arXiv:2606.16626v1 Announce Type: cross 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
The proliferation of LLMs is forcing educators and institutions to understand their impact on learning processes and academic integrity.
This research provides early data on student perceptions and uses of LLMs, which is crucial for developing effective educational policies and tools.
The explicit acknowledgment of LLM use in education, moving from a novel tool to an integrated, albeit complex, part of the learning landscape.
- · AI developers focused on educational applications
- · Students leveraging AI for learning support
- · Educational institutions adapting AI into curriculum
- · Traditional educational methods without AI integration
- · Cheating detection software providers (as the nature of 'cheating' evolves)
- · Educators resistant to AI integration
Universities will accelerate the development of AI usage policies and guidelines for students and faculty.
New pedagogical approaches will emerge that integrate LLMs as tools for learning rather than solely as sources of answers.
The definition of 'original work' and academic integrity will undergo significant re-evaluation and potential redefinition in higher education.
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Read at arXiv cs.AI