唐秀秀，教育心理学博士，现任中国科学技术大学人文与社会科学学院科技传播系特任副研究员。研究方向聚焦于教育与心理测量、人工智能和学习分析交叉领域，主要围绕智能教育环境中的能力测量与数据驱动教育决策开展研究。具体包括复杂认知结构下的自适应测评、人机协同环境中的人工智能融合测评、大语言模型能力评估，以及人工智能环境下新型能力构念的量化研究。相关研究成果发表于Psychometrika、Journal of Educational Measurement 等国际期刊，并有研究成果被机器学习领域顶级会议 ICML 接收并选为 spotlight。目前致力于推动心理测量理论与人工智能方法的融合研究，探索人工智能时代智能测评与教育决策的新型理论与方法框架。
联系方式
联系邮箱：txx@ustc.edu.cn
工作经历
2026.05–至今，中国科学技术大学，科技传播系，特任副研究员
2024.04–2026.04，美国圣母大学，心理系和Notre Dame Learning，博士后研究员
教育背景
2019.08-2024.05，美国普渡大学，教育心理与研究方法学，博士
2017.08-2019.05，美国伊利诺伊大学香槟分校，教育心理学，硕士
2012.09-2016.06，南方医科大学，应用心理学，学士
参与科研项目
2024.05-2026.04，美国国家科学基金项目，化学数据科学人才Cybertraining
2023.01-2024.04，美国国家科学基金项目，基于自适应测验的个性化教学与科研改进
2021.02-2022.12，经济合作与发展组织项目，PISA 自适应测验
期刊/会议论文
Li, P.*, Tang, X.* (co-first author), Chen, S., Cheng, Y., Metoyer, R., Hua, T., & Chawla, N.V. (accepted). Adaptive testing for LLM evaluation: A psychometric alternative to static benchmarks. International Conference on Machine Learning (ICML). Preprint: arXiv:2511.04689. [ICML 2026 Spotlight, acceptance rate 2.2% (536 out of 23,918 submissions)]
Tang, X., & Cheng, Y. (2026). A likelihood-based profile shrinkage algorithm for cognitive diagnostic computerized adaptive testing. Psychometrika.https://doi.org/10.1017/psy.2026.10086[SSCI]
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[SSCI]
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[SSCI]
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 [SSCI]
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[SSCI]
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 [Conference paper]
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 [SSCI]
书章节
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]
部分学术会议汇报
Tang, X., Lu, Y., & Cheng, Y. (2026, April). Can LLMs Replicate Student Testing Behaviors? A Cognitive Diagnostic Modeling Approach.Paper presented at the National Council on Measurement in Education, Los Angeles, CA.
Tang, X., Wang, X., Dong, L., & Zhang, J. (2026, April). Generative AI in Education: A Meta-Analysis of its Impact on Student Achievement. Poster presented at the American Educational Research Association, Los Angeles, CA.
Tang, X., & Cheng, Y. (2025, April). A Likelihood-Based Profile Shrinkage Algorithm for Cognitive Diagnostic Computerized Adaptive Testing. Paper presented at the National Council on Measurement in Education, Denver, CO.
Tang, X., Bediwy, A., & Kern, J. L. (2025, April). Conditional Dependence in RT-Incorporated Item Selection Methods for CAT. Paper presented at the National Council on Measurement in Education, Denver, CO.
Tang, X., Zhang, Y., & Chang, H-H. (2024, April). Developing Dual-objective CD-CAT Algorithms for College Gate-way STEM Courses. Paper presented at the National Council on Measurement in Education, Philadelphia, PA.
Tang, X., Zheng, Y., & Chang, H-H. (2023, April). Incorporating Response Time into On-the-fly Multistage Adaptive Testing for PISA. Paper presented at the American Educational Research Association, Chicago, IL.
Tang, X., Wu, T., Zheng, Y., Hau, T. K., & Chang, H-H. (2022, April). Comparison of On-the-fly MST with Preassembled MST on PISA Data. Paper presented at the National Council on Measurement in Education, San Diego, CA.
奖项
2025, ND Postdoc Spotlight Competition Finalist,圣母大学
2024, Purdue Employee Recognition Award,普渡大学
2023, EDST Graduate Student Travel Award, 普渡大学
2019, Ross Fellowship,普渡大学
2014, Outstanding Student,南方医科大学
2014,The Third-class Scholarship,南方医科大学
