Empirical studies of writing and generative AI: Introduction to the special issue
DOI:
https://doi.org/10.17239/jowr-2026.17.03.01Keywords:
writing and generative AI, qualitative inquiry, corpus analysis, mixed-methods research, computational linguistics, experimental comparisonsAbstract
This special issue of the Journal of Writing Research brings together seven empirical studies of the relationship between writing and generative AI, examining what can be systematically observed and measured about the functioning of generative AI in educational and professional writing contexts. Collectively, the studies demonstrate the necessity and value of methodological pluralism for investigating a complex, rapidly evolving phenomenon. In their contributions, the researchers use experimental comparisons, mixed-methods intervention designs, corpus-based analyses, computational linguistic techniques, and qualitative interpretive approaches. Taken together, these methods enable lines of inquiry that no single approach could sustain: comparisons of AI and human performance in professional writing tasks; analyses of how writers at different ages and levels of expertise engage AI tools; examinations of how assessment systems register and respond to AI-generated prose; and investigations of how human readers interpret texts with ambiguous authorship. By foregrounding both the affordances and limitations of different methodological traditions, the articles present a multifaceted approach to the study of writing and generative AI.
References
Anson, C. M., & Cole, K. (in press). Generative AI does all the work for the writer. In C. Basgier, A. Mills, M. Olegnik, M. Rodak, & S. Sharma (Eds.), Bad ideas about AI and writing: Toward generative practices for teaching, learning, and communication. The WAC Clearinghouse and University Press of Colorado.
Anson, C. M., & Straume, I. (2022). Amazement and trepidation: Implications of AI-based natural language production for the teaching of writing. Journal of Academic Writing, 12(1), 1-9. https:doi.org/https://doi.org/10.18552/joaw.v12i1.820
Aguilar, G. L. (2025). AI writing is always embodied: Building a critical awareness of the invisible labor of humans-in-the-loop in AI products. College Composition and Communication, 77(1), 39-61. https://doi.org/10.58680/ccc202577139
Association for Writing Across the Curriculum (2025). AWAC Statement on AI and writing across the curriculum. https://wacassociation.org/statement-on-ai-writing-tools-in-wac/
Belland, B. R. (2013). Scaffolding: Definition, current debates, and future directions. In M. J. Spector, M. D. Merril, J. Elen, & M. J. Bishop (Eds.), Handbook of research on educational communications and technology (pp. 505-518). Springer. https://doi.org/10.1007/978-1-4614-3185-5_39
Bridgeman, B, Trapani, C. & Yigal, A. (2012). Comparison of human and machine scoring of essays: Differences by gender, ethnicity, and country. Applied Measurement in Education 25(1), 27-40. https://doi.org/10.1080/08957347.2012.635502
Bui, N. M., & Barrot, J. (2025). Using generative artificial intelligence as an automated essay scoring tool: a comparative study. Innovation in Language Learning and Teaching, 1-16. https://doi.org/10.1080/17501229.2025.2521003
Ericsson, P. F., & Haswell, R. (2006). Machine scoring of student essays: Truth and consequences. Utah State University Press.
Gegg‑Harrison, W., & Shapiro, S. (2025). From policing to empowerment: Promoting student agency in the context of AI text‑generators and AI‑detection tools. In C. Wang & Z. Tian (Eds.), Rethinking writing education in the age of generative AI (pp. 26–41). Routledge. https://doi.org/10.4324/9781003426936‑4
Ghanbari, N. (2019). Promoting fairness in EFL writing assessment: Are there any effects of the writers’ awareness of the rating criteria? Journal of Asia TEFL, 16.
Herrington, A., & Moran, C. (2006). WritePlacer Plus in place: An exploratory case study. In P. F. Ericsson & R. H. Haswell (Eds.), Machine scoring of student essays: Truth and consequences (pp. 114-129). Utah State University Press. https://doi.org/10.18823/asiatefl.2019.16.1.173
Haas, C., & Flower, L. (1988). Rhetorical reading strategies and the construction of meaning. College Composition and Communication, 39(2), 167-183.
