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TRUVACE RECORD VERSION
record: TRV-2026-2764
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:20:40.878487Z
status: published
lens: g_space
sector: education
headline: Artificial Intelligence-Supported Flipped Learning and Academic Achievement: A Systematic Review and Meta-Analysis with Implications for Sustainable, Quality Education
dek: 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 fl…
gain_title: A meta-analysis of ten experimental studies (937 learners) found students in AI-supported flipped classes scored higher on achievement than those in flipped classes without AI (Hedges' g = 0.67, 95% CI 0.32 to 1.02).
problem_title: (none)
trace_subject: (none)
gain_reading: A meta-analysis of ten experimental studies (937 learners) found students in AI-supported flipped classes scored higher on achievement than those in flipped classes without AI (Hedges' g = 0.67, 95% CI 0.32 to 1.02).
gain_evidence: the pooled effect was positive, moderate and statistically significant: g = 0.67, 95% CI [0.32, 1.02] | ten studies (two randomized controlled trials and eight quasi-experimental studies; N = 937 learners) met all criteria
problem_reading: (none)
problem_evidence: (none)
quick_read: A systematic review and meta-analysis pooled ten experimental and quasi-experimental studies with 937 learners to test what AI adds to flipped learning. Compared with flipped classes that did not use AI, AI-supported flipped classes produced a pooled effect of g = 0.67 on academic achievement (95% CI 0.32 to 1.02).

The comparison matters because the control group was itself flipped learning, so the estimate approximates the contribution of the AI component alone, not the combined effect of AI and the flipped model.

The evidence is weak in places. Results varied widely between studies, and a 95% prediction interval of −0.10 to 1.43 means a new setting could see a large effect or a near-zero one. Overall certainty, rated by GRADE, was low.
limitation: The evidence base is small (ten studies) and varied, with a significant small-study effect; the estimate fell to g = 0.53 when the smallest studies were excluded, and overall certainty was rated low.
tag: Evidence-backed gain
key_points: Ten studies met the strict criteria: two randomized controlled trials and eight quasi-experimental studies, with 937 learners in total. | Each study compared an AI-supported flipped condition with a matched flipped condition without AI, on an academic-achievement outcome. | The pooled effect was g = 0.67 (95% CI 0.32 to 1.02, p = 0.002) in a random-effects model with the Knapp–Hartung adjustment. | Heterogeneity was substantial (I2 = 64.6%), and the prediction interval ran from −0.10 to 1.43. | Egger's test was significant (p = 0.01), consistent with a small-study effect; excluding studies with fewer than 15 participants per arm cut the estimate to g = 0.53. | GRADE rated overall certainty of the evidence as low.
rundown: The review followed a protocol registered on the Open Science Framework. Searches covered Web of Science and Scopus, supplemented by ERIC, through 25 July 2026, and only studies with a matched non-AI flipped comparator were eligible.

One further study used conventional, non-flipped instruction as its comparator. It was excluded from the primary analysis and kept only as a sensitivity check.

The authors conclude that AI can add value to flipped learning, but that the benefit depends on implementation quality and context rather than being an inherent property of the technology. They say the findings should be read with caution given a small, heterogeneous evidence base partly influenced by small studies.
sources:
- peer_reviewed | Sustainability | https://doi.org/10.3390/su18178715 | 2026-08-25
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