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A Comparison of AI, Professor, Peer, and Self-Feedback in University English Presentations KCI 등재
pp.95-121
This study aims to compare four common types of feedback used in university English presentation activities: artificial intelligence (AI), professor, peer, and self-feedback. A total of 38 first-year students enrolled in a first-year English course at a Korean university participated in this study by recording and evaluating practice videos to provide feedback to help improve their final presentations. Each of the two practice videos received evaluation scores and written comments from multiple sources, including AI tools, the professor, their peers, and themselves. To examine students’ perceptions of these feedback sources, data were collected through Likert-scale survey items measuring perceived effectiveness and preference, as well as open-ended responses explaining students’ choices. Data analysis included descriptive statistics for the survey responses and content analysis for the qualitative comments. The research findings are as follows. Firstly, professor feedback was perceived as the most effective source for improving presentation performance. Secondly, peer feedback was viewed as helpful for providing additional perspectives on presentation quality. Thirdly, AI and self-feedback were generally perceived as less reliable than human feedback. These findings suggest that combining multiple feedback sources may support speaking development in university English presentation tasks, with pedagogical implications and directions for future research discussed.
II. Theoretical Background
1. Feedback and Student-Centered Learning in L2 Contexts
2. Human-based Feedback in Language Learning: Professor, Peer,and Self
3. AI-Based Feedback in Language Learning
III. Research Methods
1. Participants
2. Procedures
3. Data Collection and Analysis
IV. Results and Discussions
1. Effectiveness of Feedback
2. Students’ Preferred Type of Feedback
3. Differences Across Feedback Types
V. Conclusion
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