2026 ASEE Annual Conference & Exposition

Reimagining Engineering Competencies in the Generative AI Era: A Socio-Technical Analysis of Student Perceptions

Presented at The Impact of AI on Engineering Education Practice

This empirical research full paper study investigates how senior mechanical engineering students perceive Generative AI (GenAI) and which professional competencies they view as most critical for success in an AI-augmented engineering landscape. Using a cross-sectional survey of 80 senior mechanical engineering students at a large R1 university, we examine: (1) students’ perceptions, usage patterns, and concerns regarding GenAI; (2) the perceived importance hierarchy among 36 engineering Knowledge, Skills, and Abilities (KSAs); and (3) whether internship experience is associated with differences in KSA valuation. Grounded in the JO-HAI (Joint Optimization of Human-skill and Artificial Intelligence) Framework, a tripartite synthesis integrating Socio-Technical Systems (STS) theory, the hidden curriculum concept, and a proposed Dynamic Competency Interaction Model (DCIM), results show that students prioritize human-centric competencies (communication, M = 4.79; critical thinking, M = 4.77; teamwork, M = 4.70; adaptability, M = 4.56) over specialized technical knowledge. A pronounced awareness–adoption gap characterizes GenAI engagement (89.5% familiar; 4.7% daily use), with accuracy (66.3%), job displacement (65.1%), and ethics (60.5%) as leading concerns. Exploratory analyses at the uncorrected p < 0.05 level suggest internship experience may modestly shift competency valuations (interns rating flexibility higher, p = 0.036; non-interns rating applied technical knowledge higher, p = 0.027); however, these effects do not survive Bonferroni correction and should be interpreted as hypothesis-generating. The JO-HAI Framework offers an integrative lens for understanding competency development in AI-augmented professions, with implications for curriculum design, AI literacy, responsible AI use, and experiential learning.

Authors

Mr. Taiwo Raphael Feyijimi http://orcid.org/0009-0009-5752-4175 University of Georgia [biography]

Taiwo Feyijimi is a skilled AI researcher and doctoral candidate in the School of Electrical and Computer Engineering and the Engineering Education Transformations Institute (EETI) at the University of Georgia, Athens. With over 15 years of experience across the technology and education sectors, he has served in various leadership roles – including Founder/Co-Founder, CEO, Technology Strategist, Technical Lead, and EdTech professional – in both public and private organizations. This diverse background equips him with a unique perspective that bridges industry practice and academic research. Mr. Feyijimi’s expertise spans strategic system design and implementation, exploratory data analysis (EDA), machine learning (ML), natural language processing (NLP), and prompt engineering techniques (PETs) with large language models (LLMs). His research focuses on enhancing undergraduate engineering education by aligning student competencies with workforce demands. He employs computational intelligence to extract actionable insights from data and applies Q methodology to explore student perspectives on learning and competency development. A core element of his work is the integration of AI tools to cultivate essential regulatory learning skills such as metacognition, self-regulation, and collaborative problem-solving in fostering and evaluating competencies development in students. He is currently pioneering and developing a novel framework that combines Q methodology with artificial intelligence (AI), grounded in learning theories and Vygotsky’s sociocultural theory of learning, to improve regulatory learning processes in undergraduate students in engineering and STEM education. Additionally, Mr. Feyijimi is also pioneering and developing another framework that fuses the Southern African Bantu's Ubuntu philosophical perspective with the Western African Yoruba's Eko (Education) and Omoluabi (person of impeccable character and virtue) philosophical and epistemological perspectives for educating and cultivating AI workforce-ready students and educators in Africa. As a testament to his innovative contributions, Mr. Feyijimi has developed two pioneering frameworks for integrating AI into qualitative research – including an AI-assisted thematic analysis tool – both of which are currently under review for U.S. copyright protection.
Dr. Wayne M. Johnson http://orcid.org/0000-0001-9897-2366 University of Georgia [biography]

Wayne M. Johnson is a Senior Lecturer in the School of Environmental, Civil, Agricultural and Mechanical Engineering at the University of Georgia (UGA) in Athens, GA. Prior to joining UGA in 2022, he was a Professor of Mechanical Engineering at Georgia Southern University-Armstrong Campus, Savannah GA. He received his Ph.D. and M.S. in Mechanical Engineering from Georgia Institute of Technology and his B.S. in Mechanical Engineering (Cum Laude) from Louisiana State University. He has published 16 papers in peer-reviewed journals, 28 papers in peer-reviewed conference proceedings, and given 12 technical presentations on various topics including: additive manufacturing, mechatronics, biomechanics, and engineering education. He currently teaches the Engineered Systems In Society, Mechanical Engineering Professional Practice, and Capstone Design I and II courses.
Dr. Julie P Martin The University of Georgia [biography]

Julie P. Martin is the Director of the Engineering Education Transformations Institute at University of Georgia. Julie is a Fellow of ASEE and the editor-in-chief of Journal of Women and Minorities in Science and Engineering, where her vision is to change the culture of academic publishing to one characterized by constructive feedback. Julie is an ASEE Fellow and a member of the Hall of Fame.

Note

The full paper will be available to logged in and registered conference attendees once the conference starts on June 21, 2026, and to all visitors after the conference ends on June 24, 2026

« View session