I am a Research Associate Professor in the Department of Communication of Science and Technology at the University of Science and Technology of China.
My research bridges psychometrics, artificial intelligence, and learning sciences. I develop measurement theories and methods for human learning, use AI to enhance educational assessment, and apply psychometric principles to evaluate AI systems.
Psychometrics & Educational Measurement
Developing theories and methods for educational measurement, including adaptive testing, item response theory, cognitive diagnostic modeling, and process data modeling.
AI for Educational Measurement
Leveraging generative AI to support assessment development, scoring, simulation, and validation.
Psychometrics for AI
Applying psychometric principles to advance the measurement and evaluation of large language models and other AI systems.
Li, P.*, Tang, X.* (co-first author), Chen, S., Cheng, Y., Metoyer, R., Hua, T., & Chawla, N.V. (2026). Adaptive testing for LLM evaluation: A psychometric alternative to static benchmarks. International Conference on Machine Learning (ICML). arXiv:2511.04689. [ICML 26, Spotlight]
Tang, X., & Cheng, Y. (2026). A likelihood-based profile shrinkage algorithm for efficient cognitive diagnostic computerized adaptive testing. Psychometrika, 1-29. https://doi.org/10.1017/psy.2026.10086
Duha, M.S.U., Tang, X., Matsuo, A., Zhu, B., & Maeda, Y. (2025). The effect of social media use on language learning: A meta-analysis. System. https://doi.org/10.1016/j.system.2025.103931
Dong, L., Tang, X., & Wang, X. (2025). Examining the effect of artificial intelligence in relation to students’ academic achievement in classroom: A meta-analysis. Computers and Education: Artificial Intelligence, 100400. https://doi.org/10.1016/j.caeai.2025.100400
Le, V., Nissen, J. M., Tang, X., Zhang, Y., Mehrabi, A., Morphew, J. W., Chang, H. H., & Van Dusen, B. (2025). Applying cognitive diagnostic models to mechanics concept inventories. Physical Review Physics Education Research, 21(1), 010103. https://doi.org/10.1103/PhysRevPhysEducRes.21.010103
Tang, X., Zheng, Y., Wu, T., Hau, K., & Chang, H. H. (2024). Utilizing response time for item selection in on‐the‐fly multistage adaptive testing for PISA assessment. Journal of Educational Measurement, jedm.12403. https://doi.org/10.1111/jedm.12403
Le, V., Van Dusen, B., Nissen, J. M., Tang, X., Zhang, Y., Chang, H. H., & Morphew, J. W. (2024). Mechanics cognitive diagnostic: Mathematics skills tested in introductory physics courses. 2024 Physics Education Research Conference Proceedings, 243–249. https://doi.org/10.1119/perc.2024.pr.Le
Wu, X., Zhang, Y., Wu, R., Tang, X., & Xu, T. (2022). Cognitive model construction and assessment of data analysis ability based on CDA. Frontiers in Psychology, 13, 1009142. https://doi.org/10.3389/fpsyg.2022.1009142
Tang, X., Filonczuk, A, Zhang, X., & Cheng, Y. (accepted). How generative AI helps educational assessment: Various roles it plays in educational measurement research. In Mayrath M., Behrens, J., & Robinson, D. (Eds.), The Handbook of Generative AI in Education: Integrating Research into Practice. Springer. [Book chapter]
Tang, X., Filonczuk, A., & Cheng, Y. (in press). Cognitive diagnostic modeling: New developments, model estimation, and model fit. In Sinharay S. (Ed.), Encyclopedia of Measurement in Social Sciences (2nd ed.). Elsevier. https://doi.org/10.1016/B978-0-443-26629-4.00105-2 [Book chapter]
📖 For a complete and up-to-date list of publications, please visit my Google Scholar profile.