The project was developed at Rochester Institute of Technology to study how AI support can be integrated into coursework without giving up educator control or reducing learning to answer generation.
AI Foundry
Rochester Institute of Technology
TutorBot is presented here as a university-sponsored project with explicit educational and research boundaries.
TutorBot grew out of conversations at Rochester Institute of Technology about how large language models were already affecting student study habits and course work.
Rather than treating AI use as either inevitable progress or a problem to ignore, the project asks a more practical question: what happens when instructors can shape the tutoring context, boundaries, and pedagogical goals directly?
That framing makes TutorBot both a course-support platform and a vehicle for educational research on structured AI use.
Faculty define assistant behavior, assignment structure, and question context rather than accepting a generic tutoring experience.
TutorBot is designed around actual course questions, expected concepts, and common mistakes so support is grounded in the learning task.
The platform is intended to support study of AI-assisted learning, not just broaden access to another chatbot.
TutorBot does not exist to automate course assessment or collapse instructional judgment into model output.
Current TutorBot workflows center on course-specific assignments, faculty-configured assistants, and role-aware experiences for students, faculty, and administrators.
Courses can use assistants for guided tutoring, feedback, and other instructor-defined educational roles.
Faculty author question text, example answers, concepts, and misconceptions that shape how assistants respond.
Students encounter AI support through assignment sessions and other controlled workflows rather than only through a general chat entry point.
Authorized users can review tutoring sessions and interaction patterns in ways that support oversight and research.
TutorBot is in an active pilot phase. The current work focuses on refining course workflows, assistant behavior, and the public explanation of the project while keeping the academic framing explicit.
Developed through Rochester Institute of Technology and the AI Foundry.
Director, AI Hub and Initiatives; Jane King Harris Professor, School of Chemistry and Materials Science
Provides academic leadership for the project and its research framing within RIT.
cjcscha@rit.eduPrincipal Lead, AI Foundry and TutorBot; Assistant Research Professor, School of Chemistry and Materials Science
Leads platform implementation, workflow design, and the translation of educational goals into product behavior.
tefsch@rit.eduThe public pages are meant to explain what TutorBot is, why it exists, and how it is being piloted. Existing users can sign in directly, while faculty and academic partners can request access when participation is appropriate.