Trio-Ethnography Reveals Invisible AI Learning in Programming Education
An investigation known as trio-ethnography, which included two computing educators and a single undergraduate student majoring in computer science, explored the shifting perceptions of AI-enhanced learning through conversation. Initially, the educators based their conclusions about the student’s use of AI on what they observed in class. However, the student’s accounts uncovered learning experiences that were mostly unnoticed during lessons. This led the educators to rethink their beliefs regarding AI utilization, evaluation, transparency, and teaching programming. The research suggests that trio-ethnography serves as a significant reflective method for comprehending education supported by AI.
Key facts
- arXiv:2607.22463v1
- Trio-ethnography involved two computing educators and one undergraduate computer science student
- Educators had different teaching philosophies
- Three conversations were held
- Student narratives revealed invisible learning processes
- Educators reconsidered assumptions about AI use, assessment, transparency, and programming instruction
- Study argues trio-ethnography is valuable for reflective practice
- Focus on programming education
Entities
Institutions
- arXiv