Exploring the Potential of Generative AI in Shaping Engineering Education: Opportunities and Challenges
Abstract
Abstract—Engineering education equips students with the necessary knowledge and skills to thrive in rapidly evolving field of engineering and computer science. However, updating the curriculum to be current with the latest advancements can be challenging for educators. The curriculum and activities (both cocurricular and extra-curricular) activities must be designed such way as to educate a global engineer with multidimensional attributes. The faculty and administrators must be willing to adapt to the fast changes in technology and learning habits of our students. In this day and age of ChatGPT, Bard, Perplexity and other generative AI tools, educators have to adapt and focus on creative ways of engaging students towards lifelong learning with strong work ethics and professional skills. This paper explores the potential of generative artificial intelligence (AI) in revolutionizing engineering education by designing and offering up-to-date content. It examines specific examples in various engineering domains and discusses the benefits of generative AI in accreditation efforts and faculty time allocation. Keywords—engineering education; generative AI; prompt engineering.
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Fewer studies examine engineering students through a theory-based information-seeking lens that brings together learning, trust, judgment, and verification during problem solving.
“As a result, fewer studies examine engineering students through a theory-based information-seeking lens that brings together learning, trust, judgment, and verification during problem solving.”
From Engineering student perceptions of generative AI use in learning trust judgment and information seeking (2026) · cites this paper as [27]
The sections on student learning and AI ethics discuss problem-solving, critical thinking, and checking generated content for accuracy. Our copy does not address whether fewer studies use a theory-based information-seeking lens that brings together learning, trust, judgment, and verification; searches for that research gap found no supporting passage. (LLM, conf=0.74)
Experimental — accuracy not yet measuredPrior work emphasizes opportunities in curriculum development, simulations, and faculty support alongside challenges related to bias, accuracy, and faculty training.
“while also emphasizing opportunities in curriculum development, simulations, and faculty support alongside challenges related to bias, accuracy, and faculty training [27].”
From Engineering student perceptions of generative AI use in learning trust judgment and information seeking (2026) · cites this paper as [27]
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Research works printed but not linked: 3
- Johri, A., Katz, A. S., Qadir, J., & Hingle, A. (2023, June 11). Generative artificial intelligence and engineering education. Journal of Engineering Education, 112(3), 572-577. https://doi.org/10.1002/jee.20537 · DOI · Paper page
- Ouatu, BI., Gifu, D. (2021). Chatbot, the Future of Learning?. In: Mealha, Ó., Rehm, M., Rebedea, T. (eds) Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education. Smart Innovation, Systems and Technologies, vol 197. Springer, Singapore. https://doi.org/10.1007/978-981-15-7383- 5_23 · DOI
- Yu, H., & Guo, Y. (2023, June 1). Generative artificial intelligence empowers educational reform: current status, issues, and prospects. Frontiers in Education, 8. https://doi.org/10.3389/feduc.2023.1183162 · DOI · Paper page
Cited by
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Requirements Are All You Need: The Final Frontier for End-User Software Engineering
Robinson, D., Cabrera, C., Gordon, A. D., Lawrence, N. D., & Mennen, L. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3708524 · Cited by 12 · see citing works
Hewes, M., & Wada, K. (2026) · 2026 Intermountain Engineering, Technology and Computing (IETC) · doi:10.1109/ietc69527.2026.11568618
Mohammed, Y., Qandil, H., & Lund, B. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-02086-2
- Authors
- College of Engineering, T. T. U., Yelamarthi, K., Dandu, R., Mechanical Engineering Technology, K. S. U., Rao, M., Department of Computer Science, T. W. U., Yanambaka, V. P., College of Engineering, T. T. U., Mahajan, S., & College of Engineering, T. T. U.
- Venue
- Journal of Engineering Education Transformations
- Publisher
- Rajarambapu Institute of Technology
- Published
- 2024-01-01
- Volume
- 37
- Issue
- IS2
- Pages
- 439-445
- ISSN
- 2349-2473
- 2394-1707
- Cited by
- 31
- References
- 0
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