This project introduces a novel approach for generating synthetic data using generative AI (GenAI) to provide more accurate evaluations of existing and new quantitative methods in real-world settings. Our framework consists of five key steps: (i) pre-processing input data, (ii) training GenAI models on input data, (iii) assessing synthetic data quality, (iv) conducting AI-based simulations, and (v) evaluating simulation results. Our original work on this project can be found below.

Publications/Working Papers

  • Suk, Y., Pan, C., & Yang, K. (2025). Using Generative AI for sequential data generation in Monte Carlo simulation studies. Journal of Educational and Behavioral Statistics. [Journal Article] [Preprint]

Recent Conferences/Seminars

  • Suk, Y., Pan, C, & Yang, K. (2026, April). Using Generative AI for sequential data generation in Monte Carlo simulation studies. The National Council on Measurement in Education (NCME), Los Angeles, CA, U.S.

  • Suk, Y., & Yang, K. (2024, July). Using Conditional Tabular Generative Adversarial Networks for process data generation in Monte Carlo simulation studies. The International Meeting of Psychometric Society (IMPS), Prague, Czech Republic.