Engineering education researcher
Daniel I. Adeniranye, PhD
Known to students and colleagues as ProfDan. Faculty, Department of Engineering Education, The Ohio State University.
I study the decisions engineering students make at points of transition: whether to use, limit, or decline AI, how to move between institutions, and how to step into professional practice.
Research program
Three connected strands
AI in engineering and STEM education
How students judge when to use, limit, or decline generative AI, and how AI competency demands are defined across majors, co-ops, and the workforce.
Studies and papersTransfer and transition pathways
The advising systems, policies, and institutional conditions that shape how students move into, through, and out of engineering programs.
Studies and papersEngineering formation and experiential learning
First-year engineering, co-op and internship learning, and how students are prepared for professional practice.
Studies and papersNow
October 2026
- Starting round-1 usability testing of Usewright with instructors, graduate supervisors, and students at other institutions.
- The Use, Restraint, and Refusal study of first-year engineering students and generative AI is in pre-review with Ohio State’s Office of Responsible Research Practices.
- Submitted a decision-science pilot proposal, When to Trust, When to Check, with a colleague in Computer Science and Engineering, and four abstracts to the 2027 ASEE Annual Conference.
- Served on a National Science Foundation review panel for undergraduate STEM education.
- Teaching three sections of first-year engineering with a team of six undergraduate and three graduate teaching assistants.
About
Biography
Daniel I. Adeniranye, PhD, known to students and colleagues as ProfDan, is a faculty member in the Department of Engineering Education at The Ohio State University. He is an engineering and computing education researcher who studies how students use, limit, and decline generative AI, how AI competency demands are defined across engineering and the technical workforce, and how students move through engineering pathways. His recent work on AI in higher education appears in Computers and Education Open and the proceedings of the American Society for Engineering Education (ASEE).
Trained first as a mechanical engineer, he holds degrees from institutions in the United States, the United Kingdom, France, and Nigeria, and has taught and designed learning programs in the United States, Nigeria, the United Arab Emirates, and the United Kingdom. He earned his PhD in Engineering and Computing Education at Florida International University, where he was named Outstanding PhD Graduate of the College of Engineering and Computing. He has contributed to federally funded engineering education projects supported by the National Science Foundation and the U.S. Department of Defense, totaling more than $24 million. He is a research fellow funded by the National Science Foundation, a member of the Edward A. Bouchet Graduate Honor Society, a past Chair of the ASEE Student Division, and has served on a National Science Foundation review panel for undergraduate STEM education. He is the founder of Usewright.
Selected work
Recent publications
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Adeniranye, D. I., Lunn, S. J., Mosobalaje, O., Eze, P., Berhane, B., & Adeniyi, D. (2026). Institutional structures, digital inequality, and AI integration in higher education. Computers and Education Open, 100400. https://doi.org/10.1016/j.caeo.2026.100400
In brief: Examines how institutional structures and unequal access to digital resources shape the way universities integrate AI.
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Adeniranye, D. I., Berhane, B. T., & Keah, A. (in press). AI technician workforce demand and community college curriculum development: A systematic secondary analysis of Maryland’s Eastern Shore. Journal of Career and Technical Education.
In brief: Sets regional employer demand for AI technicians against what community college curricula in Maryland’s Eastern Shore currently prepare students to do.
In press -
Adeniranye, D., Brijmohan, Y., Wasay, A., & Jaco, M. (2026, June). Understanding undergraduate students’ use of generative AI for learning in STEM: An integrative literature review. Proceedings of the 2026 ASEE Annual Conference & Exposition, Charlotte, NC.
In brief: Brings together research on how undergraduates use generative AI to learn in STEM courses.