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Evidence record 960 · automatically gathered

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

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 verificat

Record details

Published: 15 June 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Children & education
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Bias & fairness · Shared topic: Children & education

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Shared topic: Bias & fairness · Shared topic: Children & education

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Shared topic: Bias & fairness · Shared topic: Children & education

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Shared topic: Bias & fairness · Shared topic: Children & education

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arXiv · 2 June 2026

Shared topic: Bias & fairness · Shared topic: Children & education

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Shared topic: Bias & fairness · Shared topic: Children & education

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How to cite this record

ethics.ai (15 June 2026), “Using AI in engineering education: a balancing act, driven by clear purpose,” evidence record 960, https://ethics.ai/record/960 (originally published by arXiv).

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