Burhan Ogut, PhD

Principal Researcher · AIR · Co-founder & CEO, Inclusive AI Institute · Senior Advisor, RISEI Lab

Research at the intersection of measurement, machine learning, and educational equity.

Sixteen+ years using large-scale assessment data and modern computational methods to surface what works in education — and for which students.

Publications Research Contact

Latest publication · 2025

Universal by Design

Ogut, B., Yin, M., Vu, H., Hicks, J., & Circi, R. (2025). Educational Measurement: Issues and Practice.

Using NAEP process data to disentangle universal-design features from targeted accommodations — which interventions actually help, and for which students.

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Years of research

Publications & reports

IES grants as PI

Funded research

Featured research

Recent work, in detail

Five projects — new papers, IES-funded grants, and the work behind them. Hover to pause; click the dots or arrows to navigate.

Journal Article · 2025

Universal by Design: Unveiling the Effectiveness of Accommodations and Universal Design Features through Process Data

Ogut, B., Yin, M., Vu, H., Hicks, J., & Circi, R. (2025). Educational Measurement: Issues and Practice.

Process data — the keystroke-level record of what students do during a digital assessment — lets us distinguish universal-design features that help everyone from accommodations that help specific learners. The paper shows how to do that distinction empirically, and what the answers look like for NAEP.

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Journal Article · 2025

Running Out of Time: Leveraging Process Data to Identify Students Who May Benefit from Extended Time

Ogut, B., Circi, R., Huo, H., Hicks, J., & Yin, M. (2025). International Electronic Journal of Elementary Education, 17(2), 253–265.

Not every student who finishes late needs extended time — and not every student who needs it asks. This paper uses NAEP process data to identify response-time profiles that predict who would benefit from extended-time accommodations, opening the door to fairer, more individualized assessment design.

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IES Grant · PI

Rethinking Accessibility Using NAEP Process Data: Exploring Universal Design and Accommodations

Award R324P210002 · Institute of Education Sciences · 2020–2023

$699,533 over three years to use NAEP process data and modern psychometric methods to evaluate how universal-design features and accommodations affect test-taking behavior and validity for students with disabilities. The published findings now span four journal articles and a working paper.

AP Calculus and Science Coursetaking: Their Relationships With Choosing a STEM Major and Expecting to Be in a STEM Occupation

Bohrnstedt, G. W., Ogut, B., Yee, D., & Bai, Y. (2023). AERA Open.

Using nationally representative longitudinal data, this paper traces how high-school course-taking sequences — particularly AP Calculus — shape both STEM major choice and STEM occupation expectations, with implications for closing equity gaps in STEM pipelines.

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IES Grant · PI

The Relationship Between Course-Taking Patterns and Postsecondary Outcomes

Award R305A190073 · Institute of Education Sciences · 2019–2022

$583,210 to trace high-school course-taking sequences forward into college enrollment, STEM major choice, and labor-market outcomes — and to ask how different operationalizations of "rigor" change the answers. The grant produced three of the papers featured above.

All grants

Research themes

Three currents that have shaped the work over fifteen years — and continue to.

Accessibility & universal design

Process-data and universal-design approaches that make large-scale assessments more equitable for students with disabilities and other historically underserved learners.

Course-taking & postsecondary transitions

How high school course-taking sequences (and the way we measure them) shape college enrollment, STEM persistence, and labor-market outcomes.

AI & machine learning in education

Applying data mining, NLP, and machine learning to NAEP, ECLS, and other large-scale datasets — with care for bias, accessibility, and what the methods can tell us about real students.

Better measurement is how we find out what's actually working — and for which students. Everything else follows from that. On the work

Affiliations & service

Where the work lives.

American Institutes for Research Inclusive AI Institute RISEI Lab Northwestern University SESP American Educational Research Association National Council on Measurement in Education American Statistical Association IES Peer Reviewer (FY2020)

Open to collaboration & speaking.

Interested in partnering on a research project, commissioning a measurement study, or inviting a talk on AI and equity in education?

Get in touch