<b>Generative Artificial Intelligence (GenAI) for Academic Writing in Higher Education: A Scoping Review of Applications, Challenges, and Implications </b>

Abstract

Generative AI is reshaping academic writing faster than universities can produce evidence-informed guidance, putting practice ahead of proof. Using a Population–Concept–Context frame and PRISMA-ScR procedures, we mapped peer-reviewed, English-language empirical studies in higher education from 2024 to Q2 2025. A total of 25 studies met criteria. Findings were synthesized via convergent integration that paired quantitative distributions with qualitative themes. Across populations and contexts, GenAI was most often used as assistive scaffolding from planning through revision. Reported benefits clustered around organization, fluency, efficiency, and language support, especially for multilingual writers. Recurrent risks included hallucinations and fabricated citations, inconsistent disclosure or attribution, overreliance when use was unscaffolded, and the limited reliability of AI-detection tools for integrity judgments. Context mattered: clearer policies and better access supported more constructive use. The evidence base skews toward English-medium, well-resourced institutions and relies heavily on short-term or proxy outcomes. Gaps include faculty and postgraduate cohorts and Global South contexts. Overall, the pattern supports an assistive, not substitutive stance: GenAI complements rather than replaces human judgment in argument construction, source interrogation, and synthesis.

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