Answers are grounded in this paper and cite their sources.
Critical morphological analysis reveals gaps in ChatGPT adoption and educational impact in higher education, guiding future research.
Purpose The integration of artificial intelligence (AI) into education has significantly advanced, with ChatGPT emerging as a key technology for transforming learning environments. Prior reviews examined ChatGPT but lacked a multidimensional synthesis of its integration into higher education. This study offers a holistic understanding of ChatGPT’s role and identifies overlooked dimensions to guide future research and practice. Design/methodology/approach This study conducts a critical morphological analysis (MA) to understand how ChatGPT is integrated into global higher education. Findings It draws on six dimensions: user characteristics, ChatGPT adoption, enablers, applications, outcomes and ethical concerns. This study developed a cross-consistency matrix to map existing research patterns and research gaps. Key trends reveal growing attention toward institutional readiness and learning effectiveness, while gaps related to overdependence risks, socio-emotional learning and equitable access remain. Furthermore, thematically grouped future research directions are proposed for key stakeholders in academic settings: students, educators, institutional staff and higher education institutions/policymakers. Originality/value This study advances previous reviews by moving beyond thematic descriptions toward cross-dimensional analyses. The MA reveals three substantive findings that are invisible to single-dimension reviews: institutional enabler research remains empirically disconnected from measurable learning outcomes; individual user traits, such as anxiety and work avoidance, are rarely examined alongside ethical concern variants; and equity and digital divide concerns are not linked to institutional infrastructure conditions in the literature. These gaps, identified using the cross-consistency matrix, provide a structured and replicable basis for future empirical investigations.
No takes yet. Share an insight, caveat, or question.
Dhaigude et al. (2026) studied this question.
Synapse has enriched 10 related papers. Consider them for comparative context: