Beyond Perspectives: A Trio-Ethnography of Interpretation Evolution in LLM-Supported Programming Education

As generative AI reshapes programming education, instructors often infer student learning purely from classroom observation, missing crucial context. This study uses trio-ethnography, pairing two educators with differing teaching philosophies alongside an undergraduate student, across three dialogic sessions exploring AI-assisted learning. The student's firsthand narratives revealed learning processes invisible to instructors, prompting both educators to revise assumptions about assessment, transparency, and pedagogy. Applications include informing computing curriculum design, faculty development around AI-integrated teaching, and demonstrating collaborative reflective methodologies that other educational researchers could adopt to better understand student AI use beyond surface-level classroom behavior. Authors: Jennie Ren, Jordan H. McDowell, Kyrie Zhixuan Zhou Paper: https://arxiv.org/abs/2607.22463v1