The contingent impact of artificial intelligence on teaching effectiveness: a meta-analytic review of boundary conditions and moderating factors
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 (g_p = 0.586) exceeding the pre-test-post-test gain (g_delta = 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.
How strong is the evidence?
Evidence Hub reads this as a meta-analysis. 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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