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

Q: What research approach does the study use to evaluate digital technologies for self-directed learning?

The study uses a meta-analytic approach to examine the effectiveness of digital technologies in promoting students’ self-directed learning and to test moderating variables.

Q: How many studies and participants are included in the meta-analysis?

A total of 27 articles are included, representing 30 independent studies with 3,711 participants.

Q: Which factors significantly moderate the effect of digital technologies on self-directed learning?

The type of digital technology and teaching size show significant moderating effects, while educational stage, subject area, and intervention duration do not.

Oct 15, 2026

Uploaded by Ophelia

Digital technologies to support student learning have become a major education trend, yet their effectiveness for self-directed learning remains contested. This meta-analytic study synthesizes 27 articles (30 independent studies; 3,711 participants) and tests moderating variables. Results show digital technologies significantly improve self-directed learning with a moderate effect size (Hedges’s g=0.778, 95% CI 0.510–0.847, p<0.05). Moderation is significant for technology type and teaching size, but not for educational stage, subject area, or intervention duration, informing targeted policy and practice recommendations.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Abstract

1 INTROUCTION

2 LITERATURE REVIEW

2.1 Self-directed learning and digital technology

TOJET: The Turkish Online Journal of Educational Technology – January 2026, volume 25 Issue 1 Understanding How Generative AI Cultivates Self-Directed Learning Capabilities: A Perspective Based on Digital Technology Evolution Guo Shouchaoa, Xu Ningjieb, Xu Zhenguoc* a Qufu Normal University, Rizhao, Shandong, China, [guosc@qfnu.edu.cn](guosc@qfnu.edu.cn), 0000-0002-0168-899xb Qufu Normal University, Rizhao, Shandong, China, [1361527388@qq.com](1361527388@qq.com), 0009-0003-0818-9688 c Qufu Normal University, Rizhao, Shandong, China, [qfnuxzg@qfnu.edu.cn](qfnuxzg@qfnu.edu.cn), 0000-0002-9961-7021 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 selfdirected learning, and the formulation of educational technology policies. Keywords: digital technologies, meta-analytic, self-directed learning 1 INTROUCTION As online education opportunities expand and digital technologies such as artificial intelligence (AI) advance rapidly, self-directed learning (SDL) has emerged as a fundamental skill for individuals to adapt and succeed in the digital era(Morris, 2019) . This shift has also established SDL as an indispensable component of contemporary education(Aulakh et al., 2025) . SDL emphasizes learners’ ability to proactively set goals, select strategies, monitor progress, and evaluate outcomes, monitor progress, and evaluate outcomes. In the digital era characterized by rapid knowledge iteration, the significance of SDL has become even more pronounced(Gaol & Prasolova-Førland, 2022) . SDL can cultivate students' self-awareness, goal orientation, and sense of mastery during the learning process. These competencies enhance learners' motivation, engagement, and confidence in digital learning environments(Morrison & McCutheon, 2019) . Meanwhile, the explosive development of digital technologies - from mobile learning platforms to virtual reality systems and today's generative artificial intelligence-has provided learners with unprecedented learning resources and educational experiences(Gaol & Prasolova-Førland, 2022) . Currently, a growing number of educators are integrating digital tools into traditional instructional environments and developing innovative teaching models(Morrison & McCutheon, 2019) . However, the actual efficacy of applying digital technologies to foster students' SDL remains debated. One view is that digital technology can facilitate the development of students' SDL(Lingling, 2024; Rashed Ibraheam Almohesh, 2024) . But another view suggests that the role of digital technology in cultivating students' SDL is negligible or even nonexistent(Lee, 2024; Yeh et al., 2022) . Existing studies have inconsistent conclusio

FAQs

What research approach does the study use to evaluate digital technologies for self-directed learning?

The study uses a meta-analytic approach to examine the effectiveness of digital technologies in promoting students’ self-directed learning and to test moderating variables.

How many studies and participants are included in the meta-analysis?

A total of 27 articles are included, representing 30 independent studies with 3,711 participants.

Which factors significantly moderate the effect of digital technologies on self-directed learning?

The type of digital technology and teaching size show significant moderating effects, while educational stage, subject area, and intervention duration do not.

Oct 15, 2026

Uploaded by Ophelia