He, P., Li, Z., Wang, Z. Xiong, J., & Li, T. (2026). Judging the Judges: Human Validation of Multi-LLM Evaluation for High-Quality K–12 Science Instructional Materials. Computing Conference 2026.
2025
Chu, Y., He, P., Li, H., Yang, K., Xue, Y., Li, T., Krajcik, J., & Tang, J. (2025). Enhancing LLM-Based Short Answer Grading with Retrieval-Augmented Generation. Proceedings of the 18th International Conference on Educational Data Mining, 396-402. https://doi.org/10.5281/zenodo.15870304
Li, J., Li, T., Li, H., Han, H., He, P., & Liu, H. (2025). Human-AI collaboration for next-generation assessment design: Leveraging LLMs with RAG. 2025 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE).
Zhu, H., Li, T., He, P., & Zhou, J. (2025). Enhancing automated grading in science Education through LLM-driven causal reasoning and multimodal analysis. The 34th International Joint Conference on Artificial Intelligence (IJCAI-25). https://doi.org/10.24963/ijcai.2025/1150
2024
Li, T., Miller, E. A., & He, P. (2024). Culturally and linguistically “Blind” or Biased? Challenges for AI Assessment of Models with Multiple Language Students. In Lindgren, R., Asino, T. I., Kyza, E. A., Looi, C. K., Keifert, D. T., & Suárez, E. (Eds.), Proceedings of the 18th International Conference of the Learning Sciences – ICLS 2024 (pp. 1323-1326). International Society of the Learning Sciences. https://doi.org/10.22318/icls2024.806499.
2023
Li, T., Liu, F., & Krajcik, J. (2023)Automatically assess elementary students’ hand-drawn scientific models using deep learning of Artificial Intelligence. Proceedings of the Annual meeting of the International Society of the Learning Sciences (ISLS).
Wang, H., Li, T., Haudek, K., Royse, E., Manzanares, M., Adams, S., Horne, L., & Romulo, C. (2023).Is ChatGPT a threat to formative assessment in college-level science? An analysis of linguistic and content-level features to classify response types. Proceedings of the 4th International Conference of Artificial Intelligence in Educational Technology (AIET).
Zeng, M., He, P., Shin, N., & Krajcik, J. (2023). Characterizing students’ performances for interactive instructional decisions making to meet individual needs. In Blikstein, P., Van Aalst, J., Kizito, R., & Brennan, K. (Eds.), Proceedings of the 17th International Conference of the Learning Sciences – ICLS 2023 (pp. 1913-1914). International Society of the Learning Sciences. https://doi.org/10.22318/icls2023.267425.
2022
Miller, E., Li, T., Bateman, K., Akgun, S., Makori, H., Codere, S., Danziger, S., Simani, Maria C., & Krajcik, J. (November 2022). Adaptation principles to foster engagement and equity in project-based science learning. Proceedings of the Annual meeting of the International Society of the Learning Sciences (ISLS).