Expanding a free content library for an open-source adaptive tutoring system using large language models
Zachary Pardos, Professor
Education
Applications for Spring 2026 are closed for this project.
Adaptive tutoring systems are designed to provide students in K-12 and intro college courses a personalized homework experience. This means giving the right problem to a student at the right time based on a continuous assessment of their mastery of a skill. At Berkeley, the ALEKS system is used to bridge gaps in Mathematics knowledge between high school and college. These systems are effective but costly and this cost represents an impediment to equitable education, since not all parents and districts can afford this type of product. The Computational Approaches to Human Learning research lab is working on an open-source version of this type of tutoring system. The code-base for the tutoring system has been established, but the content is currently limited. This project calls on students with interests in teaching, technology, equity, and machine learning to draw on open license educational content on the internet (called open educational resources) and natural language AI models (i.e., ChatGPT) to help build-out the content pool for this open-source system.
Join the team at and be at the forefront of AI, prompt engineering, and education technology deployment at Cal.
Role: The role would involve (1) transcribing educational content utilizing a cutting edge AI tool developed for the lab to prompt engineer high quality/impact content at CAL and beyond (2) periodically creating new tutorial content from scratch or using an AI model to assist (3) editing testing, and quality checking educational content transcribed by a network of qualified volunteers and (4) being paired with Berkeley professors help them implement the tutor in their classroom.
Qualifications: Applicants must have an interest in education, technology, and equity. Teaching or tutoring experience in STEM is recommended (Data Science, Mathematics, Chemistry, etc). Specifically, this semester we will be deploying the system in Data 8, with future content also needed for Data 100 and are looking for undergraduates with prior course experience. No technical/programming skills are required; however, if you have a background in NLP, please note that and you could contribute to testing new language models used for generating tutor hints.
Day-to-day supervisor for this project: Ioannis Anastasopoulos, Graduate Student
Hours: 6-8 hrs
Off-Campus Research Site: Remote
Related website: https://dl.acm.org/doi/full/10.1145/3706598.3714051
Related website: https://dl.acm.org/doi/full/10.1145/3706598.3714051