Views: 397
Arab World English Journal (AWEJ) Special Issue on CALL Number 12. July 2026 Pp. 109-125
Department of English, College of Education for Humanities, University of
Al-Anbar, Ramadi, Iraq
Department of English, College of Education for Humanities, University of
Al-Anbar, Ramadi, Iraq
Department of Artificial Intelligence, Computer Sciences and Information
Technology, University of Al-Anbar, Ramadi, Iraq
This paper examines how the syntactic complexity of academic writing is affected mainly by the source of authorship (AI-generated or human) or by the genre of disciplinary writing (linguistic or literary). The primary purpose is to test the syntactic-complexity differences between these variables and to establish the degree of influence of genre conventions on structural variation. The importance of the research is that it adds to the existing discussions about AI as a phenomenon in academic writing and, specifically, whether AI-generated texts are capable of syntactically reproducing the specific norms of a specific discipline of writing. To this end, the comparative corpus-based design was used. The sample consisted of 20 introduction sections: equal numbers of linguistic and literary texts and equal numbers of human-authored AI-generated texts. The Second Language Syntactic Complexity Analyzer (L2SCA) extracts fourteen syntactic complexity measures, which include length of production unit, subordination, coordination and phrasal sophistication. The results indicate that the complexity of syntax is genre-based and not source-based. Although no differences were found to be constant in both AI-generated and human academic introductions in linguistic data, there were much higher levels of subordination in literary texts of human origin. In general, the discipline genre had a more significant effect on syntax variation than the authorship source. The paper suggests the implementation of genre-sensitive models to assess AI-written academic texts and recommends additional studies that would use a bigger sample and discourse analysis.
Alheety, A.A., khalaf, M. K., & Mohammed, H.J. (2026).Syntactic Complexity in AI-Generated vs. Human-Authored Linguistic and Literary Texts. Arab World English Journal (AWEJ) Special Issue on CALL 17. (12) 109-125.
Abdul Ghafoor, N. H., & Challob, A. I. (2021). The influence of using online discussion on developing EFL students’ grammar knowledge. Korean Journal of English Language and Linguistics, 21, 837–855.http://doi.org/10.15738/kjell.21..202109.837.
Al-Zubaidi, K., Jaafari, M., & Touzani, F. Z. (2024). Impact of ChatGPT on academic writing at Moroccan universities. Arab World English Journal (AWEJ) Special Issue on ChatGPT, 4–25. https://dx.doi.org/10.24093/awej/ChatGPT.1
Biber, D., & Gray, B. (2010). Challenging stereotypes about academic writing: Complexity, elaboration, explicitness. Journal of English for Academic Purposes, 9(1), 2–20. https://doi.org/10.1016/j.jeap.2010.01.001.
Biber, D., et al. (2011). Should we use characteristics of conversation to measure grammatical complexity in L2 writing development? TESOL Quarterly, 45(1), 5–35. https://doi.org/10.5054/tq.2011.244483.
Bulté, B., & Housen, A. (2014). Conceptualizing and measuring short-term changes in L2 writing complexity. Journal of Second Language Writing, 26, 42–65. https://doi.org/10.1016/j.jslw.2014.09.005.
Casal, J. E., & Kessler, M. (2023). Can linguists distinguish between ChatGPT/AI and human writing? A study of research ethics and academic publishing. Research Methods in Applied Linguistics, 2(3), 100068. https://doi.org/10.1016/j.rmal.2023.100068.
Chung, E. et al. (2025). A mixed-methods study of strategy use in academic vocabulary learning among first-year undergraduates in Hong Kong. Journal of English for Academic Purposes, 74, 101482. https://doi.org/10.1016/j.jeap.2025.101482.
González-Pereira et al. (2010). A new approach to the metric of journals’ scientific prestige: The SJR indicator. Journal of Informetrics, 4(3), 379–391. https://doi.org/10.1016/j.joi.2010.03.002.
Khalaf, M. K., & Fadhil, R. A. (2020). Analysis of written discourse errors committed by Iraqi EFL learners. Dirasat: Human and Social Sciences, 47(2), 521–532.
Ling Jen, S., & Salam, A. R. (2024). Using artificial intelligence for essay writing. Arab World English Journal (AWEJ) Special Issue on ChatGPT, 90–99. https://dx.doi.org/10.24093/awej/ChatGPT.5
Lu, X. (2011). A corpus-based evaluation of syntactic complexity measures as indices of college-level ESL writers’ language development. TESOL Quarterly, 45(1), 36–62. https://doi.org/10.5054/tq.2011.240859.
Mukhlif, M. I., & Challob, A. I. (2021). Enhancing vocabulary knowledge among secondary school EFL students by using Facebook. Turkish Online Journal of Distance Education (TOJDE), 22(3), 246–261. https://doi.org/10.17718/tojde.961834.
Ortega, L. (2003). Syntactic complexity measures and their relationship to L2 proficiency: A research synthesis of college-level L2 writing. Applied Linguistics, 24(4), 492–518. https://doi.org/10.1093/applin/24.4.492.
Pallant, J. (2020). SPSS survival manual: A step-by-step guide to data analysis using IBM SPSS (7th ed.). Routledge.
Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics. Pearson Education.
Pryma, V., Pelivan, O., Teletska, T., Tsobenko, O., & Zagrebelna, N. (2025). AI-writing assistants and student competence: A linguistic aspect. Arab World English Journal (AWEJ), Special Issue on Artificial Intelligence,319–329. https://dx.doi.org/10.24093/awej/AI.18
Asia Alheety is a teacher in Hanaa al Alshaibani Secondary School for Girls, Iraq. ition for several years. She also participated in English curriculum development courses with UNESCO to develop the English language curriculum in Iraq in 2002. In addition, she took part in training courses for English language teachers and instructors in Ramadi. She is also interested in publishing research in the field of Applied linguistics. ORCID ID: https://orcid.org/0009-0001-0099-7838
Meethaq Khamees Khalaf is an Assistant Professor and the Head of the Department of English at the University of Anbar, Iraq. With a focus on English Language and Linguistics, he holds a B.A. and an M.A. from the College of Education for Humanities at the same university. Over his academic career, he has taught various subjects, including Linguistics, Morphology, Discourse Analysis, and composition, and has supervised several impactful theses in his field. ORCID ID:https://orcid.org/0000-0002-9352-3999
Hussam J. Mohammed is a Doctor of Philosophy specializing in Digital Forensics and Artificial Intelligence. He earned his BSc and MSc degrees in computer science from the University of Anbar, Iraq, before pursuing his PhD in digital forensics and cybersecurity at the University of Plymouth, UK. Currently, Hussam is a faculty member in the Al Department at the University of Anbar, where he is actively involved in research and academic development. ORCID ID : https://orcid.org/0000-0001-9925-3945