RET Site: Teacher-Researcher Co-Design of Adaptive GenAI for Computational Thinking

Many high school students have little opportunity to understand how artificial intelligence (AI) works or to develop the computational thinking skills needed to engage with it critically. This makes it essential that teachers be not only knowledgeable about AI but also skilled enough to guide student inquiry into it. Research Experiences for Teachers (RET) programs represent one promising pathway, yet fewer than 40% of participants ever translate what they learned into actual classroom practice. This shortfall stems from several recurring challenges: research projects are often too distant from real teaching contexts, support ends with the summer program itself, and teachers are treated as beginners rather than as experts in the learning sciences. This project addresses these challenges by requiring teachers to work side-by-side with professors as genuine research partners. Together, they develop AI tools designed specifically to improve how computational thinking and AI are taught in high schools. Because teachers are central members of the development process, the resulting tools reflect actual classroom needs. Participating teachers also gain the knowledge and research skills needed to mentor their own students in AI and computational thinking projects. This project establishes a teacher-researcher co-design model in which ten high school teachers annually conduct six-week AI research experiences at the University of North Texas (UNT). Beyond the summer, the UNT team provides continued support to help teachers transfer what they have learned into their classrooms. The co-design model requires teachers to contribute their pedagogical expertise directly to the research projects they pursue alongside faculty, positioning them as active AI co-creators and ensuring that research is grounded in classroom realities from the outset. The project will also generate new theoretical knowledge about how domain expertise outside computer science and AI shapes complex technology design, and how non-technical experts can meaningfully participate in AI research. Longitudinal study of co-design processes and student interactions with the resulting systems will deepen understanding of computational thinking development and effective human-AI collaboration. Over three years, 30 teachers will be transformed into AI co-designers, each reaching several hundred students annually. Open-source tools and curricula produced through the project will be made available to schools nationwide, and the co-design model itself will demonstrate how pedagogical expertise can drive AI research that genuinely serves educational needs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2601493 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Junhua Ding | Institution: University of North Texas, DENTON, TX | Award Amount: $599,496 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2601493 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2601493.html