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

2026-07-24

AI Summary

In the context of large language model–assisted programming education, instructors often struggle to discern students’ authentic learning processes and experiences through classroom observation alone. This study proposes and implements a reflective research paradigm termed “triadic ethnography,” organizing three rounds of structured dialogue among two computing educators holding divergent pedagogical perspectives and an undergraduate student. Through collaborative reflection, experiential sharing, and qualitative analysis, the approach unveils otherwise invisible learning mechanisms enabled by AI support. Moving beyond the limitations of traditional unidirectional observation, this work prompts educators to critically re-examine core assumptions regarding AI usage, assessment practices, transparency, and programming pedagogy, thereby offering empirical grounding and practical insights for instructional adaptation in the era of generative artificial intelligence.

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.

Problem

• programming education
• generative AI
• student learning
• classroom observation
• AI-supported learning

Innovation

• trio-ethnography
• generative AI
• programming education
• interpretation evolution
• reflective pedagogy