The Linguistic Impact of Generative Artificial Intelligence on Academic Writing: A Corpus-Based Investigation of Human–AI Text Production

Abstract

The emergence of Generative Artificial Intelligence (GAI) has rapidly changed academic writing by offering high-quality tools that are able to generate coherent, grammatically correct, and contextually relevant text. Although the use of these technologies is increasingly becoming popular, there is still the issue of the linguistic impact of these technologies, authorship authenticity, and the effects of these technologies on academic integrity. This research paper explores the linguistic effects of generative artificial intelligence on academic text on the production of text by human and artificial intelligences based on a corpus analysis of human and AI text production. The research design used was a quantitative cross-sectional research design, and 275 participants who comprised of students, researchers, and faculty members of various academic disciplines were used to collect data. The questionnaire was a structured questionnaire comprising 30 items, six constructs, to measure awareness and use of generative AI, linguistic features of AI-generated writing, effects on writing quality, human-AI disparities in text generation, ethical issues, and future implications of AI-assisted writing. The data were analysed with the help of descriptive statistics, reliability analysis, and chi-square testing with the help of SPSS Version 29.

From the results, it was found that academic writers were highly aware and made use of generative AI tools. The respondents perceived that AI-generated texts were grammatically correct and had a good vocabulary, while human-generated texts were more creative, with unique authorial voice and varied language. The results also showed that using AI to write is far more effective in improving clarity, cohesion, organization, and quality of the writing. Nevertheless, there were major apprehensions about academic honesty, the risk of plagiarism, and overreliance on automatic content creation. The participants also agreed that the development of institutional guidelines, AI literacy programmes, and human supervision processes to ensure responsible use should be supported. Furthermore, the respondents believed that generative AI would in the future effect more in the field of scholarly communication and academic writing.

The given study can be implemented into the existing body of work in the field of artificial intelligence and corpus linguistics since it offers the empirical evidence of the dynamic relationship between human and AI-produced academic speech. The findings highlight the importance of the technological innovation/moral responsibility balance, critical thinking and authenticity of the scholarship in the contemporary higher educational institution.

References

Afzal, M., Arshad, N., & Shaheen, A. (2025). ChatGPT and the future of academic writing: Enhancing productivity and creativity. Journal of Engineering and Computational Intelligence Review, 3(1), 1–11.

Alghazo, S., Rababah, G., El-Dakhs, D. A. S., & Mustafa, A. (2025). Engagement strategies in human-written and AI-generated academic essays: A corpus-based study. Ampersand, 15, Article 100237. https://doi.org/10.1016/j.amper.2025.100237   

Ali, M., & Ali, S. (2024). Can artificial intelligence preferences be an alternative to human linguistic choices? A multidimensional analysis of research abstracts of English linguistics. Journal of Nusantara Studies, 9(2), 514–536.

Arshad, N., Baber, M. U., & Ullah, A. (2024). Assessing the transformative influence of ChatGPT on research practices among scholars in Pakistan. Mesopotamian Journal of Big Data, 2024, 1–10.

Curry, N., Baker, P., & Brookes, G. (2024). Generative AI for corpus approaches to discourse studies: A critical evaluation of ChatGPT. Applied Corpus Linguistics, 4(1), Article 100082. https://doi.org/10.1016/j.acorp.2024.100082

Du, M., Lu, M., Dai, Y., & Wang, F. (2025). A corpus-based analysis of verb collocations in human and AI-generated IELTS writing. Journal of Educational Technology and Innovation, 7(2).

Fu, L., & Liu, L. (2024). What are the differences? A comparative study of generative artificial intelligence translation and human translation of scientific texts. Humanities and Social Sciences Communications, 11(1), 1–12. https://doi.org/10.1057/s41599-024-02670-5

Khudai, U., Thakur, R. S., Haq, A. U., Bibi, R., & Taj, A. (2025). Improving academic writing for English learners: Innovative approaches in higher education. The Critical Review of Social Sciences Studies, 3(3), 2406–2420.

Kurt, G. (2025). A qualitative exploration of pre-service English teachers' integration of generative artificial intelligence in corpus-based language pedagogy. Computer Assisted Language Learning, 1–34. Advance online publication. https://doi.org/10.1080/09588221.2025.2471514

Liu, T., & Zou, B. (2025). Enhancing students' L2 writing by integrating artificial intelligence with corpus-based language pedagogy. Computer Assisted Language Learning, 1–24. Advance online publication. https://doi.org/10.1080/09588221.2025.2485803

Mahmood, B., Masood, T., & Mehmood, H. N. (2025). A corpus-based comparative analysis of human and ChatGPT-generated academic abstracts. International Research Journal of Arts, Humanities and Social Sciences, 2(3), 660–676.

Mateen, H. (2024). Harnessing systemic functional linguistics (SFL) for AI-generated feedback on ESL writing: A corpus-based study. Corporum: Journal of Corpus Linguistics, 7(2), 103–144.

Pereira, R., Reis, I. W., Ulbricht, V., & Santos, N. D. (2024). Generative artificial intelligence and academic writing: An analysis of the perceptions of researchers in training. Management Research: Journal of the Iberoamerican Academy of Management, 22(4), 429–450. https://doi.org/10.1108/MRJIAM-01-2024-1505

Taj, A., Khudai, U., & Thakur, R. S. (2026). The effectiveness of AI-based evaluation tools for English language learners. Inverge Journal of Social Sciences, 5(3), 247–257.

Tudino, G., & Qin, Y. (2024). A corpus-driven comparative analysis of AI in academic discourse: Investigating ChatGPT-generated academic texts in social sciences. Lingua, 312, Article 103838. https://doi.org/10.1016/j.lingua.2024.103838

Wang, L., & Ren, B. (2024). Enhancing academic writing in a linguistics course with generative AI: An empirical study in a higher education institution in Hong Kong. Education Sciences, 14(12), Article 1329. https://doi.org/10.3390/educsci14121329

Wang, Q., Deng, Y., & Liu, D. (2025). Corpus-driven and AI-assisted learning in teaching business academic writing: Examining their efficacies and the predicting/moderating effects of learner variables. Computer Assisted Language Learning, 1–29. Advance online publication. https://doi.org/10.1080/09588221.2025.2488240

Wu, J. (2025). A corpus-based multidimensional analysis of linguistic features between human-authored and ChatGPT-generated compositions. International Journal of Linguistics, Literature and Translation, 8(5), 102–110.

Xu, H., & Huang, Y. (2025). Corpus-based translation and interpreting studies in the age of AI: Innovations and challenges. In Translation studies in the age of artificial intelligence (pp. 85–99). Routledge. https://doi.org/10.4324/9781032758634-7

Zhao, W. (2025). Reconstructing stance in EFL doctoral thesis writing through generative artificial intelligence. Humanities and Social Sciences Communications, 12(1), Article 1963. https://doi.org/10.1057/s41599-025-04920-x

Zhu, L., Su, Y., Xu, S., & Liu, K. (2025). Enhancing EFL argumentative writing through an AI-powered corpus: Impact on learner writing proficiency. Computer Assisted Language Learning, 1–26. Advance online publication. https://doi.org/10.1080/09588221.2025.2491908