Daniel I. AdeniranyePhD

Research

A program built around transitions

Each strand asks the same question in a different setting: what shapes the decisions students make as they move into, across, and out of engineering. Select a strand or a connecting project on the map to see the studies, papers, and funding behind it.

Research map Three research strands sit at the corners of a triangle: AI in engineering and STEM education, transfer and transition pathways, and engineering formation and experiential learning. Projects that connect two strands sit on the lines between them. The central question is how students decide at points of transition. CENTRAL QUESTION How students decide at points of transition S-STEM fellowship: AI and pathway choices AI competencies in co-ops and internships Technicians becoming engineers (RISE) AI in engineering and STEM education Transfer and transition pathways Engineering formation and experiential learning

Funding status reflects the CV dated October 2026.

Strand 1

AI in engineering and STEM education

This strand treats the decision to use, limit, or decline generative AI as a reasoned judgment that can be learned and assessed, rather than as a failure to adopt. It also asks how AI competency demands are defined by employers, faculty, and programs.

Current studies

  • Use, Restraint, and Refusal (PI). Repeated cross-sectional survey of first-year Fundamentals of Engineering students at Ohio State, Autumn 2026 through Spring 2028. Co-investigator: Dr. Kadri A. A. Parris.
  • When to Trust, When to Check (PI). An incentivized decision experiment on whether engineering students pay a small cost to verify AI-generated answers on MATLAB tasks. Co-investigator: Parkash Singh.
  • AI Expectations Across Engineering Disciplines (PI and lead author). Explanatory sequential mixed-methods study of industry, faculty, and student views across mechanical, electrical, and biomedical engineering.
  • Usewright: AI Judgment Framework (creator). A five-step framework and free platform at usewright.org; round-1 usability testing begins October 2026.
  • Founding member, Collaborative Intelligence and AI in Engineering Education, a multi-institutional research collaborative.

Papers

  • Institutional structures, digital inequality, and AI integration in higher education. Computers and Education Open (2026).
  • AI technician workforce demand and community college curriculum development. Journal of Career and Technical Education (in press).
  • Undergraduate students’ use of generative AI for learning in STEM: An integrative literature review. ASEE (2026).
  • AI integration capacity in Nigerian universities’ engineering programs. ASEE (2026).
  • Open-source large language models for African universities. IST-Africa (2026).

Funding

  • Decision Sciences Collaborative Pilot Grant, Ohio State, $2,990 (PI; under review).
  • 2027 Tools Competition, Catalyst level, $50,000 (lead applicant for Usewright; Phase I abstract under review).
  • OAA AI Fluency Innovation Microfunds, Ohio State, $10,850 (lead applicant; target submission October 2026).
  • NSF CyberAICorps Scholarship for Service, Innovation Track (PI; target submission April 2027).

Strand 2

Transfer and transition pathways

This strand looks at the institutional structures, advising systems, and policy conditions that shape how students move into, through, and out of engineering, with particular attention to community college transfer and minority-serving institutions.

Studies

  • Dissertation: the lived experiences of engineering transfer students at a Hispanic-Serving Institution, a transcendental phenomenological study (Florida International University, 2026). Three manuscripts are in revision.
  • Better Together: Optimizing Black Engineering Student Pathways (co-investigator). Focus groups and interviews across 15 ABET-accredited HBCU engineering programs and 15 community colleges.
  • Denver Metro Engineering Consortium (co-investigator) and Inclusive Engineering Consortium (key personnel), studying 2-to-4 engineering transfer pathways.

Funding

  • NSF Award #2145961, $373,632 (co-investigator, 2022–2025).
  • U.S. Department of Defense, DoD STEM Community College Consortium, $10,879,283 (co-investigator, 2023–2025).
  • U.S. Department of Defense, Inclusive Engineering Consortium, $10,997,746 (key personnel, 2022–2025).

Papers

  • Paradoxical navigation: Black engineering transfer students’ lived experiences at a Hispanic-Serving Institution. Journal of Engineering Education (revise and resubmit).
  • The importance of advisors in the engineering transfer process. Studies in Engineering Education (revise and resubmit).
  • Beyond community cultural wealth: Diasporic cultural capital. Studies in Engineering Education (revise and resubmit).
  • Policy and program leaders’ lenses on engineering transfer. ASEE (2025).
  • Tracing Black transfer students’ success in engineering. ASEE (2024).

Strand 3

Engineering formation and experiential learning

This strand follows how students become engineers: their first year, the learning that happens in co-ops and internships, and the preparation that carries them into professional practice.

Studies

  • From shared materials to enactment: a self-study in first-year engineering (ASEE 2027 abstract submitted).
  • CREED²: Culturally Responsive Engineering Experience Design and Development (key personnel). Summer Institute curriculum for in-service high school teachers.

Funding

  • NSF Discovery Research K-12, Award #2201255, $1,837,566 (key personnel, 2022–2023).

Where the strands meet

Connecting projects

  • S-STEM fellowship: AI and pathway choices

    Strands 1 and 2. As a 2026–2027 Research Fellow of the Community College S-STEM Network (NSF DUE #2224623, $15,000), Daniel is designing a mixed-methods study of how AI competency demands shape low-income community college STEM students’ decisions to persist, transfer, or enter the workforce. Mentors: Dr. Michelle Van Noy and Dr. Will Tyson.

  • AI-powered interactive transfer system

    Strands 1 and 2. Key contributor to an NSF IUSE proposal (under review) in which community college and four-year STEM students codesign an AI-powered transfer support tool with advisors.

  • AI competencies in co-ops and internships

    Strands 1 and 3. PI of an Ohio State study, in partnership with Engineering Career Services, of how AI competencies are defined, developed, and assessed in engineering co-ops and internships. Protocol in development.

  • Technicians becoming engineers (RISE)

    Strands 2 and 3. Key contributor to an NSF Research in the Formation of Engineers proposal (under review) on how engineering technicians move into engineering degree pathways. Led the three-phase exploratory mixed-methods research design.

  • ENTER-AI

    Strands 1 and 3. Key associate on an NSF Advanced Technological Education proposal (under review) for a credit-bearing engineering technician curriculum that integrates AI competencies with industry-readiness skills.

Methods

How the work is done

Qualitative designs (phenomenology, case study, self-study, Delphi, systematic and integrative review, document and policy analysis), quantitative designs (survey design and validation, descriptive and inferential analysis, secondary and institutional data including IPEDS), and explanatory sequential and exploratory mixed methods.