Supporting the Instructional Videos With Chatbot and Peer Feedback Mechanisms in Online Learning: The Effects on Learning Performance and Intrinsic Motivation
Abstract
This study investigated the effects of artificial intelligence (AI)-based chatbot and peer feedback mechanisms integrated into the instructional videos (IVs) as a feedback tool on learning performance and intrinsic motivation of pre-service teachers (PTs) in online learning. The participants were 144 PTs from a university in Turkey. A pretest–posttest quasi-experimental design was adopted in this study. Two experimental (EG-1: Immediately elaborated feedback with a chatbot for IVs; EG-2: Delayed peer feedback with comments for IVs) groups and a control group (teaching with IVs) were selected. To collect qualitative data, a survey consisting of open-ended questions was conducted in the experimental groups. The results showed that the learning performance and intrinsic motivation scores of chatbot-based and peer feedback groups were higher than the scores in the traditional learning group. The implications for AI-powered feedback mechanisms and directions for future studies were discussed in this study.
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Chatbot-assisted instructional videos and micro-learning systems could effectively promote intrinsic motivation.
“Furthermore, chatbot-assisted instructional videos and micro-learning systems could also effectively promote intrinsic motivation [18,44].”
From A Meta-Analysis and Systematic Review of the Effect of Chatbot Technology Use in Sustainable Education (2023) · cites this paper as [44]
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- Authors
- Fidan, M., & Gencel, N.
- Affiliations
- Bartin University (TR)
- Ministry of National Education, Bartin, Turkey
- ORCID
- Fidan, Mustafa
- Venue
- Journal of Educational Computing Research
- Publisher
- SAGE Publications
- Published
- 2022-02-14
- Volume
- 60
- Issue
- 7
- Pages
- 1716-1741
- ISSN
- 0735-6331 (ISSN-L)
- 1541-4140
- OpenAlex
- W4213293189
- Semantic Scholar
- e0048548bc7224d9a1c1f880717ad96fd4997218
- Cited by
- 186
- References
- 17
- Citations by year
- 2026: 42 · 2025: 73 · 2024: 46 · 2023: 21 · 2022: 4
- FWCI
- 11.85
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Educational Computing Research (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Education › Student Assessment and Feedback
- Keywords
- chatbots, peer feedback, educational videos, online learning, pre-service teachers, learning performance, intrinsic motivation, AI-generated feedback, elaborated feedback, delayed feedback
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