<scp>ChatGPT</scp> in Education: A Systematic Review of Current Landscape, Limitations and Future Directions Through General System Theory Lens
Hsin‐Yu Lee, Yueh‐Min Huang, Ting‐Ting Wu
Abstract
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.