Date
Publisher
arXiv
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.
Study at a glance
- Question: How do computing educators' interpretations of students' AI-supported learning evolve through dialogue with a student?
- Setting and data: Two university computing educators and one undergraduate computer science student in the United States.
- Method: Trio-ethnography using collaborative dialogue and a semi-structured interview.
Why it matters
A growing gap exists between observable student code and how students actually learn with generative AI, risking misaligned instruction.
Key findings
- Educators' interpretations evolved from viewing undisclosed AI use as stigma to recognizing instructional ambiguity and the necessity of explicit AI guidance.
- Conversations shifted educators' views of generative AI from a shortcut that replaces thinking to a tutor and learning partner that scaffolds conceptual understanding.
- Student narratives revealed invisible post-generation learning processes, showing students may analyze logic, take notes, and practice independently after receiving AI code.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
What is the application?
Who age?
Study design
