Beyond Efficiency: A Systematic Review of Human–AI Interaction in Academic Writing in Higher Education

Abstract

Background: Generative artificial intelligence (AI) is increasingly integrated into higher education, reshaping academic writing practices and student interaction with digital tools. However, the nature of human–AI interaction in academic writing and its implications for learning remain insufficiently synthesized.

Aims: This study systematically reviews AI-assisted academic writing in higher education, focusing on its cognitive, pedagogical, and ethical dimensions. It also develops a context-mediated framework explaining how pedagogical and institutional factors shape educational outcomes.

Methods: A systematic review with qualitative thematic synthesis examined studies published from 2023 to 2025. Searches of Web of Science, Scopus, ERIC, PsycINFO, and PubMed were conducted in January 2026. Following predefined eligibility criteria and structured screening, 20 studies were included.

Results: AI tools can support academic writing through improved linguistic accuracy, more efficient drafting and revision, and greater learner confidence. However, potential risks include reduced cognitive engagement, over-reliance on AI-generated content, and academic integrity concerns. Outcomes appear to depend not on the technology alone but on patterns of human–AI interaction within pedagogical and institutional contexts.

Conclusion: The review proposes a context-mediated framework in which pedagogical guidance, student engagement, and institutional policy condition the educational impact of AI-assisted writing. AI is therefore best understood as a contingent educational tool whose value depends on how responsibly it is integrated into higher education.

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