Gregory Nelson
University of Maine, ME·Education Research·Early-career
Generative artificial intelligence (GenAI) tools now write code, analyze data and handle other computing tasks, and today's computing students will decide how these tools get used across the economy. Yet computing courses mostly teach students to use such tools, not to judge when, why and how to use them.
- Fields
Education Research
Computer Science
- Topics
computing education
generative AI
GenAI
- Grants
- 1 known grant, through May 2031
What they work on
- Use design-based research to iteratively develop and evaluate peer reflection groups that build professional skills including iterative improvement, lifelong learning, GenAI use and psychological safety, with minimal instructor time.
- Test the reflection group designs in three formats, as drop-in course assignments, a standalone one-credit course, and within a computing ethics course, refining them through co-design workshops with faculty.
- Develop a video-assisted comparative reflection assignment in which students record programming sessions using generative AI tools in different ways, then compare process, productivity and learning across sessions.
- Assess the instructional techniques longitudinally by combining quantitative measures of professional skills and effective GenAI use with thematic analysis of student reflections, interviews and programming session videos.
More about the research
Employers consistently point to the missing pieces: professional judgment, habits of continuous learning and reflective thinking. This project develops and evaluates scalable teaching methods that build those professional skills alongside thoughtful GenAI use.
Why it matters
Computing graduates increasingly work alongside AI coding tools but often arrive without the judgment and self-directed learning habits employers want. Adoptable reflection assignments, a one-credit course design and faculty workshops could give instructors at many institutions practical, low-overhead ways to build those professional skills and more deliberate AI use in their students.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Christopher Bogart
Carnegie Mellon University, PA·Education Research
Identify how early-career developers and experienced students divide programming work between themselves and AI tools, using interviews and direct observation to find patterns of overtrust, lost task awareness and skill decline.
Fred Martin
University of Texas at San Antonio, TX·Education Research
Document current understandings, policies and practices for using and teaching AI literacy in partner middle school districts in Texas and New York, working with teachers and technology coaches.
Liping Liu
Lawrence Technological University, MI·Education Research
Implement an existing toolkit of training videos, activities, instructor guides and workshops for engineering undergraduate research mentors through a new Undergraduate Research Excellence Network that coaches mentors in making adoption plans.
The school
University of Maine: 10 labs on PI Pages, $199M a year on research, 81 research doctorates a year.
Reach out
A start for your email. Fill in the brackets.