Effects of artificial intelligence-enabled personalized recommendations on learners’ learning engagement, motivation, and outcomes in a flipped classroom
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AI-enabled recommendation agents can facilitate content personalization in videos to increase engagement and improve learning outcomes.
“AI-enabled recommendation agents can facilitate content personalization in videos to increase engagement and improve learning outcomes [153].”
From Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis (2024) · cites this paper as [153]
Integration of biofeedback mechanisms tracking physiological states enables personalized instruction strategies and improves engagement and performance.
“The integration of biofeedback mechanisms, which track and react to the physiological states of the learners, will enable personalized instruction strategies and improve their engagement and performance [153].”
From Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis (2024) · cites this paper as [153]
The integration of chatbots to eliminate learning anxiety has shown significant promise in most studies.
“The integration of chatbots to eliminate learning anxiety has shown significant promise in most studies ([28,46,47]).”
AI-based conversational chatbots significantly improved EFL learners' engagement.
“These studies revealed that AI-based conversational chatbots significantly improved EFL learners’ engagement.”
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- Authors
- Huang, A. Y. Q., Lu, O. H. T., & Yang, S. J. H.
- Affiliations
- National Central University (TW)
- National Chengchi University (TW)
- Corresponding author
- Yang, Stephen J.H.
- Venue
- Computers & Education
- Publisher
- Elsevier BV
- Published
- 2023-03
- Volume
- 194
- Pages
- 104684
- ISSN
- 0360-1315 (ISSN-L)
- OpenAlex
- W4310249545
- Cited by
- 605
- References
- 48
- Citations by year
- 2026: 149 · 2025: 233 · 2024: 171 · 2023: 47 · 2022: 1 · 2021: 1
- FWCI
- 87.22
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Computers & Education (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Education › Innovative Teaching Methods
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Keywords
- flipped classroom, recommender systems, artificial intelligence in education, student engagement, student motivation, learning outcomes, learning management system data, student learning performance
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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