Computer Science > Human-Computer Interaction
arXiv:2606.16626 (cs)
[Submitted on 15 Jun 2026]
Abstract:Based on a questionnaire of 100 higher-education students, predominantly from engineering-related fields, and a critical review of recent literature, this chapter examines how students use and perceive Large Language Models (LLMs) in engineering education. Students primarily value LLMs for writing support, conceptual clarification, coding assistance, and brainstorming, while simultaneously expressing concerns about inaccuracies, bias, overreliance, academic integrity, and the burden of verification. Through an analysis of two dominant metaphors, namely LLMs as an "oracle" and as a "tutor," the chapter shows how these systems cultivate expectations of authority, expertise, and personalized learning that often exceed their actual capabilities. The chapter further argues that students' attachment to the promises of efficiency and personalized support reflects a form of "cruel optimism," where the perceived benefits of LLMs often depend on the very skills, vigilance, and expertise that students are still developing. Overall, the chapter argues for a purpose-driven and context-sensitive approach to AI integration in engineering education, emphasizing critical AI literacy, reflective assessment design, pedagogical caution, and consideration of broader ethical and environmental impacts.
| Comments: | To appear in The Routledge Handbook of the Philosophy of Engineering, 2nd ed. Edited By Diane P. Michelfelder, Neelke Doorn |
| Subjects: | Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI) |
| ACM classes: | K.3.1 |
| Cite as: | arXiv:2606.16626 [cs.HC] |
| (or arXiv:2606.16626v1 [cs.HC] for this version) | |
| https://doi.org/10.48550/arXiv.2606.16626 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Olya Kudina [view email]
[v1]
Mon, 15 Jun 2026 12:21:42 UTC (418 KB)
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