customized educational materials suited to their specific learning needs. The reliance on teachers' experience to…
Descriptors: Prediction, Academic Achievement, At Risk Students, Artificial Intelligence
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Hao Zhou; Wenge Rong; Jianfei Zhang; Qing Sun; Yuanxin Ouyang; Zhang Xiong – IEEE Transactions on Learning Technologies, 2025
Knowledge tracing (KT) aims to predict students' future performances based on their former exercises and additional information in educational settings. KT has received significant attention since it facilitates personalized experiences in educational situations. Simultaneously, the autoregressive (AR) modeling on the sequence of former exercises…
Descriptors: Learning Experience, Academic Achievement, Data, Artificial Intelligence
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