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Zhiwei Liu; Haode Zuo; Yongjing Lu – Journal of Computer Assisted Learning, 2025
Background: ChatGPT, a generative artificial intelligence (GenAI) chatbot, has gained significant traction as a tool for supporting students learning. Despite its growing popularity, there is still no academic consensus on its effectiveness in enhancing students' academic achievement. Objectives: This study aims to explore the effect of ChatGPT on…
Descriptors: Artificial Intelligence, Technology Uses in Education, Academic Achievement, Meta Analysis
Carl Boel; Tijs Rotsaert; Martin Valcke; Tammy Schellens – Journal of Computer Assisted Learning, 2025
Background: As immersive virtual reality (IVR) is increasingly being used by teachers worldwide, it becomes pressing to investigate how this technology can foster learning processes. Several authors have pointed to this need, as results on the effectiveness of IVR for learning are still inconclusive. Objectives: To address this gap, we first…
Descriptors: Artificial Intelligence, Computer Simulation, Learning Strategies, Middle School Students
Jia-Hua Zhao; Shu-Tao Shangguan; Ying Wang – Journal of Computer Assisted Learning, 2025
Background: Computational thinking (CT) is a fundamental ability required of individuals in the 21st-century digital world. Past studies show that generative artificial intelligence (GenAI) can enhance students' CT skills. However, GenAI may produce inaccurate output, and students who rely too much on AI may learn little and be unable to think…
Descriptors: Artificial Intelligence, Technology Uses in Education, Skill Development, Computation
Minkai Wang; Jingdong Zhu; Gwo-Jen Hwang; Shao-Chen Chang; Qi-Fan Yang; Di Zhang – Journal of Computer Assisted Learning, 2025
Background: STEM education aims to develop innovation and problem-solving skills through interdisciplinary learning, yet struggles to foster student engagement and interdisciplinary thinking. Whilst alternate reality games (ARGs) can boost motivation via game-based problem-solving, integrating large language models (LLMs) remains underexplored.…
Descriptors: Learner Engagement, STEM Education, Natural Language Processing, Artificial Intelligence
Pieter Vanneste; Kim Dekeyser; Luis Alberto Pinos Ullauri; Dries Debeer; Frederik Cornillie; Fien Depaepe; Annelies Raes; Wim Van den Noortgate; Sameh Said-Metwaly – Journal of Computer Assisted Learning, 2024
Background: Augmented reality (AR) is receiving increasing interest as a tool to create an interactive and motivating learning environment. Yet, it is unclear how instructional support affects performance in AR. Objectives: This study sought to explore how varying the instructional support in AR can affect performance-related behaviours of…
Descriptors: Computer Simulation, Artificial Intelligence, Cognitive Ability, Student Behavior
Ndudi O. Ezeamuzie; Jessica S. C. Leung; Dennis C. L. Fung; Mercy N. Ezeamuzie – Journal of Computer Assisted Learning, 2024
Background: Computational thinking is derived from arguments that the underlying practices in computer science augment problem-solving. Most studies investigated computational thinking development as a function of learners' factors, instructional strategies and learning environment. However, the influence of the wider community such as educational…
Descriptors: Educational Policy, Predictor Variables, Computation, Thinking Skills
Zhan, Zehui; He, Guoqing; Li, Tingting; He, Luyao; Xiang, Siyu – Journal of Computer Assisted Learning, 2022
Background: Group size is one of the important factors that affect collaborative learning, however, there is no consensus in the literature on how many students should the groups be composed of during the problem-solving process. Objectives: This study investigated the effect of group size in a K-12 introductory Artificial Intelligence course by…
Descriptors: Cognitive Ability, High School Students, Cooperative Learning, Artificial Intelligence
Carpio Cañada, J.; Mateo Sanguino, T. J.; Merelo Guervós, J. J.; Rivas Santos, V. M. – Journal of Computer Assisted Learning, 2015
Limitations of formal learning (e.g., one-way communication, rigid methodology, results-oriented approach) can significantly influence the motivation and expectation of students, thus resulting in an academic progress reduction. In order to make learning processes more playful and motivating, this paper presents a new educational experience…
Descriptors: Foreign Countries, Open Education, Computer Science Education, Artificial Intelligence

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