This paper examines how engineering students use and perceive Large Language Models (LLMs) in their education, uncovering both their valued applications and significant concerns.

In plain terms

This paper investigates how engineering students use and perceive Large Language Models (LLMs), which are advanced AI programs that can understand and generate human-like text, in their higher education studies. The author conducted a survey of 100 university students, mostly in engineering fields, and also reviewed existing literature to understand current trends and challenges. Students reported valuing LLMs mainly for help with writing, clarifying difficult concepts, assisting with coding tasks, and brainstorming new ideas. However, they also expressed significant concerns, including the risk of incorrect information (inaccuracies), potential biases, becoming too dependent on the AI (overreliance), issues related to academic honesty (academic integrity), and the extra effort needed to check the LLM's outputs (burden of verification). The study highlights how students often view LLMs as an all-knowing "oracle" or a perfect "tutor," leading to expectations of expertise and personalized support that these tools frequently cannot fulfill. Ultimately, the paper advocates for a careful, purpose-driven integration of AI, stressing the importance of teaching students how to critically evaluate AI (critical AI literacy) and designing assessments that promote thoughtful use rather than just efficiency.