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

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 Findings

In a questionnaire of 100 higher-education students, predominantly from engineering-related fields, LLMs were most valued for writing support, conceptual clarification, coding assistance, and brainstorming.

Students simultaneously expressed concerns about inaccuracies, bias, overreliance, academic integrity, and the burden of verification imposed by AI-generated output.

Two dominant metaphors — the LLM as "oracle" and as "tutor" — cultivate expectations of authority, expertise, and personalized learning that exceed what probabilistic text generators can actually deliver.

Students' attachment to the promises of efficiency and personalized support reflects a form of "cruel optimism": the perceived benefits depend on the very skills, vigilance, and expertise that students are still developing.

Student respondents who offered suggestions (N=27) called for assessment redesign — in-person essays, grading shifted from memorization to analysis, oral exams, and presentations with personalized questions — pointing beyond the reproducibility of knowledge.

The chapter argues for a purpose-driven, 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.

Assessment Design

The chapter treats assessment as a key lever, reviewing frameworks such as the AAA (Against, Avoid, Adopt) approach, which keeps lower-order assessment tasks supervised when AI can complete them (e.g., recalling facts, explaining concepts) and relies on contextualization in current affairs, personal experiences, and in-class engagement for higher-order skills. These recommendations align with the students' own suggestions for closed, supervised, or oral formats, and signal a shift in the instructor's role from policing AI use toward reflectively adapting assessment for AI's possible (mis)use.

Implications for AI in Education

The "cruel optimism" argument has direct consequences for pedagogy: the efficiency gains of AI are only realized when learners already possess the Critical Thinking and domain expertise needed to judge AI output — precisely the skills that education is supposed to build. This makes AI Literacy and verification capacity prerequisites rather than optional extras, and cautions against framing AI tools as substitutes for expertise. For engineering education specifically, the balancing act lies in leveraging LLM support for writing, coding, and brainstorming while designing assessments and curricula that keep the burden of verification and the development of judgment inside the learning process, guarding against Over Reliance.

Connected Concepts

AI Literacy

Administrator

Teacher AI Competency

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Citation

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..