Research
My research program emphasizes actionable knowledge and research practicality to solve critical problems related to the learning process. Together with my research team, I collaborate with educational institutions and industry partners to understand how individuals learn and how they demonstrate their knowledge — from academic achievement to social-emotional learning.
My research program at a glance
From foundational disciplines to real-world impact. Click a foundation to read more, or an application to see the publications behind it.
Three foundations
AI · machine learning · NLP
the methods that connect them
Applied to assessment
Item & feedback generation
with large language models
View papers →Conversation-based assessment
assessment as dialogue
View papers →Adaptive testing
tailored to each learner
View papers →Process-data analysis
how learners solve problems
View papers →Recent highlights
A few representative papers on AI in educational measurement and assessment. See the full publication list for more.
Is it worth the effort? A comparison of automatic educational feedback generated by base and fine-tuned LLMs
Mazzullo, Bulut, et al. (2026) · Education and Information Technologies
Read the paper →The rise of artificial intelligence in educational measurement: Opportunities and ethical challenges
Bulut, Beiting-Parrish, et al. (2024) · Chinese/English Journal of Educational Measurement and Evaluation
Read the paper →A review of automatic item generation techniques leveraging large language models
Tan, Armoush, Mazzullo, Bulut, & Gierl (2025) · International Journal of Assessment Tools in Education
Read the paper →Advancing students' assessment experiences with conversational agents
Yildirim-Erbasli, Bulut, Demmans Epp, & Cui (2025) · Educational Technology Research and Development
Read the paper →Instruction-tuned large-language models for quality control in automatic item generation: A feasibility study
Gorgun & Bulut (2024) · Educational Measurement: Issues and Practice
Read the paper →Exploring slow responses in international large-scale assessments using sequential process analysis
Jerez, Mazzullo, & Bulut (2026) · Computers
Read the paper →Ongoing & past projects
A selection of the research streams my team and I are actively pursuing or have completed.
Automatic Item & Feedback Generation with LLMs
Fine-tuning and evaluating large language models to generate high-quality assessment items and personalized feedback for open-ended questions, with automated quality-assurance pipelines.
View related publications →Conversation-Based Assessment
Designing conversational agents that turn formative assessment into a dialogue — boosting test-taking engagement and eliciting higher-level thinking from students.
View related publications →Process Data & Response Times
Mining clickstream and response-time data from digital assessments to understand problem-solving behavior, detect disengagement, and improve scoring.
View related publications →Personalized & Adaptive Testing
From computerized adaptive testing to intelligent recommender systems for personalized test scheduling, using reinforcement learning and machine learning algorithms.
View related publications →Fairness in Learning Analytics
Developing fair machine learning approaches — including Seldonian algorithms and bias mitigation techniques — for educational prediction and classification tasks.
View related publications →Score Reporting & Feedback
Designing digital score reports and feedback systems (e.g., ExamVis) that help students and teachers turn assessment results into actionable next steps.
View related publications →Partner with us
I work with educational institutions, testing organizations, and industry partners to turn assessment and learning data into actionable insight. If your team is exploring any of the following, I'd be glad to talk.
AI & LLM Evaluation
Designing and independently evaluating AI- and LLM-powered tools for item generation, automated scoring, and feedback — including quality assurance and fairness audits.
Psychometrics & Assessment Design
Test development, item response theory, computerized adaptive testing, validity and reliability studies, and scale development or abbreviation.
Data Science & Analytics
Educational data mining, learning analytics, predictive modeling, and process-data analysis on large-scale assessment and learning-management data.
Interested in working together? Get in touch to discuss a collaboration, consultation, or contract research project.
Current graduate students
My research group includes graduate students in the Measurement, Evaluation, and Data Science (MEDS) program at the University of Alberta. Senior students work closely with new students on a wide range of research projects.
Ashley Clelland
MEDS, University of Alberta · 2022–present
Ashley is interested in applied measurement, data science, and evaluation in the context of public health and education, especially psychometric instrument development and data literacy assessment.
Bin Tan
MEDS, University of Alberta · 2023–present
Bin is interested in applying the methods and techniques in artificial intelligence and data science to modeling educational and psychological constructs (e.g., well-being and motivation).
Elisabetta Mazzullo
MEDS, University of Alberta · 2024–present
Elisabetta is interested in test development, computational psychometrics, and the application of artificial intelligence (AI) technologies to digital assessments in various educational settings.
Joyce Liu
MEDS, University of Alberta · 2025–present
Joyce is interested in computational psychometrics applications, both in low-stakes and high-stakes assessments, to gain deeper insight into students’ learning and competencies.
Kevin Vo
MEDS, University of Alberta · 2023–present
Kevin is interested in utilizing data science, particularly machine learning and educational data mining, to enrich our understanding of important educational and psychological variables.
Daniel Jerez Garcia
MEDS, University of Alberta · 2024–present
Daniel is interested in analyzing large collections of data from educational settings to address important educational and psychological questions.
Eunji Kim
MEDS, University of Alberta · 2025–present
Eunji is interested in applying quantitative and data-analytic methods to address empirical questions in educational measurement, evaluation, and learning.
Former advisees
Graduates of my lab have moved on to faculty positions and professional roles at institutions across North America.
- Joyce Liu — M.Ed.
- Tarid Wongvorachan — Ph.D.
- Guher Gorgun — Ph.D.
- Elisabetta Mazzullo — M.Ed.
- Bin Tan — M.Ed.
- Ashley Clelland — M.Ed.
- Seyma Nur Yildirim-Erbasli — Ph.D.
- Karen Fung — Ph.D.
- Jiaying Xiao — M.Ed.
- Derek Radford — M.Ed.
Prospective students
I welcome inquiries from prospective graduate students interested in computational psychometrics, educational data mining, and AI in education. Before reaching out, please review my current research projects above so we can have a productive conversation about how your interests align with the lab's work. Then get in touch with a brief description of your background and research interests.