Research Article
The AI Disruption in Engineering Education: Changing Student Norms Through CHAT
Synthesis: Leino Lindell & Stöhr (2026) use Cultural-Historical Activity Theory (CHAT) to analyze how generative AI disrupts and transforms established norms and practices in engineering education from a student perspective. Thematic analysis of interviews with 25 students at a technical university in Northern Europe identifies four themes of challenge/transformation: (1) the self-directiveness of students, (2) the objectives of learning, (3) the role of the teacher, and (4) ethical aspects. Students are developing new implicit rules for using GenAI to enhance skills and understanding, driven by contradictions between traditional academic tools and new expectations for self-directed, AI-mediated learning.
Key Findings
- Four themes of GenAI-driven norm change. The analysis surfaced challenges or transformations in: students' self-directiveness, the objectives of learning, the role of the teacher, and ethical aspects of academic work.
- New implicit rules for GenAI use. Students are developing informal, emergent norms (implicit rules) for when and how to use GenAI to support their skills and understanding — norms shaped by the CHAT interplay of individual and cultural-historical context.
- Contradictions as the driver. Norm changes are driven by contradictions between traditional academic tools/practices and new expectations for self-directed, AI-mediated learning. CHAT conceptualizes these as systematic tensions that prompt individuals to question and deviate from established norms.
- A relational view of norms. Norms are treated as historically and culturally embedded implicit rules of the activity system, not individual preferences — reframing student GenAI use as a systemic, norm-shifting phenomenon.
Implications for AI in Education
The study applies CHAT to show that student GenAI use is not merely a behavioral choice but a transformation of the norms and rules of the academic activity system. For practice, it implies instructors must recognize and explicitly negotiate new implicit norms around self-direction, learning objectives, teacher role, and ethics rather than assuming traditional norms still hold. It connects to the knowledge base's Engineering Education, Student Experience, Teacher Role, and Ethics concepts, and to the wider GenAI disruption literature.
Connected Concepts
Connected Articles
- Jiang GenAI Activity Theory Disciplines 2026 — Activity theory perspective on disciplinary differences in GenAI use
Citation
Leino Lindell, T., & Stöhr, C. (2026). The AI disruption in engineering education: An analysis of changing student norms through cultural historical activity theory. Journal of Computing in Higher Education.