ChatGPT in Education: A Systematic Review of Current Landscape, Limitations and Future Directions Through General System Theory Lens

转化式学习 透视图(图形) 比例(比率) 工作(物理) 心理学 系统回顾 数学教育 医学教育 教育学 计算机科学 工程类 政治学 医学 地理 梅德林 法学 人工智能 机械工程 地图学
作者
Hsin‐Yu Lee,Yueh‐Min Huang,Ting‐Ting Wu
出处
期刊:European Journal of Education [Wiley]
卷期号:60 (4) 被引量:4
标识
DOI:10.1111/ejed.70262
摘要

ABSTRACT The rapid proliferation of ChatGPT has fuelled expectations of transformative change in education; yet most systematic reviews remain confined to single disciplines or educational levels. Adopting general system theory (GST) and the knowledge‐skills‐attitudes (KSA) framework, this review offers a more comprehensive perspective on ChatGPT's educational impact. Following PRISMA guidelines, we searched major academic databases for studies published between November 2022 and March 2024, screened them against predefined criteria, and ultimately analysed 92 eligible papers through qualitative content analysis. Findings were mapped onto the five GST elements—subject, information, medium, environment and technology. Research to date is markedly concentrated in higher education (69.7%), with far less work in primary (3.4%) and secondary (13.5%) settings. Nearly half of the studies target STEM subjects (45.6%). The most frequently reported applications are timely feedback (55 occurrences), content generation (20) and personalised learning (15). Positive effects are most evident for knowledge construction (63 occurrences) and skills development (59), whereas attitudinal outcomes receive comparatively little attention (20). Overall, the evidence points to promising—but uneven—educational benefits. Prevailing limitations include small‐ or medium‐scale research designs and the under‐representation of primary and secondary contexts. Future work should adopt larger samples, give greater weight to attitudinal dimensions, and strive for a more balanced coverage across all educational levels to realise ChatGPT's full potential in teaching and learning.
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