Christopher Eaton

Chris brings together two areas in his pedagogy and scholarship: education and writing. From the education side, he has expertise in curriculum development, multimodality, and emerging pedagogical technologies. On the writing side, he is a rhetorician who has a keen interest in writing transfer. Both come together in fascinating ways in his classrooms (though the “fascinating” part may only apply to him). These lenses also inform the vast majority of his scholarship.
 

The connection between emerging technologies, multimodal assessment, and curriculum features prominently in Chris’s work. He has been working on several projects related to generative artificial intelligence, small language models, and genAI assessments. Along with colleagues, he formed the Generative AI Literacies Lab (GAILL) to study how generative AI affects teaching, learning, and literacies. He operates a web resource called Navigating AI Literacy, which is updated from time to time as research evolves. He publishes regularly on topics related to generative AI, writing, and literacy.
 

He is interested in examining how learners “stitch” together various modes and digital tools to support their education. The results of some of this work can be seen through the podcast series showcasing student podcast episodes developed in his utmONE Scholars classes.
 

Most of his work involves students in some capacity. Students are central to everything Chris does as a teacher and as a scholar, so it is only fitting that they have a bigger place not just as students in a classroom but also as co-constructors of knowledge. Student involvement in projects is essential—if students are not offered a seat at the table, what’s the point of academia?
 

When he is not teaching, researching, creating new projects, or avoiding email, Chris loves to hike, trek through mountains, read, and enjoy time with friends and family.
 

Select Publications
 

Eaton, C. (forthcoming 2026). Epistemological processes and artificial intelligence: Building through the unknown. Journal of the Scholarship of Teaching and Learning.
 

Eaton, C. (2026). AI-generated reading summaries are enough: AI tools can rhetorically support but not replace reading. In C. Basgier, A. Mills, M. Olejnik, M. Rodak, & S. Sharma (Eds.), Bad ideas about AI and writing: Toward generative practices for teaching, learning, and communication (pp. 65–70). WAC Clearinghouse. https://doi.org/10.37514/PER-B.2026.2777.2.08
 

Eaton, C. (2026). Writers thinking with machines: Student metacognition when writing with AI tools. Across the Disciplines.
 

Farhan, A., & Eaton, C. (2026). Learners do not trust genAI, but they find it useful anyway: Pedagogical implications of the enduring allure of genAI for learning. Current Issues in Education, 27(2), 1–17. https://doi.org/10.14507/cie.vol27iss2.2395
 

Liu, S., Ye, R., Eaton, C., Simion, B., & Liut, M. (2026). A comparative study of technical writing feedback quality: Evaluating LLMs, SLMs, and humans in computer science topics. International Conference on Artificial Intelligence in Education (AIED 2026).
 

Eaton, C., Harris, K., & Vearncombe, E. (2025). Student evaluative judgements when writing with artificial intelligence: The disconnect between structural and conceptual knowledge. Journal of Academic Writing, 15(2), 1–12. https://publications.coventry.ac.uk/index.php/joaw/article/view/1346
 

Eaton, C., Belmonte, I., Enaya, T., Flood, S., Khalil, Z., Makwanda, A., Shah, M.M.A., Toma, A., Wang, T., & Yu, C. (2025). Where we're at, what we must know, and where we can go: A systematic review of research about writing and artificial intelligence. Discourse and Writing, 35, 101–125. https://doi.org/10.31468/dwr.1167
 

Enaya, T., & Eaton, C. (2025). Probing biases with generative AI. In A. Vee, T. Laquintano, & Schnitzler, C. (Eds.), TextGenEd: Teaching with generative technologies continuing experiments. https://wac.colostate.edu/repository/collections/continuing-experiments/august-2025/rhetorical-engagements/probing-biases-with-generative-ai/