Engineering students develop implicit rules for using GenAI as a self-directed, efficient learning tool when traditional educational tools fall short¶

Claim · All claims
Evidence · 1 study (2 entries) · qualitative r2 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Engineering students increasingly integrate GenAI for personal growth due to its ease of use and efficiency, developing implicit rules for self-directed and efficient GenAI use. → Tiina Leino Lindell 2026 q2 i? The transformation is driven by a contradiction between students' object of enhancing understanding and skills through self-direction and efficiency, and the limitations of traditional educational tools. → Tiina Leino Lindell 2026 (2)

Evidence¶

Tiina Leino Lindell 2026¶

Tiina Leino Lindell, Christian Stöhr. (2026). The AI disruption in engineering education: an analysis of changing student norms through cultural historical activity theory. Journal of Computing in Higher Education. https://doi.org/10.1007/s12528-025-09488-8

q2 · i? · qualitative · r2

Reflexive thematic analysis of 11 one-to-one and 6 group interviews with 25 engineering students from 13 subject areas at a technical university in Northern Europe. Students reported using GenAI for writing support, faster data analysis, and idea generation, and were found to be "increasingly integrating GenAI for their personal growth".

"In short, the findings from this group of engineering students show that they are increasingly integrating GenAI for their personal growth due to its ease of use and efficiency compared to traditional educational tools."

Tiina Leino Lindell 2026 (2)¶

Tiina Leino Lindell, Christian Stöhr. (2026). The AI disruption in engineering education: an analysis of changing student norms through cultural historical activity theory. Journal of Computing in Higher Education. https://doi.org/10.1007/s12528-025-09488-8

q2 · i? · qualitative · r2

CHAT-based deductive analysis of the interview themes, illustrated in Fig. 2, locating the contradiction between the new object of self-directed growth and the "limitations of traditional educational tools". One student described ChatGPT processing data faster than Excel and other tools.

"This transformation appears to be driven by contradictions between their object of enhancing understanding and skills through self-direction and efficiency, and the limitations of traditional educational tools in fulfilling these needs (See Fig. 2)."

Discussion¶

Related Claims¶

• GenAI's immediate answers reduce engineering students' teacher-student interactions for generalizable tasks, while teachers remain preferred for complex problem-solving — related
• Engineering students come to view mastering GenAI as crucial as other engineering skills, driven by labour-market expectations, alongside widespread uncertainty about over-reliance — related
• In interviews with 25 engineering students at one Swedish university, students described GenAI as acceptable when it stimulates reflection and directly copying its outputs as cheating, and one used its fallibility as a prompt to double-check — a narrower finding that bears on this claim
• Conflicting student and teacher rules for GenAI produce adaptive counter-norms and student calls for teachers to refine assignments rather than impose unenforceable restrictions — related
• Qualitative themes: efficiency and peer influence drive AI use, while confidence and rule ambiguity shape students' perceptions of dependence — related
• Students in an academic writing course developed a more nuanced understanding of AI's capabilities and limitations, engaging with it as a support tool rather than a substitute for original work — related
• Frequent generative AI use enhances perceived confidence and efficiency but increases technological dependence among university students — related
• Student reflections revealed tensions between efficiency and academic integrity, and uncertainty about institutional expectations of GenAI use — related
• Reaching the qualitative SOLO phase in GenAI interaction required repeated meta-level learner interventions, demanding the very self-regulatory skills the tool was expected to support — related