Embedded Files
Half-day workshop at Festival of Learning 2026
June 27, 2026 | Seoul, South Korea
About the Workshop
About the Workshop
Adaptive learning systems based on Intelligent Tutoring System (ITS) principles have long been established as effective tools for improving learning gains compared to conventional classroom instruction [1]. Prior work has shown positive results across multiple randomized controlled trials, including studies conducted in middle school mathematics and reading contexts [2-4].
Recent advances in generative AI (GenAI) introduce new opportunities to extend these systems. In particular, GenAI-supported assistants and chatbots offer new ways to support both teachers and students, potentially reducing instructor workload while enabling more responsive and personalized learning interactions.
Open Adaptive Tutor is a fully open-source (MIT License) and Creative Commons adaptive tutoring system based on ITS principles intended to serve as a tool for learning sciences researchers and engineers to engage in rapid experimentation with the features and HCI of computer tutors [5]. In this workshop, which builds on tutorials at AIED ‘23 and L@S ’23, we will introduce OATutor 2.0 which includes a chatbot integration framework designed to support experimentation with LLM agent behavior and efficacy, including a hands-on activity customizing the chatbot using our system prompt design. In addition, guest speakers will share their experience with independent deployments of the tutor as well as best practices on chatbot design from researchers in the field.
Participants will engage in a hands-on activity with the chatbot, followed by discussions on how to integrate chatbots into courses and research contexts, as well as future directions.
Questions?
Questions?
Contact kwak [at] berkeley [dot] edu to get more information
[1] Corbett, A. T., Koedinger, K. R., & Anderson, J. R. (1997). Intelligent tutoring systems. In M. G. Helander, T. K. Landauer, & P. V. Prabhu (Eds.), Handbook of human-computer interaction (pp. 849–874). Elsevier.
[2] Roschelle, J., Feng, M., Murphy, R. F., & Mason, C. A. (2016). Online mathematics homework increases student achievement. AERA Open, 2(4). https://doi.org/10.1177/2332858416673968
[3] Roschelle, J., Shechtman, N., Tatar, D., Hegedus, S., Hopkins, B., Empson, S., Knudsen, J., & Gallagher, L. P. (2010). Integration of technology, curriculum, and professional development for advancing middle school mathematics: Three large-scale studies. American Educational Research Journal, 47(4), 833–878. https://doi.org/10.3102/0002831210367426
[4] Wijekumar, K., Meyer, B. J. F., Lei, P.-W., Lin, Y.-C., Johnson, L. A., Spielvogel, J. A., Shurmatz, K. M., Ray, M., & Cook, M. (2014). Multisite randomized controlled trial examining intelligent tutoring of structure strategy for fifth-grade readers. Journal of Research on Educational Effectiveness, 7(4), 331–357. https://doi.org/10.1080/19345747.2013.853333
[5] Pardos, Z. A., Tang, M., Anastasopoulos, I., Sheel, S. K., & Zhang, E. (2023). OATutor: An open-source adaptive tutoring system and curated content library for learning sciences research. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1–17). https://doi.org/10.1145/3544548.3581204
Page updated
Report abuse