What question did this study set out to answer?

This research aims to evaluate how AI-enabled teaching affects teaching effectiveness by examining multiple studies.

May 3, 2026Frontiers in Psychology0 citationsOpen Access

The contingent impact of artificial intelligence on teaching effectiveness: a meta-analytic review of boundary conditions and moderating factors

JCJie CaiNanjing University of Finance and Economics JZJiaqi ZhuNanjing University of Finance and Economics XTXinmeng TangNanjing University of Finance and Economics

Key Points

This research aims to evaluate how AI-enabled teaching affects teaching effectiveness by examining multiple studies.
Conducted a meta-analysis combining 35 pre-test-post-test designs and 37 post-test studies.
Analyzed effect sizes of AI interventions, with an emphasis on baseline controls and AI type.
Examined publication bias and methodological heterogeneity to ensure robust findings.
AI-driven interventions showed a significant post-test effect size of g_p = 0.586 compared to a g_delta = 0.136 for pre-test-post-test designs.
Heterogeneity in results was primarily influenced by the type of AI used and the direction of outcomes.
Low publication bias was observed, affirming the reliability of the findings.

Abstract

The digital transformation of education is reshaping pedagogical landscapes and driving the shift toward smart teaching. This meta-analysis examines the impact of Artificial Intelligence (AI) -enabled teaching on intelligent teaching assessment, synthesizing evidence from 35 pre-test-post-test designs and 37 post-test studies. Results show that AI-driven interventions yield significant positive effects, with the post-test effect size (gₚ = 0. 586) exceeding the pre-test-post-test gain (gdelta = 0. 136), primarily due to stricter baseline controls in the latter. Heterogeneity is mainly driven by AI type and results direction, while methodological factors have minor influence. Low publication bias and robust analyses support the reliability of findings, underscoring the importance of appropriate AI application. These insights provide empirical support for reconfiguring teachers’ roles and implementing data-driven instruction, offering strategic guidance for teaching process redesign and the enhancement of teachers’ digital competencies via personalized and adaptive learning.

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Cite This Study

Cai et al. (Wed,) studied this question.

synapsesocial.com/papers/69f6e5618071d4f1bdfc61ea https://doi.org/https://doi.org/10.3389/fpsyg.2026.1744690

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