2025 ASEE Annual Conference & Exposition

Introduction and Motivation:
This complete research study is a literature review conducted on the role of Generative AI (GenAI) tools in undergraduate engineering education. The rapid development of large language models (LLMs), like ChatGPT, has led to widespread use by students, educators, and researchers, creating both opportunities and challenges for learning environments [1], [2]. While previous tools like spellcheckers and grammar checkers have been commonly used in the classroom, the accessibility to and capabilities of GenAI models have prompted a re-evaluation of how these tools could be integrated into the learning environment. The increasing application of GenAI in engineering education requires updated policies and pedagogical approaches due to challenges related to academic integrity, ethical considerations, and curriculum design.
Although there is a growing body of research on the role of GenAI in engineering education, it has yet to offer a cohesive view on the role of GenAI tools in engineering education and its implications. Often studies on this topic focus on isolated topics, such as, student perceptions, ethical considerations or pedagogical implications. This has created a fragmented understanding of how these tools are reshaping engineering education. There is a gap the literature that explores the role of GenAI in engineering education as a combination of these topics. This literature review aims to map-out emerging themes from current literature, to build a framework that presents a cohesive overview of the key issues, opportunities, and challenges related to GenAI. Through developing this framework, this study can highlight areas requiring further investigation and research.
Methods:
This study aims to develop a literature review across academic databases using search terms such as “GenAI,” “ChatGPT,” and “Generative AI in engineering education” to source relevant papers pertinent to application in undergraduate engineering education. Papers outside the scope of this review are excluded, with remaining studies analyzed to identify common themes. This analysis synthesizes recurring ideas and provides a thematic overview of the role of GenAI tools in engineering education. The criteria for inclusion/exclusion will also account for papers specifically focused on undergraduate contexts, to ensure the findings are relevant to the intended audience. The themes identified in this study will lead to the development of a framework for further integration of GenAI tools in engineering education. This would serve as a tool for engineering educators, institutions and policymakers to understand the implications of GenAI tools and leverage them in the classroom.
Results:
Initial findings suggest themes around the integration of such tools in engineering education, such as: ethical considerations, pedagogical shifts, and the potentials of GenAI to enhance student learning. Ethical concerns—such as algorithmic bias, privacy, and the impact of AI on academic integrity—are highlighted by some researchers [1], [3], [4], [5]. Literature also discusses pedagogical shifts, suggesting that GenAI could fundamentally reshape curriculum design and classroom dynamics, prompting educators to reconsider how these tools can enhance learning experiences [2]. Additionally, the need for continuous upskilling of both students and educators is emphasized, reflecting the rapidly evolving nature of AI technologies [2].
Student perspectives appear to lean toward positive perceptions, viewing these tools as supportive in their learning journeys, especially in enhancing collaborative and independent study efforts [6], [7]. Educators, however, remain divided: some express concern over potential misuse, while others highlight the potential for AI tools to foster more effective teamwork and collaborative problem-solving in engineering projects [8], [9], [10]. Ongoing analyses is expected to reveal additional themes, such as shifts in assessment strategies and changes in the traditional roles of educators.
Conclusion & Significance:
This study provides a snapshot of the current landscape of GenAI use in undergraduate engineering education. Additionally, it supports the development of a framework for understanding the applications of GenAI tools in undergraduate engineering education and its implications. This research identifies approaches that may be effective and transferable, and can inform future research, policy development, and instructional practices around the integration of GenAI tools in engineering education.