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TRUVACE RECORD VERSION
record: TRV-2026-2830
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:32:08.408623Z
status: published
lens: trace
sector: science
headline: Unlocking the potential of artificial intelligence in improving learning achievement in blended learning: a meta-analysis
dek: Introduction While several studies investigated the effect of blended learning on students’ learning achievement, scant information exists on whether using artificial intelligence (AI) in blended learning could further contribute to the obtained effect. Methods To effectively address the challenges and opportunities presented by blended learning and AI, the present study conducts a meta-analysis to systematically examine the impact of AI-enhanced blended learning on students’ learning achievement, considering th…
gain_title: A meta-analysis of 21 studies with 2,873 students found AI-enhanced blended learning raised learning achievement by a medium margin (Hedges' g = 0.50), with personalized AI systems reaching g = 0.88.
problem_title: The same meta-analysis of 21 blended-learning studies found AI's benefit faded to g = 0.08 in interventions lasting a semester to a year, and the one early-childhood study showed a significant negative effect (g = -0.31).
trace_subject: AI-enhanced blended learning and student learning achievement
gain_reading: A meta-analysis of 21 studies with 2,873 students found AI-enhanced blended learning raised learning achievement by a medium margin (Hedges' g = 0.50), with personalized AI systems reaching g = 0.88.
gain_evidence: The overall pooled effect size of the 21 studies shows a medium effect size (Hedges’ g  = 0.50) | studies that used a personalized AI system ( k  = 4) reported a large effect size (Hedges’ g  = 0.88)
problem_reading: The same meta-analysis of 21 blended-learning studies found AI's benefit faded to g = 0.08 in interventions lasting a semester to a year, and the one early-childhood study showed a significant negative effect (g = -0.31).
problem_evidence: In long-term durations ( k  = 4, 1 semester to 1 year), the effect size was almost negligible and non-significant 0.08. | early childhood education included only one study which reported a small negative effect size (Hedges’ g  = −0.31)
quick_read: Researchers pooled 21 studies covering 2,873 students to test whether adding AI to blended learning (a mix of online and in-person teaching) improves learning achievement. AI-enhanced courses showed a medium effect (Hedges' g = 0.50, 95% CI 0.27 to 0.74) compared with control groups without AI. Sixteen studies used true experimental designs and five were quasi-experimental.

The benefit depended heavily on context. Personalized AI systems (four studies) had a large effect (g = 0.88), intelligent tutoring systems (16 studies) a small-to-medium one (g = 0.42), and the single chatbot study a non-significant one (g = 0.34). Higher education (nine studies) showed g = 0.7, while the one early-childhood study found a significant negative effect.

The results vary widely between studies (I² about 89%), and the prediction interval runs from -0.57 to 1.57, so a future study could find no benefit. Effects were largest in short interventions (g = 1.14 over one week to one month) and nearly vanished over a semester to a year (g = 0.08). Tests also pointed to publication bias, with a bias-adjusted estimate of g = 0.32.
limitation: Heterogeneity was very high (I² about 89%) with a prediction interval of -0.57 to 1.57, several subgroups rested on one or two studies, and tests suggested publication bias; only English-language journal papers were searched.
tag: Dual reading
key_points: The pooled effect across 21 studies and 2,873 participants was g = 0.50, with a 95% confidence interval of 0.27 to 0.74. | Personalized AI systems (k = 4) had g = 0.88; intelligent tutoring systems (k = 16) had g = 0.42; one chatbot study had a non-significant g = 0.34. | Higher education (k = 9) showed g = 0.7 and secondary education (k = 9) g = 0.40; primary education (k = 2) was medium but non-significant. | By subject, teacher training (one study) reached g = 0.99 and ICT and engineering (five studies) g = 0.88, while mathematics showed only g = 0.11. | Interventions of one week to one month had g = 1.14, one month to a semester g = 0.53, and a semester to a year g = 0.08. | Studies with 250 or fewer participants had g = 0.57; larger studies had a non-significant g = 0.24. | Rank-correlation and regression tests suggested publication bias; after removing six outliers and trim-and-fill, the estimate was g = 0.3181.
rundown: The team searched Science Direct, IEEE Xplore, Taylor & Francis, Scopus and Web of Science for peer-reviewed English-language studies published from 2011, with the last search on June 1, 2024. Of 538 records, 307 were duplicates, 115 full texts were assessed and 21 met the criteria, which required an AI intervention in blended learning and a control condition. Effect sizes were computed with Hedges' g in Comprehensive Meta-Analysis V.4.

Subgroup differences were marked. Meta-regression showed early childhood had a significantly negative coefficient, mathematics a significant negative coefficient of -0.38, and semester-to-year interventions a coefficient of -1.28. The authors suggest this may reflect a novelty effect or weaker implementation over long periods, though this is interpretation rather than a tested cause.

Publication-bias checks gave mixed signals. Kendall's tau (0.4571, p = 0.0033) and a regression test indicated small studies reporting larger effects were overrepresented, yet a p-curve analysis indicated evidential value. The authors call for longitudinal work and for evidence from primary schools, where data are thin.
sources:
- peer_reviewed | Frontiers in Psychology | https://doi.org/10.3389/fpsyg.2025.1691414 | 2025-10-31
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