Kuo Wang

Kuo’s research focuses on the use of artificial intelligence to aid in educational measurement across two projects. For Gen-ORF, he creates artificial oral-reading audio data and conducts speech-feature analyses (primarily pauses and prosody) to learn how passage features affect reading performance and passage difficulty. For AI Response, he creates AI examinees of varying performance levels using large language models and analyzes the resulting item calibration, discrimination, and distractor functioning to determine whether item response patterns generated by artificial intelligence replicate those of humans. Collectively, these projects will help reveal what can and cannot be accomplished with synthetic educational assessment data.

As a research assistant, Kuo works on various research projects with researchers from various institutions. He enjoys the research, using both R and Python, and collaborating with the other researchers. Kuo is very proud of his research, which centers on providing children with a better education. 

Selected Publications:

Wang, K.,  Kamata, A., & Kara, Y (2026). GEN-ORF: Generating Synthetic Read-Aloud Audio Data for Passage Difficulty Modeling in Oral Reading Fluency Assessment. (Under review by International Journal of Artificial Intelligence in Education, Elsevier)

 Wang, K., Hua, H., Yan, P., Jiao, H., & Song, D. (2026). Empirical Comparison of Encoder-Based Language Models and Feature-Based Supervised Machine Learning Approaches to Automated Scoring of Long Essays. arXiv.Org. https://arxiv.org/abs/2601.02659v2

Wang, K., Qiao, X., Sammit, G., Larson, E. C., Nese, J., & Kamata, A. (2024). Improving automated scoring of prosody in oral reading fluency using deep learning algorithm. Frontiers in Education, 9. https://doi.org/10.3389/feduc.2024.1440760

Wang, K., Fierbinteanu C. & Maekawa M. (2003). A conceptual framework for spatiotemporal data modeling. In: Marik V., Retschitzegger W., Štěpánková O. (eds) Database and Expert Systems Applications. DEXA 2003, pp 57-66. Lecture Notes in Computer Science, vol 2736. Springer, Berlin, Heidelberg.

Wang, K. (1999). Specifying spatio-temporal requirements with GSL. No.59th (1999) Annual national conference(Information Process Society of Japan). 1999(1). 283-284

Selected Peer-reviewed Presentations

Wang, K. & Kamata, A. (2026).  Generating Synthetic Student Reading Behavior for Passage Difficulty Estimation.  The AIME-Con 2026, Pittsburgh, PA, USA (To be held on October 5–7).

Wang, K. & Kamata, A. (2026). Gen-ORF: Generating Synthetic Read-Aloud Audio Data for Passage Difficulty Modeling in ORF Assessment.  The 2026 National Council on Measurement in Education (NCME) Annual Meeting, Los Angeles, California, USA.

Wang, K., Kara, Y., Wilhelm, A.M., Hunnicutt, J., and Wright, A. (2024). Effects of pre-k and kindergarten classroom quality on student achievements. The 2024 American Educational Research Association (AERA) Annual Meeting, Philadelphia, Pennsylvania.

Wang, K., Qiao, X., Sammit, G., Larson, E. C., Nese, J., & Kamata, A. (2023). Improving automated scoring of prosody using deep learning algorithm. The 2023 National Council on Measurement in Education (NCME) Annual Meeting, Chicago W.