OATutor: An Open-source Adaptive Tutoring System and Curated Content Library for Learning Sciences Research

OATutor (Open Adaptive Tutor) is the first open-source adaptive tutoring system built on Intelligent Tutoring System (ITS) principles, developed at UC Berkeley's CAHL Lab. It combines an MIT-licensed, fully engineered codebase with a Creative Commons (CC BY) algebra content library, knowledge tracing, A/B testing infrastructure, and LTI support — designed to democratize adaptive learning research by removing the barrier to replicating and extending experiments that proprietary platforms create.

Despite decades of validated tutoring principles, no adaptive tutoring system had been both fully featured and open-sourced to the research community. This absence forced adaptive learning research onto a small number of proprietary platforms, raising the barrier to entry for researchers wanting to replicate, extend, or field adaptive-learning experiments. OATutor addresses this gap, delivering a system that others can fork, experiment with, and A/B test component-by-component.

The system

OATutor was iteratively developed over three years (from late 2019) with field trials in classrooms and feedback from students, teachers, and researchers. Its core architecture implements established ITS design principles:

Content and authoring

OATutor ships with three Creative Commons (CC BY) OpenStax algebra textbooks (Elementary, Intermediate, College Algebra) curated into the system, with tutoring support authored by the OATutor project. Content follows a hierarchy of Course → Lessons → Problems → Steps → Tutoring Pathway (hints/scaffolds). Authors do not need to know JSON: they author via Google Spreadsheets, and an automated script converts spreadsheets to the JSON content pool, running nightly quality checks. For the College Algebra course, 16 creators completed content in ~6 months with an average of 2.27 hours of training.

Mastery-based adaptive selection

Problems are tagged with skills at the step level via a centralized skill model. Lessons define skills and a target mastery threshold; after each problem, a configurable heuristic selects the next problem the student has not yet mastered — by default, the one with the lowest average posterior mastery across its skills. Learning continues until all lesson skills are mastered.

Significance for the research community

OATutor's design goal is replicability: researchers can run experiments on OATutor, then publish the entire end-to-end framework, content, and platform as a GitHub link for others to replicate, compare, and build on. The open-source, transparent nature also fosters trust and wider adoption. The work is positioned within broader open movements in education — Open Source educational resources, open datasets, and open algorithms (e.g., pyBKT) — and argues it is "long past due" for an ITS-based platform to join them.

Connection to the wiki

OATutor is a foundational example of Adaptive Learning and Intelligent Tutoring research infrastructure, and a companion system for evaluating Generative AI tutoring capabilities (see the companion learning-gain study). It connects to Knowledge Tracing (BKT mastery estimation), Personalized Learning, and Math Education, and its open platform model is directly relevant to the AI Tutoring evidence base and to the automated authoring challenges explored in AI Tutor Authoring Promptdecipher.

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Citation

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. ACM. https://doi.org/10.1145/3544548.3581574