2025 ASEE Annual Conference & Exposition

Teaching interdisciplinary doctoral programs presents unique challenges, requiring faculty to support diverse methodological backgrounds while providing individualized mentoring. While generative AI offers potential support for educators, little is known about its effective integration in doctoral education. This autoethnographic study examines one faculty member's experience incorporating AI as a thinking partner in a required doctoral course. Through analysis of three interviews conducted across an academic term, complemented by course documentation and collaborative dialogues, the study explores both the technical possibilities ("Can I?") and ethical implications ("Should I?") of AI integration. Results reveal three pivotal moments: using AI to co-construct assignments with students, leveraging AI for real-time analysis of student learning, and discovering how AI's limitations can spark pedagogical insight. The findings suggest that AI can make the inherent challenges of doctoral education more productive rather than merely manageable. Rather than reducing teaching complexity, AI created productive constraints that supported faculty judgment while strengthening connections with students. This study offers practical guidance for faculty considering AI integration while contributing to our theoretical understanding of doctoral education in an AI-enhanced future.