Using AI in engineering education: a balancing act, driven by clear purpose

2026-06-15

AI Summary

This study addresses a critical tension in engineering education: students’ simultaneous expectation of efficiency from large language models (LLMs) and their frequent misjudgment of LLM capabilities, which can lead to accuracy errors, bias propagation, academic integrity risks, and overreliance. Through a mixed-methods approach combining surveys, qualitative metaphor analysis, and critical literature review, the research reveals that students primarily employ LLMs for writing assistance, conceptual clarification, programming support, and brainstorming. It identifies a pervasive tendency to overattribute authority to LLMs and underappreciate the burden of verification. Introducing the metaphors of “oracle” and “mentor” alongside the concept of “cruel optimism,” the study elucidates the gap between students’ role expectations and LLMs’ actual capacities. It proposes an integrative framework centered on goal-directed use, contextual sensitivity, critical AI literacy, reflective evaluation, and ethical scaffolding to guide the responsible integration of LLMs in engineering education.

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.

Problem

• Large Language Models
• engineering education
• student perception
• AI literacy
• academic integrity

Innovation

• Large Language Models
• engineering education
• critical AI literacy
• cruel optimism
• purpose-driven integration