Artificial intelligence and academic anxiety? a meta-analysis
Abstract
Background: Academic 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.
Objective: This meta-analysis quantified the overall effect of AI interventions on academic anxiety and identified potential moderators.
Methods: A 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.
Results: AI interventions showed a significant moderate overall effect on reducing academic anxiety (g = - 0.65, 95% CI [- 1.07, - 0.22], p 0.05), indicating observed differences are descriptive rather than statistically robust.
Conclusion: AI 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.
How strong is the evidence?
Evidence Hub reads this as a meta-analysis, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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