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Xiaorui Wang; Chao Liu; Jing Guo – International Journal of Web-Based Learning and Teaching Technologies, 2025
This research works on creating a hybrid Knowledge Recommendation System (KRS) for an Entrepreneurship Course using the Knowledge Graph (KG) and Clustering Technologies (CTs). The system aims at improving students' learning experience by providing relevant learning materials and even focusing on learner preferences. These results are already part…
Descriptors: Entrepreneurship, Individualized Instruction, Learning Experience, Feedback (Response)
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Joanna Vance – Journal of Faculty Development, 2025
This article explores how Los Angeles Pacific University (LAPU) uses its AI tool, Spark, to enhance student learning. Spark personalizes the learning experience, offers 24/7 tutoring, and fosters collaboration, leading to improved academic performance. The tool complements traditional teaching, providing equitable, accessible support to students…
Descriptors: Artificial Intelligence, Technology Uses in Education, Cooperation, Individualized Instruction
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Fatema Al Nabhani; Mahizer Bin Hamzah; Hassan Abuhassna – Contemporary Educational Technology, 2025
This study sought to investigate the effects of employing artificial intelligence (AI) on the customization of educational content and the enhancement of academic performance and engagement among students and teachers. The research involved a sample of ninth-grade students and their educators from diverse subjects, utilizing questionnaires to…
Descriptors: Artificial Intelligence, Technology Uses in Education, Individualized Instruction, Learning Experience
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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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Thiemo Wambsganss; Ivo Benke; Alexander Maedche; Kenneth Koedinger; Tanja Käser – International Journal of Artificial Intelligence in Education, 2025
Conversational tutoring systems (CTSs) offer a promising avenue for individualized learning support, especially in domains like persuasive writing. Although these systems have the potential to enhance the learning process, the specific role of learner control and inter- activity within them remains underexplored. This paper introduces…
Descriptors: Learner Controlled Instruction, Interaction, Intelligent Tutoring Systems, Persuasive Discourse
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Hon Keung Yau; Ka Fai Tung – Turkish Online Journal of Educational Technology - TOJET, 2025
This study explores the development and evaluation of a chatbot model designed to facilitate learning within a department of a university. The project aims to enhance the learning experience by incorporating customized data into the chatbot's knowledge base, enabling personalized and context-aware interactions. The research investigates the…
Descriptors: Instructional Effectiveness, Artificial Intelligence, Computer Software, Technology Integration
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Sonia J. Ferns; Karsten E. Zegwaard; T. Judene Pretti; Anna D. Rowe – Higher Education Research and Development, 2025
The scope of work-integrated learning (WIL) has expanded and evolved globally and is a recognised pedagogy that enhances graduate employability, strengthens students' personal attributes, and affords a personalised learning experience. Despite abundant research and discourse on WIL, misconceptions about what WIL is and how WIL educative…
Descriptors: Curriculum Design, Work Based Learning, Stakeholders, Global Approach
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Nagaletchimee Annamalai; Darwalis Sazan – Educational Process: International Journal, 2025
Background/purpose: This study examines integrating the VARK learning style model into Learning Management Systems (LMS) to create more personalized learning experiences. The VARK model classifies the learners as Visual, Auditory, Reading, Writing, or Kinesthetic, enabling tailored instructional strategies. Materials/methods: By employing an…
Descriptors: Student Attitudes, Preferences, Cognitive Style, Learning Strategies
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Hajar Majjate; Youssra Bellarhmouch; Adil Jeghal; Ali Yahyaouy; Hamid Tairi; Khalid Alaoui Zidani – Education and Information Technologies, 2025
In recent times, there has been a growing interest in enhancing recommendation systems for e-learning platforms to deliver a personalised learning experience that meets each learner's distinct requirements and preferences. Nevertheless, it is crucial to recognise the ethical considerations surrounding this technology, as it heavily relies on…
Descriptors: Ethics, Artificial Intelligence, Technology Uses in Education, Student Attitudes
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Haruna Abe; Kay Colthorpe; Pedro Isaias – Discover Education, 2025
To improve the online learning experience, adaptive learning technologies are being used to personalise learning content to suit individual learning needs, with learning analytics being integrated to collect data about the student usage behaviour on the platform. Research indicates that the adaptive learning platforms promote a supportive learning…
Descriptors: Physiology, Science Instruction, Instructional Design, Learning Management Systems
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Hafiz Muhammad Ihsan Zafeer; Samra Maqbool; Yu Rong; Sufyan Maqbool – International Journal of Technology in Education and Science, 2025
The integration of digital learning tools in school science classes has garnered significant attention, prompting an investigation into their effects on student engagement and achievement. This study examines the impact of students' access to technology, teachers' digital competency, and the frequent use of digital tools on the engagement and…
Descriptors: Science Achievement, Science Instruction, Middle School Students, Science Teachers
Project Tomorrow, 2025
In support of new local and state discussions about how to most effectively utilize technology in the classroom, Project Tomorrow, in collaboration with Spectrum Business®, is publishing a new series of reports that examine each of the three digital divides (the access, design and use divides) through the lens of the Speak Up Research findings.…
Descriptors: Technology Uses in Education, Access to Computers, Learning Processes, Educational Technology
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Emily L. Dietrich; Sean C. McWatt – Anatomical Sciences Education, 2025
Alternative assessment approaches, such as pass/fail and feedback-based designs, aim to reduce academic stress and foster deeper learning. Few studies have examined feedback-based evaluation in formative settings in medical education, but none among graduate anatomy students. This exploratory study investigated the impact of feedback-based versus…
Descriptors: Graduate Study, Anatomy, Alternative Assessment, Grading
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Wu Xiaofan; Nagaletchimee Annamalai – Contemporary Educational Technology, 2025
This investigation utilized a phenomenological approach to investigate the experience of English language educators in employing artificial intelligence (AI) tools into English language learning. The study used purposive sampling and 20 participants were interviewed. The data analysis was guided by Bronfenbrenner's (1979) ecological systems…
Descriptors: Technology Uses in Education, Artificial Intelligence, Educational Technology, Second Language Learning