Exploring Educational Innovation in the Digital Transformation Era Led by the Convergence of Neuroscience and AI through a Meta-Analysis of the Effectiveness of Evidence-Based Teaching Methods

The purpose of this study is to conduct a meta-analysis of the effectiveness of evidence-based teaching methods from a neuroscientific perspective, the integration of AI technology, and the post-pandemic situation. In addition, the differential effects of teaching methods according to various moderating variables such as learner characteristics, socioeconomic background, and treatment variables by teaching-learning strategies were analyzed. The research method is meta-analysis. According to the initial search results, there were 2,847 articles, but after applying the selection criteria, 185 articles were finally selected, and Hedges' g correction was applied and the effect size was calculated at a 95% confidence interval. Heterogeneity verification, publication bias, etc. were checked, and the reliability and validity of the effect size results were verified. The results of the study showed that the evidence-based teaching method showed a positive effect of moderate or higher overall and was statistically significant. When examining the learner characteristics, the study targeting elementary school students showed a greater effect than secondary and higher education, and showed a greater effect in the lower group of academic achievement. In terms of treatment characteristics, the effect size significantly increased when AI technology was used, and project-based learning was found to be the most effective. Long-term treatment was more effective. Evidence-based instructional practices demonstrate significant effectiveness across different educational contexts, with particularly strong effects when integrated with AI technology and implemented through long-term, project-based learning approaches, suggesting the importance of systematic and sustained implementation of technology-enhanced teaching methods in the digital transformation era. The findings provide practical guidelines for educators and policymakers to design and implement differentiated instructional strategies based on educational levels and learner characteristics, while emphasizing the need to bridge the digital divide and ensure equitable access to AI-enhanced educational opportunities across different