Understanding How Generative AI Cultivates Self-Directed Learning Capabilities: A Perspective Based on Digital Technology Evolution

 

Abstract

The use of digital technologies to support student learning has become a trend in the field of education. However, whether digital technologies can effectively facilitate students' self-directed learning remains a topic of debate in academia. This study employs a meta-analytic approach to examine the effectiveness of digital technologies in promoting self-directed learning among students, while exploring the influence of different moderating variables. A total of 27 articles (including 30 independent studies with 3,711 participants) met the inclusion criteria. The results indicate that digital technologies significantly enhance students' self-directed learning, demonstrating a moderate effect size (Hedges’s g = 0.778, 95% CI: 0.510–0.847, p < 0.05). Among the moderating variables, the type of digital technology and teaching size showed significant effects, whereas educational stage, subject area, and intervention duration did not exhibit significant moderating effects. Based on the findings, recommendations are proposed regarding the selection of digital technology types, adjustments to teaching size and intervention duration, and targeted considerations for educational level and subject area. This study provides a theoretical foundation and empirical evidence for the scientific application of digital technologies, the cultivation of self-directed learning, and the formulation of educational technology policies.