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

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.

artificial intelligence, academic writing, higher education, writing assessment

1. Agrati, L. S., Vinci, V., Beri, A., & Berardi, P. (2026). Use of AI-assisted writing tools for student self-assessment at school: Survey on teachers' perceptions and practices. Frontiers in Education, 11, Article 1701597. https://doi.org/10.3389/feduc.2026.1701597 [Google Scholar] [Crossref]

2. Akhmedjanova, D., Suchkova, S., & Zharkova, N. (2025). Teaching research writing with AI: A case study of academic development courses in higher education. Journal of Language and Education, 11(4), 112–130. https://doi.org/10.17323/jle.2025.26589 [Google Scholar] [Crossref]

3. Alawad, E. A. (2025). Transformative assessment in academic writing: AI-based feedback systems with adaptive rubrics. Journal of Teaching English for Specific and Academic Purposes, 13(1), 141–149. https://doi.org/10.22190/JTESAP250113012A [Google Scholar] [Crossref]

4. Bedington, A., Halcomb, E. F., McKee, H. A., Sargent, T., & Smith, A. (2024). Writing with generative AI and human-machine teaming: Insights and recommendations from faculty and students. Computers and Composition, 71, 102833. https://doi.org/10.1016/j.compcom.2024.102833 [Google Scholar] [Crossref]

5. Black, P., & Wiliam, D. (1998). Assessment and classroom learning. Assessment in Education: Principles, Policy & Practice, 5(1), 7–74. https://doi.org/10.1080/0969595980050102 [Google Scholar] [Crossref]

6. Chen, H. M., & Pan, J. (2022). Computer or human: A comparative study of automated evaluation scoring and instructors' feedback on Chinese college students' English writing. Asian-Pacific Journal of Second and Foreign Language Education, 7(1), Article 34. https://doi.org/10.1186/s40862-022-00171-4 [Google Scholar] [Crossref]

7. Çela, E. (2025). Exploring the potential and implications of digital tools in teaching academic writing. Sodobna Pedagogika–Journal of Contemporary Educational Studies, 76(4). https://doi.org/10.63384/sptB54z804a [Google Scholar] [Crossref]

8. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008 [Google Scholar] [Crossref]

9. De Wilde, V. (2024). Can novice teachers detect AI-generated texts in EFL writing? ELT Journal, 78(4), 414–422. https://doi.org/10.1093/elt/ccae031 [Google Scholar] [Crossref]

10. Ding, L. Q., Zou, D., & Kohnke, L. (2025). ChatGPT as an automated writing evaluation tool: How students perceive it and how it affects their writing. Education and Information Technologies, 30(18), 26777–26799. https://doi.org/10.1007/s10639-025-13775-3 [Google Scholar] [Crossref]

11. Du, J. P., & Nordin, N. R. M. (2026). The impact of the iWrite automated writing evaluation system on university EFL students' writing performance and writing anxiety. Education Sciences, 16(3), Article 411. https://doi.org/10.3390/educsci16030411 [Google Scholar] [Crossref]

12. Eusebio-Hermira, S., Cuberos-Vicente, R., & Doquin de Saint-Preux, A. (2026). Academic assessment with artificial intelligence: Comparison between teachers and ChatGPT in master's theses. Revista de Investigación en Educación, 24(2), 309–326. https://doi.org/10.35869/reined.v24i2.6942 [Google Scholar] [Crossref]

13. González-Cervera, A., Burgueño-López, J., & Urosa-Sanz, B. (2026). Ethical use of artificial intelligence tools by future teachers. Canadian Journal of Learning and Technology, 52(1). https://doi.org/10.21432/cjlt29045 [Google Scholar] [Crossref]

14. Hamamra, B. (2026). Assessed academic writing outcomes in an AI-permitted research methodology course: Evidence from EFL university students in Palestine. Asian-Pacific Journal of Second and Foreign Language Education, 11(1), Article 36. https://doi.org/10.1186/s40862-026-00413-9 [Google Scholar] [Crossref]

15. Karthika, V. K., & B, H. M. (2026). Exploring the impact of AI feedback in academic writing self-assessment: A duoethnographic study. Interactive Learning Environments. Advance online publication. https://doi.org/10.1080/10494820.2026.2619502 [Google Scholar] [Crossref]

16. Kitchenham, B. (2007). Guidelines for performing systematic literature reviews in software engineering (Version 2.3). EBSE Technical Report. [Google Scholar] [Crossref]

