Paper Type
Complete
Abstract
Generative artificial intelligence (GenAI) tools are rapidly reshaping academic writing in higher education. This systematic literature review synthesizes empirical studies on GenAI use in academic writing and organizes the findings around technical, social, and environmental dimensions of AI-mediated writing systems. The literature shows that GenAI supports writing, feedback, and assessment tasks. However, GenAI also reshapes the social and institutional meaning of academic writing by raising questions about student agency, authorship, trust, and academic integrity. The review further identifies governance and design challenges related to policy clarity, AI literacy, disclosure, overreliance, hallucinations, bias, equity, and responsible use. The findings suggest that GenAI’s educational value is conditional and depends on alignment among tool capabilities, user capability, faculty judgment, assessment design, and institutional governance. Future research should examine how GenAI affects learning development and responsible use across diverse learners, disciplines, and institutional contexts.
Paper Number
1937
Recommended Citation
Sutrave, Kruttika; Stachowicz, Tamara L.; and Divetta, Joshua Scotto, "Generative AI in Academic Writing: Writing Support, Human Agency, and Institutional Governance" (2026). AMCIS 2026 Proceedings. 23.
https://aisel.aisnet.org/amcis2026/sig_ed/sig_ed/23
Generative AI in Academic Writing: Writing Support, Human Agency, and Institutional Governance
Generative artificial intelligence (GenAI) tools are rapidly reshaping academic writing in higher education. This systematic literature review synthesizes empirical studies on GenAI use in academic writing and organizes the findings around technical, social, and environmental dimensions of AI-mediated writing systems. The literature shows that GenAI supports writing, feedback, and assessment tasks. However, GenAI also reshapes the social and institutional meaning of academic writing by raising questions about student agency, authorship, trust, and academic integrity. The review further identifies governance and design challenges related to policy clarity, AI literacy, disclosure, overreliance, hallucinations, bias, equity, and responsible use. The findings suggest that GenAI’s educational value is conditional and depends on alignment among tool capabilities, user capability, faculty judgment, assessment design, and institutional governance. Future research should examine how GenAI affects learning development and responsible use across diverse learners, disciplines, and institutional contexts.
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