February 6, 2026
Abstract. The proliferation of artificial intelligence (AI) tools and large language models (LLMs) has sparked dramatic changes to the landscape of post-secondary education resulting in new opportunities—and obligations—to re-evaluate norms for teaching and learning. This presentation includes a brief overview with perspective about rethinking assessment practices—i.e., how student learning is evaluated—during a period of such rapidly evolving technology. The session then transitions to sharing greater detail about ongoing research sponsored by the National Science Foundation, Penn State’s Center for Socially Responsible Artificial Intelligence, and a strategic partnership between Penn State and the University of Auckland in New Zealand, which seeks to develop LLM and AI-based tools intended to amplify instructor efforts to provide timely, personalized feedback to open-ended questions during class, especially for use in large classes (hundreds of students) at scales for which the logistics of doing so would be either untenable or impossible without a teacher-AI partnership. To this end, we also discuss how our team has approached evaluating performance of the tools they develop in order to build trust and confidence that human-AI partnerships make a responsible contribution to teaching and learning.
Beckman, Burke, Fiochetta, Fry, Susan Lloyd, Patterson, Elle Tang (in review). Developing Consistency Among Undergraduate Graders Scoring Open-Ended Statistics Tasks. Preprint URL: https://arxiv.org/abs/2410.18062
Li, Z., Susan Lloyd, Beckman, M. D., & Passonneau, R. J. (2023). Answer-state Recurrent Relational Network (AsRRN) for Constructed Response Assessment and Feedback Grouping. Findings of the Conference on Empirical Methods in Natural Language Processing (EMNLP) 2023. https://doi.org/10.18653/v1/2023.findings-emnlp.254
Susan Lloyd, Beckman, M., Pearl, D., Passonneau, R., Li, Z., & Wang, Z. (2022). Foundations for AI-Assisted Formative Assessment Feedback for Short-Answer Tasks in Large-Enrollment Classes. In Proceedings of the eleventh international conference on teaching statistics. Rosario, Argentina.
Sayali Phadke, Beckman, M.D., Lock Morgan, K. (2024). Measuring contextualized statistical literacy: Evidence from an isomorphic assessment.
Lloyd, S., Beckman, M., Pearl, D., Passonneau, R., Li, Z., & Wang, Z. (2022). Foundations for AI-Assisted Formative Assessment Feedback for Short-Answer Tasks in Large-Enrollment Classes. In Proceedings of the eleventh international conference on teaching statistics. Rosario, Argentina.
Alyssa Hu, Hatfield, N. J., Beckman, M. D. (2025). Exploring individuals’ computational thinking with data. ZDM Mathematics Education, 57. https://doi.org/10.1007/s11858-025-01669-0
Beckman, M. D., Cetinkaya-Rundel, M., Horton, N. J., Rundel, C. W., Sullivan, A. J., & Tackett, M. (2021). Implementing Version Control With Git and GitHub as a Learning Objective in Statistics and Data Science Courses. Journal of Statistics and Data Science Education, 29(1). https://doi.org/10.1080/10691898.2020.1848485
Beckman, M. D., & delMas, R. C. (2018). Statistics students’ identification of inferential model elements within contexts of their own invention. ZDM Mathematics Education, 50(7). DOI: 10.1007/s11858-018-0986-5
Matthew Beckman
Assoc Research Professor | Dept of Statistics
Director | CAUSE (https://causeweb.org/)
office: 421C Thomas Building
email: mdb268 [at] psu [dot] edu
webpage: https://mdbeckman.github.io/