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Publications

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INVITE-supported publications

2026

  • Papers
    • Zapata-Rivera, D., Forsyth, C.M., Zhang, L., & Graesser, A.C. (2026). Exploring the use of generative AI in conversation-based assessment. In Sinatra, A.M., Rus, V., Graesser, A.C., & Lawton, P.M. (Eds.). Design Recommendations for Intelligent Tutoring Systems: Volume 12 – Generative Artificial Intelligence. Orlando, FL: US Army Combat Capabilities Development Command – Soldier Center. 11-24. ISBN 978-0-9977258-7-2
    • Ober, T., Zhang, S., Zapata-Rivera, D., Schroeder, N., & Botelho, A. (2026). Using LLMs to Identify Indicators of Persistence from Students’ Dialogues with a Pedagogical Agent. Journal of Educational Data Mining, 18(1), 208-243.
    • Zhang, S., Zambrano, A. F., Tian, X., Song, Y., Botelho, A. F., Boyer, K. E., Israel, M., & Jiang, S. (in-press). Analyzing middle school students’ dialogue and behaviors during collaborative AI chatbot development using ordered network analysis. In International Conference on Artificial Intelligence in Education (pp. TBD). Cham: Springer Nature Switzerland.
    • Davis Jaldi, C., Saini, A., Zhang, S., Ilkou, E., Schroeder, N., & Shimizu, C. (in-press). Small, private language models as teammates for educational assessment design. In International Conference on Artificial Intelligence in Education (pp. TBD). Cham: Springer Nature Switzerland.
  • Posters, Workshops and WIP
    • Zhang, S., Earle-Randell, T. V., Ganapathy Prasad, P., Liu, Z., Shi, Y., Bhat, S., … & Botelho, A. F. (2026, February). Examining Students’ Code Comprehension with LLMs in Block-and Text-Based Programming. In Proceedings of the 57th ACM Technical Symposium on Computer Science Education V. 2 (pp. 1601-1602).
    • Zhang, S., Ganapathy Prasad, P., Earle-Randell, T. V., Shi, Y., Bhat, S., & Israel, M. (2026, February). Investigating High School Students’ Code Comprehension and Strategy Use Across Block-Based and Text-Based Programming. In Proceedings of the 57th ACM Technical Symposium on Computer Science Education V. 2 (pp. 1603-1604).

