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Although generative artificial intelligence (GenAI) feedback shows promise for educational applications, its actual impact on learning outcomes and the factors influencing its effectiveness remain unclear. This study conducted a systematic review and meta-analysis to evaluate the effectiveness of GenAI feedback and identify key moderating factors. Following the PRISMA 2020 guidelines, we reviewed 36 experimental and quasi-experimental studies published between 2023 and 2025, yielding 72 effect sizes. The results revealed that GenAI feedback had a moderate positive effect on academic achievement (g = 0.61), with significant moderation by contextual factors. Subgroup analysis revealed that teaching methods significantly moderated the effectiveness of GenAI feedback, with stronger effects observed in learner-centered environments promoting active construction than in teacher-centered, receptive instruction; whereas educational level, disciplines, intervention duration, and GenAI role showed no significant moderation. A three-level random-effects model was employed to account for effect size dependencies, correcting for the underestimation of standard errors typical of conventional two-level models. Outcome dimension analysis showed that GenAI feedback had the strongest impact on cognitive outcomes, with promising but less established benefits for metacognitive development, and modest effects on non-cognitive outcomes. Future research should further clarify the roles of metacognitive and non-cognitive outcomes in GenAI feedback. In practice, GenAI feedback should serve as complementary scaffolding within constructivist pedagogies to support metacognitive development, while teacher emotional support should be preserved to foster students’ non-cognitive development.
Huang et al. (Fri,) studied this question.
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