My research focuses on the valid and trustworthy use of artificial intelligence (AI) in education. I am currently advised by Professor Hyo Jeong Shin at Sogang University.
I am a prospective Ph.D. applicant for Fall 2027, with research interests at the intersection of psychometrics, educational measurement and evaluation, and generative AI.
Excited to join the organizing committee and help build a focused research community around LLM Psychometrics.
Honored to have the opportunity to build this research community alongside Giora Alexandron (Weizmann Institute of Science) ↗.
Heading to Pittsburgh & San Francisco to present new work on validity-aware AI assessment and psychometric approaches to evaluating AI systems.
Grateful to have served as Student Volunteer Chair and helped bring together the AIED, EDM, and L@S communities in Seoul.
A special thank you to my co-chair, Jinsook Lee (Cornell University) ↗, for being such a wonderful colleague!
Validity theory, item response theory, structural equation modeling, measurement invariance, fairness, and score comparability.
Large language models, automated scoring, embeddings, retrieval-augmented generation, multi-agent systems, and AI evaluation.
Statistical modeling, survey methods, multivariate analysis, educational data science, and empirical validation.
Learning processes, instructional contexts, and evidence-centered educational design.
Human judgment, trust, collaboration, fairness perceptions, and AI-supported decision making.
Computational modeling of learner, assessment, interaction, and behavioral data.
Interpreting learning traces to support learners, teachers, and institutions.
Validity-aware automated item generation, scoring, feedback, multilingual adaptation, and assessment workflows.
Adaptive, interpretable systems connecting measurement, learning analytics, and human–AI collaboration.
Integrated environments combining learner data, assessment evidence, analytics, and intelligent support.
M.Ed. in AI Integrated Educational Design & Management
GPA 4.48/4.50 · Expected Feb 2027
Academic Exchange Education Program
B.A. in American CultureB.E. in Big Data ScienceB.Ed. in Educational CultureSumma cum laude · GPA 4.12/4.50
Consultant & AI Research Scientist
Project Manager
Administrative Assistant · Office of International Affairs
Coordinated paper session proposal submitted to the NCME 2027.
I brought psychometric latent-trait modeling into the AI-evaluation conversation and used the talk to connect two research communities that do not always speak the same methodological language.
I helped support an international learning-sciences community across LAK, AIED, and EDM, coordinating the student-volunteer experience and contributing to a welcoming conference environment.
I presented our work on English-to-Bulgarian assessment adaptation and emphasized that translation quality is ultimately a measurement-equivalence question, not only a language-quality question.
I shared empirical results connecting option-level linguistic properties with item functioning, bringing a language-sensitive perspective into psychometric item analysis.
I presented a practical framework for item review and item analysis, linking psychometric reasoning with real-world decisions about validity, wording, scoring, and assessment quality.
For a complete and up-to-date publication record, please see my Google Scholar profile.
Selected presentations are highlighted below. For further details on related publications, please contact me.
Externally commissioned research and consulting projects are listed below. Projects subject to confidentiality agreements are not included.
Professional societies, conference series, and research communities that connect closely with my work in educational measurement, psychometrics, learning sciences, and artificial intelligence.
Educational measurement, assessment, validity, fairness, and score interpretation.
Psychometric theory, quantitative methodology, latent-variable modeling, and measurement science.
Artificial intelligence in education, intelligent learning systems, and human-centered educational AI.
Learning analytics, learner modeling, educational data, and data-informed learning design.
Educational data mining, computational modeling of learning, and data-driven educational research.
Learning processes, learning environments, collaborative learning, and the role of technology in supporting learning.
| Conference | Society / Community | Primary focus | Link |
|---|---|---|---|
| NCME Annual Meeting | NCME | Educational measurement · validity · assessment | Visit ↗ |
| AIME-Con | NCME | Artificial intelligence in measurement and education | Visit ↗ |
| IMPS | Psychometric Society | Psychometrics · quantitative methodology | Visit ↗ |
| ICLS | ISLS | Learning sciences · learning environments · technology | Visit ↗ |
| AIED | International AIED Society | Artificial intelligence in education | Visit ↗ |
| LAK | SoLAR | Learning analytics · learner modeling | Visit ↗ |
| EDM | International Educational Data Mining Society | Educational data mining · computational learning research | Visit ↗ |
| ACL | Association for Computational Linguistics | NLP · language models · evaluation | Visit ↗ |
| COLM | Conference on Language Modeling | Language models · evaluation · foundations | Visit ↗ |