Publications

  • Akgun, S., & Krajcik, J. (in press). Artificial intelligence as the growing actor in education: Addressing the issues of power and ethics of AI in K-12 STEM classrooms. In X. Zhai & J. Krajcik (Eds.), Uses of Artificial Intelligence in STEM Education (pp. xxx-xxx). Oxford University Press.

  • Bradford, L. (in press). Use of machine learning to score teacher observations. In X. Zhai & J. Krajcik (Eds.), Uses of Artificial Intelligence in STEM Education (pp. xxx-xxx). Oxford University Press.

  • He, P., Shin, N., Zhai, X., & Krajcik, J. (in press). Guiding teacher use of artificial intelligence-based knowledge-in-use assessment to improve instructional decisions: A conceptual framework. In X. Zhai & J. Krajcik (Eds.), Uses of Artificial Intelligence in STEM Education (pp. xxx-xxx). Oxford University Press.

  • He, P. Shin, N. Kaldaras L., & Krajcik, J. (in press). Integrating artificial intelligence into learning progression-based learning systems to support student knowledge-in-use: Opportunities and challenges. In Jin, H., Yan, D., & Krajcik, J. Handbook of Research in Science Learning Progressions.

  • Kaldaras, L., Akaeze, H., & Krajcik, J. (2021). Developing and validating Next Generation Science Standards-aligned learning progression to track three‐dimensional learning of electrical interactions in high school physical science. Journal of Research in Science Teaching, 58(4), 589-618.

  • Kaldaras, L. (2020). Developing and validating NGSS-aligned 3D learning progression for electrical interactions in the context of 9th grade physical science curriculum. Michigan State University.

  • Kaldaras L, Yoshida NR., & Haudek KC (2022) Rubric development for AI-enabled scoring of three-dimensional constructed-response assessment aligned to NGSS learning progression. Front. Educ. 7:983055. doi: 10.3389/feduc.2022.983055

  • Kaldaras, L., Li, T., Haudek, K., & Krajcik, J. (accepted). NGSS learning progression-based scoring of scientific models. Paper submitted to the 2024 Annual Meeting of the American Educational Research Association (AERA), Philadelphia, PA.

  • Kaldaras, L., Li, T., Haudek, K., & Krajcik, J. (accepted). Rubric development for AI scoring of NGSS learning progression-based scientific models to support individual opportunity to learn. Paper submitted to the 2024 annual conference of National Association of Research in Science Teaching (NARST), Denver, CO.

  • 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).

  • Li, T., Kaldaras, L., Haudek, K., & Krajcik, J. (accepted). Using AI to evaluate multi-modal formative assessments in physical science. Paper submitted to the 2024 Annual Meeting of the American Educational Research Association (AERA), Philadelphia, PA.

  • Li, T., Kaldaras, L., Haudek, K., & Krajcik, J. (accepted). Utilizing deep learning AI to evaluate scientific models: overcoming the challenges. Paper submitted to the 2024 annual conference of National Association of Research in Science Teaching (NARST), Denver, CO.

  • Shiroda, M., Doherty, J., & Haudek, K. C. (in press). Exploring attributes of successful machine learning assessments for scoring of undergraduate constructed response assessment items. In X. Zhai & J. Krajcik (Eds.), Uses of Artificial Intelligence in STEM Education (pp. xxx-xxx). Oxford University Press.

  • 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).

  • Zhai, X., He, P., & Krajcik, J. (2022). Applying machine learning to automatically assess scientific models. Journal of Research in Science Teaching, 59(10), 1765–1794. https://doi.org/10.1002/tea.21773.

  • Zhai, X., & Krajcik, J. (in press). Pseudo artificial intelligence bias. In X. Zhai & J. Krajcik (Eds.), Uses of Artificial Intelligence in STEM Education (pp. xxx-xxx). Oxford University Press

  • Zhai, X., & Krajcik, J. (Eds.). (in press). Uses of Artificial Intelligence in STEM Education. Oxford University Press.