Toward Scalable and Responsible Integration of Course-Specific AI Tutors: Instructor Experiences with a Campus-Wide Platform

Despite rapid investment in generative AI across higher education, how instructors create, evaluate, and implement course-specific AI tutors remain empirically underexplored, highlighting critical tensions between institutional adoption and instructional practices. Drawing on interviews with 20 instructors, teaching assistants, and instructional designers at a large U.S. research university, we examine how participants engaged with a university-wide platform for creating course-specific AI tutors. Our findings reveal how instructors’ epistemic beliefs and pedagogical orientations shaped their perceptions of appropriate and inappropriate AI uses, as well as how instructional challenges motivated tutor creation across disciplines, class sizes, and course levels. We also identified three key patterns in instructor evaluation of course-specific AI tutors, along with the pedagogical, technical, and ethical implementation challenges they faced. We contribute timely insights to inform research, platform development, and institutional policy toward the responsible and scalable integration of course-specific AI tutors in higher education.