Olya Kudina (2026) — The Routledge Handbook of the Philosophy of Engineering, 2nd ed. 📄 Full text (arXiv)
Based on a questionnaire of 100 higher-education engineering students and a critical literature review, examines how students use and perceive LLMs. Students value LLMs for writing support, conceptual clarification, coding assistance, and brainstorming, but express concerns about inaccuracies, bias, overreliance, and academic integrity. Analyzes two dominant metaphors — LLM as 'oracle' and 'tutor' — showing they cultivate expectations exceeding actual capabilities. Argues students' attachment to LLM efficiency reflects 'cruel optimism' — benefits depend on skills still being developed.
Key Contributions
- Students' attachment to LLMs reflects 'cruel optimism' where promised benefits depend on the critical thinking skills students are still developing.
Related Pages
- ai-k12-evidence-base — Empirical evidence on AI in K-12 education outcomes
- intelligent-tutoring — Automated tutoring systems and their evaluation
- student-experience — Student perspectives on AI in education
- learning-analytics — Data-driven approaches to understanding learning
- llm-feedback-programming-classroom — LLM feedback in classroom settings
Citation
APA: Olya Kudina (2026). Using AI in engineering education: a balancing act, driven by clear purpose. arXiv:2606.16626. The Routledge Handbook of the Philosophy of Engineering, 2nd ed..