My research question

How can AI writing support for first-year writers be designed around how people actually learn?

First-year writers are already using AI, mostly at the very beginning of a project, when they are choosing a topic and looking for ideas. Research on learning has a lot to say about attention, practice, and feedback at that moment.

I want AI writing support that works with how people learn instead of doing the thinking for them: tools that help students think, give them feedback that is fair and honest, and leave the writing in their own hands.

Research areas

  • The science of learning

  • Psychology and student well-being

  • The neuroscience of attention and memory

  • User-centered design

  • Fairness in feedback

  • Classroom action research

Action research through the Writing & Thinking Institute (WTI)

Through San Antonio College’s Writing & Thinking Institute, I have been running classroom action research on project-based learning paired with generative AI in first-year composition. I designed and administered classroom surveys in my own courses to see how students actually experienced the work. A few descriptive findings:

  • Students in my first-year composition courses found the course guides helpful for improving their writing.

  • Many said ChatGPT helped them, mainly for generating ideas and choosing or narrowing a topic.

  • Some described a worry about losing originality when they used it.

  • When I surveyed incoming students before this work began, many had heard of ChatGPT, but fewer described having used it.

Some of these findings come from open-ended answers, so I treat them as preliminary. They describe what students reported. They don’t prove that project-based learning with AI works, and I don’t present them that way. What they do is point to where better measurement is needed: students use AI at the front end of writing, and a meaningful share of them already worry about losing originality.

Selected references that shape my work

Anderson, P., Anson, C. M., Gonyea, R. M., & Paine, C. (2015). The contributions of writing to learning and development: Results from a large-scale multi-institutional study. Research in the Teaching of English, 50(2), 199 to 235.

Dehaene, S. (2020). How we learn: Why brains learn better than any machine… for now. Viking.

Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving students’ learning with effective learning techniques. Psychological Science in the Public Interest, 14(1), 4 to 58.

Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81 to 112.

Immordino-Yang, M. H., & Damasio, A. (2007). We feel, therefore we learn: The relevance of affective and social neuroscience to education. Mind, Brain, and Education, 1(1), 3 to 10.

Ludwig, L. D., & Zakrajsek, T. D. (2026). The science of learning meets AI: A practical faculty guide to purposeful integration, student engagement, and ethical practice. Routledge.

Nicol, D. J., & Macfarlane-Dick, D. (2006). Formative assessment and self-regulated learning: A model and seven principles of good feedback practice. Studies in Higher Education, 31(2), 199 to 218.

Norman, D. (2013). The design of everyday things (Rev. and expanded ed.). Basic Books.

Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68 to 78.

Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257 to 285.

Wiggins, G., & McTighe, J. (2005). Understanding by design (Expanded 2nd ed.). ASCD.