REVIEW article
Front. Educ.
Sec. Higher Education
Volume 10 - 2025 | doi: 10.3389/feduc.2025.1649650
This article is part of the Research TopicEthical Considerations of Large Language Models: Challenges and Best PracticesView all 5 articles
Review of Current and Potential Uses of Large Language Models in Engineering
Provisionally accepted
- University of Oklahoma, Norman, United States
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Background: Large Language Models (LLMs) have emerged as transformative tools in engineering, offering capabilities that streamline complex processes and support decision-making across diverse disciplines. Despite notable advancements in applications such as robotics task planning, autonomous driving, program repair, and technical documentation, challenges persist concerning ethical considerations, transparency, and accountability in safety-critical systems. Purpose: This study aims to conduct a systematic literature review (SLR) on the applications, challenges, and ethical implications of LLMs in engineering. The objective is to synthesize existing knowledge and identify research gaps to guide future investigations. Method: A comprehensive review of peer-reviewed publications from 2014 to 2024 was conducted, resulting in the selection of 23 relevant articles. These articles were classified into five thematic categories: automation of complex engineering tasks, knowledge generation and discovery, enhancing engineering education, ethical considerations and challenges, and integration with real-world engineering practices. Results: The review highlighted (i) increasing interest in LLM applications across multiple engineering domains, (ii) a growing emphasis on ethical and regulatory concerns related to LLM adoption, (iii) significant potential for enhancing productivity and fostering innovation, and (iv) a critical need for interdisciplinary collaboration to address reliability and scalability challenges. Conclusions: LLMs hold considerable promises for advancing engineering practices by automating tasks, facilitating knowledge discovery, and supporting education. However, ensuring ethical deployment, transparency, and model reliability remains essential. Future research should focus on developing frameworks for responsible AI adoption and fostering interdisciplinary efforts to overcome existing limitations.
Keywords: Engineering applications, Ethical AI Deployment, Large language models, Systematic Literature Review, Engineering
Received: 18 Jun 2025; Accepted: 15 Sep 2025.
Copyright: © 2025 Nguyen and Kittur. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Javeed Kittur, jkittur@ou.edu
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.