Currently, data-driven adaptive learning technology has shown tremendous potential in the field of education. However, its opaque “black box” nature has raised widespread concerns among educational researchers and practitioners. Explainable artificial intelligence (XAI) is believed to have the potential to help learners understand intervention decisions in adaptive learning contexts, thereby enhancing learning outcomes, but there is controversy over its practical effects in educational applications. Therefore, the paper employed meta-analysis to analyze 66 effect sizes from 29 empirical studies. It was found that interpretable AI improved the learning effect of adaptive learning to a moderate degree, with a greater impact on learners’ cognitive and metacognitive dimensions. The facilitation effect of XAI varied due to differences in explanation design, presentation design, and experimental design. Based on research results, the paper proposed that future adaptive learning interventions should remain learner-centered, emphasize the interactivity, readability, and boundaries of learning intervention explanations, so as to further promote the in-depth implementation of XAI in education.