SynthesisBMC psychology2026
Artificial intelligence and academic anxiety? a meta-analysis.
Synthesis in BMC psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
3 authors.
Funding
Abstract
backgroundAcademic anxiety negatively impacts students' performance and well-being. Although AI technologies are increasingly used in education, findings on their effectiveness in reducing academic anxiety remain mixed.
objectiveThis meta-analysis quantified the overall effect of AI interventions on academic anxiety and identified potential moderators.
methodsA systematic search identified 22 studies with 30 independent effect sizes. Robust variance estimation with random-effects models was used. Subgroup analyses and Q-tests were conducted.
resultsAI interventions showed a significant moderate overall effect on reducing academic anxiety (g = - 0.65, 95% CI [- 1.07, - 0.22], p < .01). Moderator analyses revealed: (a) test anxiety and general academic anxiety had significant effects, whereas mathematics, foreign language, and learning anxiety did not; (b) intelligent tutoring systems and robotics produced significant effects, but chatbots and virtual technology did not; (c) primary and middle school effects were marginally significant, university effects non‑significant; (d) Eastern cultural context showed a significant effect, Western and Islamic contexts did not; (e) effects sustained from immediate to delayed posttests; (f) post‑2022 studies yielded larger effects than earlier studies (marginally significant difference); (g) smaller‑sample studies tended to produce larger effect sizes. However, Q‑tests for most moderators were non‑significant (p > 0.05), indicating observed differences are descriptive rather than statistically robust.
conclusionAI interventions significantly reduce academic anxiety with a moderate effect size. Although efficacy varies descriptively across several factors, most subgroup differences are not statistically significant. Future research should employ rigorous experimental designs, longer follow‑ups, and meta‑analytic structural equation modeling to test underlying mechanisms.
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