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

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Title: Understanding How Generative AI Cultivates Self-Directed Learning Capabilities: A Perspective Based on Digital Technology Evolution
Language: English
Authors: Guo Shouchao (ORCID 0000-0002-0168-899x), Xu Ningjie (ORCID 0009-0003-0818-9688), Xu Zhenguo (ORCID 0000-0002-9961-7021)
Source: Turkish Online Journal of Educational Technology - TOJET. 2026 25(1):157-169.
Availability: Sakarya University. Esentepe Campus, Adapazari 54000, Turkey. Tel: +90-505-2431868; Fax: +90-264-6141034; e-mail: tojet@sakarya.edu.tr; Web site: https://tojet.net/
Peer Reviewed: Y
Page Count: 13
Publication Date: 2026
Document Type: Journal Articles
Reports - Research
Descriptors: Artificial Intelligence, Independent Study, Technology Uses in Education, Program Effectiveness, Effect Size, Instructional Program Divisions, Intellectual Disciplines, Intervention, Class Size
ISSN: 1303-6521
2146-7242
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.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1497769
Database: ERIC
FullText Text:
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  Data: Sakarya University. Esentepe Campus, Adapazari 54000, Turkey. Tel: +90-505-2431868; Fax: +90-264-6141034; e-mail: tojet@sakarya.edu.tr; Web site: https://tojet.net/
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  Data: 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&#39; 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&#39; self-directed learning, demonstrating a moderate effect size (Hedges&#39;s g = 0.778, 95% CI: 0.510-0.847, p &lt; 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.
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  Data: 2026
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    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 13
        StartPage: 157
    Subjects:
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Independent Study
        Type: general
      – SubjectFull: Technology Uses in Education
        Type: general
      – SubjectFull: Program Effectiveness
        Type: general
      – SubjectFull: Effect Size
        Type: general
      – SubjectFull: Instructional Program Divisions
        Type: general
      – SubjectFull: Intellectual Disciplines
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      – SubjectFull: Intervention
        Type: general
      – SubjectFull: Class Size
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    Titles:
      – TitleFull: Understanding How Generative AI Cultivates Self-Directed Learning Capabilities: A Perspective Based on Digital Technology Evolution
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            NameFull: Guo Shouchao
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            NameFull: Xu Ningjie
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            NameFull: Xu Zhenguo
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              Type: published
              Y: 2026
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