Zachary Pardos

Zachary Pardos is an Associate Professor of Education at UC Berkeley studying adaptive learning and AI. His early scholarship focused on formative assessment using Knowledge Tracing, the predominant model used for estimating skill mastery in computer tutoring system contexts. His recent work designing Human-AI collaborations to pave pathways to and within higher education systems has been published in venues such as SIGCHI, AAAI, The Internet and Higher Education, and Science. This work has included the development of high-quality tools used by tens of thousands of users, including course recommender systems (AskOski.com), a Python library for Knowledge Tracing (pyBKT.com), and an open-source adaptive tutoring system and creative commons content library (OATutor.io).

He earned his PhD in Computer Science at Worcester Polytechnic Institute. Funded by a National Science Foundation Fellowship (GK-12), he spent extensive time with K-12 educators and students working to integrate educational technology into the curriculum as a formative assessment tool. After completing his PhD in 2012, he spent one year as a Postdoctoral Associate at the Massachusetts Institute of Technology. At Cal, he directs the Computational Approaches to Human Learning research lab, teaches in the data science undergraduate program, and is an affiliated faculty in Cognitive Science.