Effect of Artificial Intelligence‐Based Tutoring, Cognitive Engagement, and Teacher Feedback on University Students' Academic Performance

Integrating AI into education has significantly improved students' personalized learning skills. However, the absence of human‐based feedback or sentiment oversight has caused unrest among university students. The present study was designed with theoretical foundations of self‐regulated learning to examine the contribution of AI‐based tutoring (AIBT), cognitive engagement (CE), and teacher feedback (TF) to students' academic performance (AP). The research design of the current quantitative study was correlational in nature. The study participants were Chinese University students, and data were collected from 314 students selected through multi‐method and multi‐stage sampling techniques. The data were analysed using Jamovi software. The results revealed that AIBT significantly enhances students' AP. The analysis indicated that students' CE positively enhances and mediates the relationship between AIBT and AP. We found that TF also contributes to students' AP. However, its moderating effect on the relationship between AIBT and AP was found to be insignificant. The study concludes that in SRL theory driven context, TF was positively related to AP, but it did not change the AIBT and AP relationship. This suggests that AIBT and TF may help in parallel. Guidance is still needed on when to rely on AIBT and when to seek TF.