17. Li, Z. (2021). Teachers in automated writing evaluation (AWE) system-supported ESL writing classes: Perception, implementation, and influence. System, 99, 102505. https://doi.org/10.1016/j.system.2021.102505 [Google Scholar] [Crossref]

18. Lin, C. J., & Hwang, G. J. (2026). Artificial intelligence-supported procedural scaffolding for promoting EFL learners' writing performance in flipped peer assessment activities. Interactive Learning Environments, 34(3), 1839–1853. https://doi.org/10.1080/10494820.2025.2532629 [Google Scholar] [Crossref]

19. Lockwood, C., Munn, Z., & Porritt, K. (2015). Qualitative research synthesis: Methodological guidance for systematic reviewers utilizing meta-aggregation. International Journal of Evidence-Based Healthcare, 13(3), 179–187. https://doi.org/10.1097/XEB.0000000000000062 [Google Scholar] [Crossref]

20. Mahdi, H. S., & Alkhateeb, A. (2025). Revolutionising essay evaluation: A cutting-edge rubric for AI-assisted writing. International Journal of Computer-Assisted Language Learning and Teaching, 15(1), Article 368226. https://doi.org/10.4018/IJCALLT.368226 [Google Scholar] [Crossref]

21. Marzuki, Widiati, U., Rusdin, D., Darwin, & Indrawati, I. (2023). The impact of AI writing tools on the content and organization of students' writing: EFL teachers' perspective. Cogent Education, 10(2), Article 2236469. https://doi.org/10.1080/2331186X.2023.2236469 [Google Scholar] [Crossref]

22. Miranty, D., & Widiati, U. (2021). Automated writing evaluation (AWE) in higher education: Indonesian EFL students' perceptions about Grammarly use across student cohorts. Pegem Eğitim ve Öğretim Dergisi, 11(4), 126–137. https://doi.org/10.47750/pegegog.11.04.12 [Google Scholar] [Crossref]

23. Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x [Google Scholar] [Crossref]

24. Nascimento, K. A. S., Fialho, L. M. F., & da Costa, M. A. A. (2025). Pedagogy professors' perspectives on the use of artificial intelligence in academic work. Educação e Pesquisa, 51, e294604. https://doi.org/10.1590/S1678-4634202551294604en [Google Scholar] [Crossref]

25. Nguyen, L., & Barrot, J. S. (2024). Detecting and assessing AI-generated and human-produced texts: The case of second language writing teachers. Assessing Writing, 62, 100899. https://doi.org/10.1016/j.asw.2024.100899 [Google Scholar] [Crossref]

26. Oladokun, B. D., Ukubeyinje, E. S., Oyighan, D., Obim, I. E., & Oloniruha, E. A. (2026). What if AI wrote it: Exploring the perspectives of university teachers toward AI-generated literature by undergraduates in library schools. College & Undergraduate Libraries, 33(1), 42–64. https://doi.org/10.1080/10691316.2025.2607372 [Google Scholar] [Crossref]

27. Page, M. J., McKenzie, J. E., Bossuyt, P., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. Medicina Fluminensis, 57(4), 444–465. https://doi.org/10.21860/medflum2021_264903 [Google Scholar] [Crossref]

28. Palacios-Núñez, M. L., Mendoza-García, E. M., Zarate, J. W. N., & Deroncele-Acosta, A. (2025). ChatGPT in the teaching of academic writing in higher education: Teachers' perspectives on its uses, challenges, and future in personalized learning. Edutec, 93, 33–51. https://doi.org/10.21556/edutec.2025.93.3995 [Google Scholar] [Crossref]

29. Steiss, J., Tate, T., Graham, S., Cruz, J., Hebert, M., Wang, J. L., Moon, Y., Tseng, W., Warschauer, M., & Olson, C. B. (2024). Comparing the quality of human and ChatGPT feedback of students' writing. Learning and Instruction, 91, 101894. https://doi.org/10.1016/j.learninstruc.2024.101894 [Google Scholar] [Crossref]

30. Yildiz, Ü., & Asma, B. (2026). Comparing artificial intelligence (AI) and instructor scoring in writing skills assessment. Pamukkale Üniversitesi Eğitim Fakültesi Dergisi, 66. https://doi.org/10.9779/pauefd.1574813 [Google Scholar] [Crossref]

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