We are excited to announce that two projects have been selected for the 2026 Provost Fellows Program, advancing WPI’s commitment to pedagogical excellence and curricular innovation. Sponsored by the Office of the Provost, these transformative projects explore the application of generative AI in broad and creative ways. Congratulations to the individuals selected for this new initiative, which empowers faculty to lead bold, lasting change in our academic community.
John-Michael Davis: “Transforming Stakeholder Engagement Pedagogy Through Generative AI Simulations”
This project will develop, pilot, and refine an immersive AI-powered simulation to help students practice ethical stakeholder engagement and qualitative interviewing. Integrated into WPI’s ID2050 course, the browser-based tool will feature lifelike personas grounded in real-world data and offer structured feedback to support skill development. Students and faculty will co-design and test the simulation to ensure usability and relevance. The simulation will be hosted on the Global Lab website, making it scalable across disciplines. The fellowship will also support external grant development to expand adoption, advancing inclusive, experiential learning through generative AI and transforming stakeholder engagement pedagogy across WPI.
Ahmet Can Sabuncu: “Adaptive Troubleshooting Through Generative AI: A Conversational Agent for Cognitive Scaffolding in Ill-Structured Engineering Problems”
This project develops an AI-powered conversational agent that guides engineering students through troubleshooting ill-structured problems using Socratic dialogue. By simulating real-world complexity and promoting divergent reasoning, the agent helps students identify root causes from partial, ambiguous cues. Case studies tagged with cognitive complexity types are delivered through Canvas-integrated chat interfaces. Logs and reflections will be analyzed to assess reasoning paths and learning outcomes. The project produces reusable tools, a faculty-facing training module, and case studies aligned with course outcomes. This approach supports adaptive expertise and offers a novel methodology for understanding student cognition in engineering problem-solving.
