The moderating mechanism of teacher-student interaction quality on the alleviating effect of artificial intelligence teaching assistant on college students' learning burnout |
| Yijun Quan Bowei Liu Kwon Jaehwan |
| Dongshin University,Naju-si,Jellanam-do 58245, Korea |
Abstract : With the deep integration of artificial intelligence (AI) technology in education, AI-assisted teaching has become a crucial support for higher education reform. This study investigates the alleviating effect of AI-assisted teaching on college students 'academic burnout and examines the mediating role of teacher-student interaction quality. A questionnaire survey was conducted among 520 university students using the Academic Burnout Scale and Teacher-Student Interaction Quality Scale, with data processing and analysis performed through SPSS 26.0 and AMOS 24.0. Results indicate: (1) There is a significant negative correlation between AI-assisted teaching frequency and academic burnout (r=-0.32, p<0.01); (2) AI-assisted teaching frequency shows a significant positive correlation with teacher-student interaction quality (r=0.45, p<0.01); (3) Teacher-student interaction quality demonstrates a significant negative correlation with academic burnout (r=-0.51, p<0.01); (4) Teacher-student interaction quality partially mediates the relationship between AI-assisted teaching and academic burnout, accounting for 57.8% of the total effect. The findings provide empirical evidence for universities to optimize teaching processes and alleviate students' academic burnout through AI technology.
Keywords AI teaching assistants; college students; learning burnout; teacher-student interaction quality; mediating mechanisms
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References
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Conflict of Interest
The author declares no conflict of interest.
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