2025

  • Papers
    • Mannekote, A., Davies, A., Kang, J., & Boyer, K. E. (2025). Can LLMs reliably simulate human learner actions? A simulation authoring framework for open-ended learning environments. Proceedings of the AAAI Conference on Artificial Intelligence.
    • Earle-Randell, T. V., Zhang, S., Schroeder, N., Boyer, K. E., & Dorley, E. (2025). How virtual agents can shape human-human collaboration: A systematic review. International Conference on Artificial Intelligence in Education. Springer Nature Switzerland.
    • Gladstone, J. R., Schroeder, N. L., Heidig, S., Zhang, S., Palaguachi, C., & Pitera, M. (2025). Do pedagogical agents enhance student motivation? Unraveling the evidence through meta-analysis. Educational Psychology Review.
    • Hou, X., Forsyth, C., Andrews-Todd, J., Rice, J., Cai, Z., Jiang, Y., Zapata-Rivera, D., & Graesser, A. (2025). An LLM-enhanced multi-agent architecture for conversation-based assessment. International Conference of Artificial Intelligence in Education (AIED 2025), 119-134.
    • Hum, S., Gadbury, M., Duda, K., Ginger, J., Lane, H. C., & Nadler, D. (2025). BarrelBots: Measuring self-efficacy and puzzle-based computational thinking in Minecraft. Proceedings of the 19th International Conference of the Learning Sciences (ICLS 2025). International Society of the Learning Sciences.
    • Hum, S., Gadbury, M., Ginger, J., Duda, K., & Lane, H. C. (2025). BarrelBots: ChatGPT feedback for middle school student creative Minecraft artifacts. International Conference on Artificial Intelligence in Education. Springer Nature Switzerland.
    • Kabir, A., Tankala, C., & Lowd, D. (2025). On the practicality of differential privacy for knowledge tracing. Educational Data Mining (EDM’25).
    • Rajarathinam, R. J., & Kang, J. (2025). Taking the lead: Exploring collaborative initiative in CSCL contexts. Proceedings of the 19th International Conference of the Learning Sciences (ICLS 2025). International Society of the Learning Sciences.
    • Schroeder, N. L., Zhang, S., Jaldi, C. D., Gladstone, J. R., López, A. A., & Dorley, E. (2025). Virtual characters help K–12 students learn and improve motivation: A meta-analysis. Review of Educational Research.
    • Sparks, J. R., Lehman, B., Gladstone, J. R., Zhang, S., Schroeder, N., & Israel, M. (2025). Measuring persistence and academic resilience of K-12 students: Systematic review and operational definitions. Frontiers in Education.
    • Zhai, C. (2025). Information retrieval for artificial general intelligence: A new perspective of information retrieval research. Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’25), 3876–3886.
    • Zhou, Y., Kang, J., & Nguyen, H. (2025). Can we trust LLM for video analysis: An exploration of hallucination in multimodal large language model. Proceedings of the 19th International Conference of the Learning Sciences (ICLS 2025). International Society of the Learning Sciences.
  • Posters, Workshops and WIP
    • Mannekote, A., Davies, A., Li, G., Boyer, K. E., Zhai, C., Dorr, B. J., & Pinto, F. (2025, May). Do role-playing agents practice what they preach? Belief-behavior alignment in LLM-based simulations of human trust [Poster presentation]. First Workshop on Social Simulation with LLMs.
    • Davies, A., Nguyen, E., Simeone, M., Johnston, E., & Gubri, M. (2025). Social science is necessary for operationalizing socially responsible foundation models [Workshop paper]. ICLR 2025 Workshop on Human-AI Coevolution.
    • Alvarez, D. E., & Zhai, C. (2025). TINK: Text Information Navigation Kit [Poster/Short Paper]. Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’25), 4056–4060.
    • Barrett, J., Zapata-Rivera, D., Lehman, B., Sparks, J., Ginger, J., Gooch, R., & Israel, M. (2025). WIP: Building on teacher perceptions to help bring AI to K-12 classrooms [Work in progress]. Frontiers in Education Conference, Nashville, TN.
    • Ros, K., Suresh, R., & Zhai, C. (2025). InstInfo: A just-in-time literature recommendation system for presentations [Poster/Short Paper]. Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’25), 4046–4050.
    • Tension, C., Beigman Klebanov, B., Schroeder, N., Zhang, S., Suhan, M., & Zhang, C. (2025). Towards assessing persistence in reading in young learners using pedagogical agents [Conference presentation]. Artificial Intelligence in Measurement and Education Conference (AIME–Con).
    • Zhang, S., Meshram, P. S., Ganapathy Prasad, P., Israel, M., & Bhat, S. (2025). An LLM-based framework for simulating, classifying, and correcting students’ programming knowledge with the SOLO taxonomy [Extended abstract]. Proceedings of the 56th ACM Technical Symposium on Computer Science Education, V. 2, 1681-1682.
  • Digital Repository / Preprint
    • Canby, M. E., Davies, A., Rastogi, C., & Hockenmaier, J. (2024). How reliable are causal probing interventions? arXiv. https://arxiv.org/abs/2408.15510
    • Lamb, T., Davies, A., Paren, A., Torr, P., & Pinto, F. (2024). Focus on this, not that! Steering LLMs with adaptive feature specification. arXiv. https://arxiv.org/abs/2410.22944
    • Lee, S., Davies, A., Canby, M. E., & Hockenmaier, J. (2025). Evaluating and designing sparse autoencoders by approximating quasi-orthogonality. arXiv. https://arxiv.org/abs/2503.24277
    • Mannekote, A., Davies, A., Li, G., Boyer, K. E., Zhai, C., Dorr, B. J., & Pinto, F. (2025). Do role-playing agents practice what they preach? Belief-behavior alignment in LLM-based simulations of human trust. arXiv. https://arxiv.org/abs/2507.02197
    • Modesitt, E., Yang, K., Hulsey, S., Liu, X., Zhai, C., & Kindratenko, V. (2024). ORBIT: Cost-effective dataset curation for large language model domain adaptation with an astronomy case study. arXiv. https://arxiv.org/abs/2412.14436
    • Wu, M., Jiang, J., Zheng, H., Li, M., Li, Z., Tian, B., Chen, B., Park, Y., Zhang, M., Zhai, C., & Nahrstedt, K. (2025). Cache-of-Thought: Master-apprentice framework for cost-effective vision language model reasoning. arXiv. https://arxiv.org/abs/2502.20587
    • Zhang, Y., Li, S., Qian, C., Liu, J., Yu, P., Han, C., Fung, Y. R., McKeown, K., Zhai, C., Li, M., & Ji, H. (2025). The law of knowledge overshadowing: Towards understanding, predicting and preventing LLM hallucination. arXiv. https://arxiv.org/abs/2502.16143

2024

2023

Informing Literature

Some sample works that have contributed to the knowledge base INVITE is inspired by:

Learning STEM in INVITE platforms

Learner Modeling and Adaptive Educational Systems

Learning Analytics and Educational Data Mining

assessment

MACHINE LEARNING AND NATURAL LANGUAGE PROCESSING