Beyond Perspectives: A Trio-Ethnography of Interpretation Evolution in LLM-Supported Programming Education
2026-07-24 • Human-Computer Interaction
Human-Computer InteractionArtificial Intelligence
AI summaryⓘ
The authors describe a study where two computer science teachers and a student talked together about how the student uses AI tools while learning to program. By sharing their different views and the student's personal experiences, the teachers learned things they couldn't see just by watching in class. This helped them rethink their ideas about how AI affects learning, grading, and teaching methods. The authors suggest that this kind of group dialogue, called trio-ethnography, can help teachers better understand and improve AI-supported education.
generative AIprogramming educationethnographyAI-supported learningpedagogystudent perspectivesinstructional adaptationassessmentcomputing educationreflective practice
Authors
Jennie Ren, Jordan H. McDowell, Kyrie Zhixuan Zhou
Abstract
Generative AI is reshaping programming education, yet educators often infer students' AI-supported learning from classroom observations alone. This experience report presents a trio-ethnography involving two computing educators with different teaching philosophies and one undergraduate computer science student to examine how these interpretations evolve through dialogue. Across three conversations, the educators reflected on students' AI use, discussed changes to programming pedagogy, and revisited their assumptions after engaging with the student's lived experiences. Rather than simply confirming or contradicting the educators' perspectives, the student's narratives revealed learning processes that were largely invisible in the classroom, prompting both educators to reconsider assumptions about AI use, assessment, transparency, and programming instruction. We argue that trio-ethnography offers a valuable reflective approach for helping computing educators move beyond observable student behaviors toward a richer understanding of AI-supported learning and for informing instructional adaptation in the era of generative AI.