Engineering students most often use LLMs for academic writing (27%), conceptual clarification (20%), programming help (17%) and brainstorming (13%)¶

Claim · All claims
Evidence · 1 study · associational r1 · q2
Currency · newest study 2025 · not yet checked against its sources · not reviewed by a person · recheck by 2030: baselines and conditions change

Subclaims¶

q2 i? In a 2023-24 questionnaire of 100 higher-education students dominated by engineering fields, the most prevalent LLM use pattern (27%) was drafting, improving and refining text, followed by learning and conceptual clarification (20%), programming support (17%) and brainstorming (13%). → Kudina 2025

Evidence¶

Kudina 2025¶

Kudina, O. (2025). Using AI in engineering education: a balancing act, driven by clear purpose. Draft version, to appear in The Routledge Handbook of the Philosophy of Engineering, 2nd ed. https://arxiv.org/abs/2606.16626 doi:10.48550/arxiv.2606.16626

q2 · i? · associational · r1

Online questionnaire conducted 2023-24 with 100 respondents, 52% from AI, Robotics, Engineering and Design fields; 81 reported using LLMs. The author reports the top use themes with percentages, including drafting text at 27% and, in Table 2, conceptual clarification at 20%, programming at 17%, brainstorming at 13%.

"The most prevalent use pattern (27%) concerns the use of LLMs for drafting text, its improvement, and language refinement, e.g., “making my English sound more professional”"

Discussion¶

Related Claims¶

• Respondents anticipated using LLMs most often (23%), followed by NLP (17%), predictive AI (14%), chatbots (13%), and ASR (10%) — related
• Students' top two concerns about educational LLM use are output quality (fabricated or misleading content, 53 mentions) and the verification burden (34 mentions) — related
• Students' LLM use in CS2 fell into three themes: understanding, assisting code writing, and writing code, with only a few using LLMs to write code — related
• Students use the integrated AI assistant differently across writing stages: clarification early, verification late — related
• Design students' GenAI use concentrates on research, writing and brainstorming tasks, with low use for image, media and code generation — related
• Instant access to information and 24/7 availability are the most recognised benefits of LLMs for learning — related
• Language learning was the most common subject domain for AIEd in higher education, at 17% — related
• The majority of AI-assisted participants self-report using the AI primarily to get answers directly, a pattern validated against chatlogs — related