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The Relationship between Students' Self-Regulated Learning Skills and Technology Acceptance of GenAI
Peer reviewed
Direct linkNegin Mirriahi; Rebecca Marrone; Abhinava Barthakur; Florence Gabriel; Jill Colton; Ting Nga Yeung; Peter Arthur; Vitomir Kovanovic – Australasian Journal of Educational Technology, 2025
Generative artificial intelligence (GenAI) has quickly become prolific in our daily lives, including the higher education sector. Although an AI-fuelled world is unpredictable, there is an urgent need to understand how university students use GenAI to support their learning and the factors influencing GenAI adoption. In this study, underpinned by…
Descriptors: Artificial Intelligence, College Students, Adoption (Ideas), Computer Uses in Education
Donggil Song – International Journal of Artificial Intelligence in Education, 2025
Artificial intelligence (AI) applications, including machine learning (ML), have received attention in education, and generative AI-powered chatbots have been adopted in diverse educational settings worldwide. However, the actual use of and perception regarding generative AI chatbots by learners have been under-investigated. To better prepare for…
Descriptors: Trust (Psychology), Undergraduate Students, Artificial Intelligence, Student Attitudes
Liu Shi; Shengji Li; Jingjing Xing – European Journal of Education, 2025
The growing integration of artificial intelligence (AI) tools into English as a foreign language (EFL) instruction presents new opportunities for fostering students' self-regulated learning (SRL) and task engagement (TE). While prior research has shown that AI-assisted environments can enhance metacognitive monitoring and learning motivation,…
Descriptors: Foreign Countries, Secondary School Students, English (Second Language), Second Language Learning
Download full textDina Fitria Murad; Meta Amalya Dewi; Arbaiah Inn; Silvia Ayunda Murad; Noor Udin; Taufik Darwis – Journal of Educators Online, 2025
This study aims to produce a more personalized recommendation system for online learning using multicriteria in collaborative filtering and data from the Binus Online Learning repository as a knowledge base. The study uses forecasting (regression) and consists of three stages: (1) collecting data on the results of the learning process; (2) adding…
Descriptors: Electronic Learning, Data Collection, Context Effect, Learning Processes
