2026 ASEE Annual Conference & Exposition

Full Paper - The Hidden Labor Market Titles of AI Engineering Education

Presented at DSAI-Session 14: Research Methods, Collaboration Networks, and Learning Analytics

Artificial intelligence (AI) is reshaping how learning happens in engineering, yet many roles that carry out core pedagogical work in AI are not labeled as “teaching.” Across universities, educational technology (edtech) companies, startups, and large technology firms, labor-market titles such as developer educator, AI educator, learning experience designer, and AI product researcher describe roles that encompass education. These roles include designing learning materials, facilitating training, documenting practices, and evaluating learning outcomes.

This study examines how AI engineering education-related job titles are described and operationalized, the roles and responsibilities associated with these titles, and their implications for preparing engineering education graduate students and instructors. A stratified corpus of public job postings from higher education, edtech, and industry teams that support engineering or STEM learning was analyzed using a directed content analysis approach.

The analysis focused on identifying patterns in: (a) pedagogical actions (e.g., design, facilitate, assess, document); (b) required instructional artifacts (e.g., curricula, labs, tutorials, assessment plans); (c) intended learner audiences; (d) delivery contexts and modalities; (e) assessment and evaluation practices; and (f) references to learning science frameworks. Content patterns were examined to identify areas of convergence and divergence in competency expectations using a combination of large language model (LLM)-assisted evaluation and human-led analysis.

Findings highlight how pedagogical work is distributed across sectors and embedded within a range of AI-related roles, often without being explicitly labeled as teaching. These results provide insight into the competencies, artifacts, and practices associated with AI-related educational work and offer guidance for aligning engineering education curricula with emerging workforce expectations.

Authors

Chibuzor Okocha http://orcid.org/0009-0008-5411-160X University of Florida [biography]

Chibuzor Okocha is a Ph.D. student in Computer Science at the University of Florida. His research focuses on Artificial Intelligence in audio and speech processing, with a specific interest in leveraging large audio language models (LALMs) for reasoning tasks and audio question answering. He is particularly interested in the application of these multimodal technologies to enhance engineering education and accessibility.
Atayliya Natasha Irving http://orcid.org/0009-0006-3644-7455 University of Florida [biography]

Atayliya N. Irving is a PhD Candidate at the University of Florida (UF) in the Engineering Education department. She earned her Bachelor’s degree in Computer Science from Jackson State University. The scope of her interests is to explore the experiences of marginalized students and faculty in higher learning to broaden participation in computing. She specifically looks at the experiences and motivations of women in computing-related PhD programs, with the overall goal of recreating these experiences for the next generation of computer scientists. Upon graduating, Atayliya aspires to pursue a career that broadens interest and representation in the STEMM+ C fields.
Idalis Villanueva Alarcón http://orcid.org/https://0000-0002-8767-2576 University of Florida [biography]

Dr. Idalis Villanueva Alarcón is Chair and Professor in the Department of Engineering Education in the Herbert Wertheim College of Engineering at the University of Florida. A presidential awardee (PECASE), she has led multiple pioneering efforts in engineering education including multimodal methods in engineering education using sensor technologies and biophysiological tools, hidden curriculum, mentoring, active learning, professional identity, among others. She is a renowned national and international leader in engineering education research and practice earning her multiple accolades and honors through professional organizations such as the National Academy of Engineering, IEEE, and ASEE. She integrates her technical experiences as a Chemical Engineering, Biological Engineer, Analytical Cell Biologist, and Engineering Education Researcher to tackle complex engineering education problems across the learner life span.
Dr. Curtis R. Taylor http://orcid.org/0000-0001-9184-5487 University of Florida [biography]

Dr. Curtis Taylor, Ph.D. is an Associate Professor in the Departments of Engineering Education and Mechanical and Aerospace Engineering at the University of Florida.
Christan Grant Ph.D. University of Florida [biography]

Dr. Christan Grant is the Arnold and Lisa Goldberg Rising Star Associate Professor in the Department of Computer & Information Science & Engineering at the University of Florida, where he directs the UF Data Studio research group. His work focuses on end-to-end data and AI systems—how data are collected, labeled, modeled, and visualized—in domains ranging from civic and health data to intelligent infrastructure, with support from the National Science Foundation, federal agencies, and industry partners. At UF he teaches data engineering and introductory data science and has previously developed courses in databases, operating systems, and text analytics that foreground real-world datasets and collaborations reflecting contemporary AI and data science careers. He earned both his Ph.D. in Computer Science and B.S. in Computer Engineering from the University of Florida and has led community-building efforts such as the Broadening Participation in Data Mining workshop and a human-centered data science seminar series. In this paper, his interest lies in how AI-related roles across higher education, edtech, and industry encode teaching work in their titles and descriptions, and how that "hidden" labor market can inform more transparent, career-relevant preparation for engineering students and instructors.

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

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