Artificial Intelligence-Supported Flipped Learning and Academic Achievement: A Systematic Review and Meta-Analysis with Implications for Sustainable, Quality Education

This study meta-analyzed the incremental contribution of artificial intelligence (AI) to flipped learning on students’ academic achievement, relative to flipped learning without AI. Following a protocol registered on the Open Science Framework (OSF), systematic searches were conducted in Web of Science and Scopus, supplemented by an ERIC search, with coverage through 25 July 2026; eligible studies used experimental or quasi-experimental designs comparing an AI-supported flipped condition with a matched non-AI flipped comparator on an academic-achievement outcome. Applying the comparator criterion strictly, ten studies (two randomized controlled trials and eight quasi-experimental studies; N = 937 learners) met all criteria; one further study whose comparator was conventional, non-flipped instruction was excluded from the primary analysis and retained only as a sensitivity check. Using Hedges’ g in a random-effects model (REML with the Knapp–Hartung adjustment), the pooled effect was positive, moderate and statistically significant: g = 0.67, 95% CI [0.32, 1.02], t(9) = 4.36, p = 0.002. Heterogeneity was substantial (I2 = 64.6%, τ2 = 0.086) and the 95% prediction interval ranged from −0.10 to 1.43, indicating that the true effect in a new setting could plausibly be large or close to negligible. Egger’s test was statistically significant (t = 3.25, p = 0.01), consistent with a small-study effect; excluding studies with fewer than 15 participants per arm attenuated the estimate to g = 0.53. Because the comparator was itself flipped learning, this estimate approximates the incremental contribution of the AI component rather than the combined effect of AI and the flipped model. Overall certainty of the evidence (GRADE) was low. AI can add value to flipped learning, but the benefit is conditional on implementation quality and context rather than an inherent property of the technology, and the findings should be read with caution given a small, heterogeneous and partly small-study-influenced evidence base.