Artificial Intelligence in Academic Writing in Higher Education: A Systematic Literature Review of Pedagogical Integration, Assessment Practices, and Teachers' Perspectives

Abstract

Artificial intelligence (AI) is rapidly transforming academic writing in higher education by enhancing teaching practices, writing assessment, and student learning. Despite the growing adoption of AI-powered tools such as ChatGPT, Grammarly, PaperPal, and Automated Writing Evaluation (AWE) systems, existing research remains fragmented across pedagogical, assessment, and teacher-related perspectives. This study systematically reviews the current evidence on AI in academic writing using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines. Guided by four research questions formulated through the PICo framework, a comprehensive search of Scopus and Web of Science identified 236 records, from which 24 peer-reviewed studies published between 2021 and 2026 met the inclusion criteria following screening, eligibility assessment, and quality appraisal. The findings reveal three dominant themes: (1) AI integration in academic writing teaching and learning, (2) AI-assisted assessment, feedback, and writing evaluation, and (3) teachers' perspectives, ethical issues, and AI adoption. Collectively, the reviewed studies demonstrate that AI enhances writing quality, learner engagement, self-regulated learning, and formative assessment through timely and personalized feedback. However, AI remains limited in evaluating higher-order writing competencies, including critical thinking, originality, and contextual reasoning, reinforcing the continued importance of human expertise in writing assessment. The review further highlights the influence of teachers' perceptions, ethical concerns, and institutional readiness on successful AI implementation. The study contributes a comprehensive synthesis of current research by integrating pedagogical, assessment, and human perspectives through the TPACK, Assessment for Learning, and Technology Acceptance Model frameworks. These findings provide practical implications for educators, institutions, and policymakers while identifying priorities for future research on responsible and pedagogically sound AI integration in academic writing within higher education.