Using AI in engineering education: a balancing act, driven by clear purpose
2026-06-15 • Human-Computer Interaction
Human-Computer InteractionArtificial Intelligence
AI summaryⓘ
The authors surveyed 100 mostly engineering students to understand how they use and think about Large Language Models (LLMs) in their studies. Students find LLMs helpful for writing, coding, and brainstorming but worry about errors, bias, and relying too much on them. The authors explain that students often see LLMs like all-knowing or personal tutors, which can lead to unrealistic expectations. They suggest teaching AI use carefully, focusing on critical thinking, ethics, and realistic understanding of AI’s strengths and limits in education.
Large Language ModelsEngineering EducationAI LiteracyAcademic IntegrityBias in AIPedagogyVerificationEthical AIAI IntegrationCritical Thinking
Authors
Olya Kudina
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.