Hayes, J. R. (2012). Modeling and Remodeling Writing. Written Communication, 29(3), 369-388. https://doi.org/10.1177/0741088312451260
Hidi, S., & Renninger, A. (2006). The four-phase model of interest development. Educational Psychologist, 41(2), 111–127. https://doi.org/10.1207/s15326985ep4102_4
Hodges, T. S., Wright, K. L., Wind, S. A., Matthews, S. D., Zimmer, W. K., & McTigue, E. (2019). Developing and examining validity evidence for the writing rubric to inform teacher educators (WRITE). Assessing Writing, 40, 1-13. https://doi.org/10.1016/j.asw.2019.03.001
Jian, Y., Hao, J., Fauss, M., & Li, C. (2024). Toward fair detection of AI-generated essays in large-scale writing assessments. In S. M. Olney, I. Chounta, Z. Liu, O. C. Santos, & I. I. Bittencourt (Eds.), Artificial intelligence in education: Proceedings of the 25th International Conference of AIED, Recife, Brazil, July 8-12. https://link.springer.com/book/10.1007/978-3-031-64312-5
Kelly-Riley, D., Macklin, T., & Whithaus, C. (2024). Toward fairness in writing assessment. In D. Kelly-Riley, T. Macklin, & C. Whithaus (Eds.). Considering students, teachers, and writing assessment (pp. 107-116). The WAC Clearinghouse and University Press of Colorado.
Lee, S., Choe, H., Zou, D., & Jeon, J. (2025) Generative AI (genAI) in the language classroom: A systematic review. Interactive Learning Environments. https:doi.org/ 10.1080/10494820.2025.2498537
Lo, N., Wong, A., & Chan, S. (2025). The impact of generative AI on essay revisions and student engagement. Computers and Education Open, 9, 100249. https://doi.org/10.1016/j.caeo.2025.100249
McQuire, A., Qureshi, W., & Saad, M. (2024). A constructivist model for leveraging genAI tools for individualized, peer-simulated feedback on student writing. International Journal of Technology in Education, 7(2), 326-352.
Modern Language Association and Conference on College Composition and Communication (2024). Generative AI and policy development: Guidance from the MLA-CCCC task force. https://cccc.ncte.org/mla-cccc-joint-task-force-on-writing-and-ai
National Council of Teachers of English (2013). NCTE position statement on machine scoring. National Council of Teachers of English. https://cdn.ncte.org/nctefiles/resources/positions/machinescoring_booklet.pdf
Nimi, H., Lu, M., & Chacon, J. C. (2025). Embodied co-creation with real-time generative AI: An Ukio-E interactive art installation. Digital, 5(4), 1-21. https://doi.org/10.3390/digital5040061
Noller, J. (2025). 4E cognition and the coevolution of human–AI interaction. Discover Artificial Intelligence, 5(323), 1-19. https://doi.org/10.1007/s44163-025-00595-0
Plakans, L., & Lee, K. (2025). Fairness, justice, and criticality: Reviewing second language writing assessment. Language Teaching, Firstview, 1-28. https://doi.org/https://doi.org/10.1017/S0261444825100876
Perl, S. (1979). The composing processes of unskilled college writers. Research in the Teaching of English, 13(4), 317-336.
Poe, M., & Elliot, N. (2019). Evidence of fairness: Twenty-five years of research in Assessing Writing. Assessing Writing, 42. https://doi.org/10.1016/j.asw.2019.100418
Polanyi, M. (1958). Personal knowledge. Routledge.
Rose, D., & Martin, J. R. (2012). Learning to write/reading to learn: Genre, knowledge and pedagogy in the Sydney school. University of Toronto Press.
Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55, 68-78. https://doi.org/ 10.1037//0003-066x.55.1.68
Sharples, M., & Pérez y Pérez, R. (2022). Story machines: How computers have become creative writers. Routledge. https://doi.org/10.4324/9781003161431
Steve, C., Roland, C., & Joseph, O. (2025). Assessing the quality and accuracy of AI-generated feedback in STEM v. humanities education. Research Gate. https://www.researchgate.net/publication/393465033
Tang, K-S. (2025). AI-textuality: Expanding intertextuality to theorize human-AI interaction with generative artificial intelligence. Applied Linguistics, XX, 1-19. https://doi.org/10.1093/applin/amaf016
Vygotsky, L. (1978). Mind in society: The development of higher psychological processes. M. Col, V. Johnson-Steiner, S. Scribner, & E. Souberman (Eds.). Harvard University Press.
Wilkinson, I. A. G., & Gaffney, J. S. (2015). Literacy for schooling: Two-tiered scaffolding for learning and teaching. In L. Corno & E. M. Anderman (Eds.), Handbook of educational psychology (pp. 243–258). Routledge.
Wood, D., Bruner, J. S., & Ross, G. (1976). The role of tutoring in problem solving. Journal of Child Psychology and Psychiatry, 17(2), 89-100.
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