Acceso usuarios
Haohui Lu
, Na Chen, Jie Zhang, Shuzhen Tang, Katayoun Mehdinezhad Nouri
Artificial Intelligence (AI) has been incorporated into education, particularly in delivering scalable, individualized responses. The given research examines how AI-driven feedback influences students’ academic resilience, which is a crucial measure in dealing with academic stress and failure. This study aims to examine the overall effect of AI-based feedback interventions on academic resilience, analyze the mediating role of feedback quality in this relationship, and identify moderating factors, such as educational level, that influence AI feedback effectiveness. It involves a meta-analysis of the information gathered in 10 peer-reviewed quantitative studies published between 2017 and 2025. Effect sizes were calculated using the Hunter-Schmidt method, and the mediating roles of feedback quality and educational level were also tested. Meta-analysis indicated a moderate positive association (r = 0.41) between AI-based feedback and academic resilience. The main aspects that play a significant role in determining this relationship would be the quality and personalization of feedback. Furthermore, the educational level did not have a significant moderation factor in this effect, and thus, AI feedback can develop resilience in different educational contexts. AI-driven feedback positively impacts academic resilience, especially when it is personalised, timely, and of high quality. The findings suggest that AI feedback systems can be universally applied across educational contexts to foster resilience in students.
