2025 ASEE Annual Conference & Exposition

Exploring the Impact of Generative AI in Engineering Education: A Scoping Review of Applications and Innovations

Generative AI technologies are leading the way in innovation by enabling the automatic creation of text, images, audio, and video through advanced AI systems, as noted by [1]. These systems excel in generating realistic content, such as TV scripts from text inputs, lifelike images from brief descriptions, and replicating voices with minimal audio samples. Generative AI has found applications across various sectors, including entertainment, marketing, and digital content creation, but its impact is particularly notable in education [2].

In the realm of engineering education, generative AI is transforming learning by offering personalized, adaptive environments. These systems customize study materials, problem sets, and quizzes to meet the individual needs of students, adjusting to their progress and understanding. This tailored approach ensures learners receive content at the appropriate difficulty level, enhancing both comprehension and retention. AI-driven tutoring systems further support this process by providing real-time feedback, allowing students to learn at their own pace while staying engaged. Additionally, generative AI simplifies the creation of educational tools such as quizzes, study guides, and lesson plans, making learning more efficient and engaging.

This paper outlines a research plan that aims to examine the application of generative models within the engineering education landscape through a comprehensive scoping review. The primary research question guiding this work is: What is the extent of generative models' utilization in engineering education? The study will explore the techniques, uses, and impacts of generative technology in this field, with a focus on how it can revolutionize learning and classroom practices. The review will follow Arksey and O'Malley's five-step framework (2005) in conjunction with PRISMA guidelines to ensure a rigorous and systematic approach. This work is a part of a larger, ongoing research project delving into the use of generative AI in engineering education.

Authors

Animesh Paul University of Georgia [biography]

Animesh was born in Tripura, India, and raised in a liberal modern “brown” military upbringing. He prefers the pronouns “He/They” and considers himself a creative, sanguine, and outgoing individual. He graduated with a bachelor’s degree in Technology focusing on Electronics and Electrical Engineering from KIIT University. He is now a part of the Engineering Education Transformation Institute as a Ph.D. student under the advisement of Dr. Racheida Lewis. His research is in Engineering Education, focusing on equity, inclusion in the classroom, and easing student transition to the workforce catering to STEM graduates.
VINCENT OLUWASETO FAKIYESI University of Georgia [biography]

Vincent Oluwaseto Fakiyesi received the B.Tech. degree in chemical engineering from the
Ladoke Akintola University of Technology, Ogbomosho, Oyo State, , Nigeria in 2015, and He is presently a Doctoral Students at Engineering Education Transformative Institute at the University of Georgia College of Engineering.
Md Ulfat Tahsin http://orcid.org/0000-0002-2810-2821 The Ohio State University [biography]

Md Ulfat Tahsin is a Ph.D. student and a Graduate Fellow in the Department of Engineering Education at The Ohio State University. He is from Bangladesh. He has previously worked closely with engineering faculties and students in Bangladesh from where his love and interest for engineering education started to flourish.
Lexy Chiwete Arinze Purdue University at West Lafayette (COE) [biography]

Lexy Arinze is a first-generation PhD student in the School of Engineering Education at Purdue University and a Graduate Research Assistant with the Global Learning Initiatives for the Development of Engineers (GLIDE) research group. Lexy's research interests include early career engineers, Artificial Intelligence, experiential learning, and global experiences. He earned his master's degree in Civil Engineering from Purdue University. Before that, he received an Erasmus scholarship for an exchange program at the University of Jaén, Spain. He completed his undergraduate degree in Civil Engineering at the University of Ibadan, Nigeria.
Dr. Sreyoshi Bhaduri Private Entity [biography]

Dr. Sreyoshi Bhaduri is an AI scientist. Currently, she spearheads innovative research in applying generative AI to solve complex supply chain logistics and operations challenges. Her expertise spans applied statistics and natural language processing, with a PhD from Virginia Tech and specialized training in Responsible AI from MILA. Sreyoshi has been recognized as a Graduate Academy for Teaching Excellence (VTGrATE) Fellow, a Global Perspectives Program (GPP) Fellow, and was inducted in the Bouchet Honor Society in 2017. Sreyoshi is committed to demystifying and democratizing generative AI solutions and bridging the gap between theoretical research and practical applications using AWS technologies.

Note

The full paper will be available to logged in and registered conference attendees once the conference starts on June 22, 2025, and to all visitors after the conference ends on June 25, 2025