Dr. Pardos is an Associate Professor of Education at UC Berkeley studying adaptive learning and AI. His current research focuses on knowledge representation and recommender systems approaches to increasing upward mobility in postsecondary education using behavioral and semantic data.
He earned his PhD in Computer Science at Worcester Polytechnic Institute with a dissertation on computational models of cognitive mastery. 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.
Latest news releases:
- Policy Brief on AI for Tutoring and Transfer Student Success (by UC Center Sacramento) (PDF file)(link is external)
- Open-source Adaptive Tutoring System launches
- California 100 Grant to Evaluate Education for California’s Future [final reports(link is external)]
- Undergraduate Council Report on Student Learning and Quality of Life in the COVID Era and Beyond (PDF file)(link is external)
- The Resilience of Berkeley - teaching and learning under emergency remote instruction
- The pandemic could open a door to new technology — and dramatic innovation — in education(link is external)
- What to Look for in Online Learning Apps for K-12
- This is Data Science: Using Machine Learning to Broaden Pathways from Community College(link is external)
Please see my social media for other news: https://twitter.com/zpardos(link is external)
https://www.linkedin.com/in/zacharypardos/(link is external)
For a list of publication relating to AI for articulation and wayfinding in higher education, please see: AskOSki Project(link is external)
Publications
Featured:
Lekan, K., & Pardos, Z. A. (2025). AI-Augmented Advising: A Comparative Study of GPT-4 and Advisor-based Major Recommendations(link is external). Journal of Learning Analytics, 12(1), 110-128.
Reza, M., Anastasopoulos, I., Bhandari, S., & Pardos, Z. A. (2025). PromptHive: Bringing Subject Matter Experts Back to the Forefront with Collaborative Prompt Engineering for Educational Content Creation(link is external). In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI '25). Association for Computing Machinery, New York, NY, USA. Article 148, 1–22.
Liu, Y., Bhandari, S., & Pardos, Z. A. (2025). Leveraging LLM respondents for item evaluation: A psychometric analysis(link is external)British Journal of Educational Technology, 00, 1–25
Bhandari, S., Liu, Y., Kwak, Y., & Pardos, Z. A. (2024). Evaluating the Psychometric Properties of ChatGPT-generated Questions(link is external). Computers and Education: Artificial Intelligence, 100284.
Li, Z., Pardos, Z. A., & Ren, C. (2024). Aligning open educational resources to new taxonomies: How AI technologies can help and in which scenarios(link is external). Computers & Education, 216, 105027.
Pardos, Z. A., & Bhandari, S. (2024). ChatGPT-generated help produces learning gains equivalent to human tutor-authored help on mathematics skills(link is external). PLoS ONE, 19(5), e0304013.
Zhuang, Y., Liu, Q., Zhao, G., Huang, Z., Huang, W., Pardos, Z.A., Chen, E., Wu, J., Li, X. (2023). A Bounded Ability Estimation for Computerized Adaptive Testing(link is external). In Proceedings of the Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS). New Orleans, LA.
Pardos, Z.A., Tang, M., Anastasopoulos, I., Sheel, S.K., and Zhang, E. (2023). OATutor: An Open-source Adaptive Tutoring System and Curated Content Library for Learning Sciences Research(link is external). In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI '23). Association for Computing Machinery, New York, NY, USA, Article 416, 1–17.
Kizilcec, R.F., Baker, R.B., Bruch, E., Cortes, K.E., Hamilton, L.T., Lang, D.N., Pardos, Z.A., Thompson, M.E., Stevens, M.L. (2023). From pipelines to pathways in the study of academic progress(link is external). Science, 380, 344-347.
Most Recent:
Goulart, S., Pardos, Z. A. (2025). Measures of Articulation Coverage and Credit Granting. [pre-print(link is external)]
Borchers, C., Xu, Y., & Pardos, Z. A. (2025). Workload Overload? Late Enrollment Leads to Course Dropout(link is external). Journal of Educational Data Mining, 17(1), 126-156.
Badrinath, A., & Pardos, Z. (2025). Optimizing Bayesian Knowledge Tracing with Neural Network Parameter Generation(link is external). Journal of Educational Data Mining, 17(1), 41-65.
Lekan, K., Pardos, Z.A. (2024) AI-Augmented Advising: A Comparative Study of GPT-4 and Advisor-based Major Recommendations(link is external). In Proceedings of the 2024 AAAI Conference on Artificial Intelligence, PMLR 257:85-96
Borchers, C., Xu, Y., & Pardos, Z. A. (2024). Are You an Early Dropper or Late Shopper? Mining Enrollment Transaction Data to Study Procrastination in Higher Education(link is external). Proceedings of the 17th International Conference on Educational Data Mining (EDM). Atlanta, GA, USA. *best short paper award*
Kwak, Y., & Pardos, Z. A. (2024). Bridging large language model disparities: Skill tagging of multilingual educational content(link is external). British Journal of Educational Technology, 00, 1–19.
Mangal, M., & Pardos, Z. A. (2024). Implementing equitable and intersectionality-aware ML in education: A practical guide(link is external). British Journal of Educational Technology, 55, 2003–2038.
Baucks, F., Schmucker, R., Borchers, C., Pardos, Z. A., & Wiskott, L. (2024). Gaining Insights into Group-Level Course Difficulty via Differential Course Functioning(link is external). Proceedings of the Tenth (2024) ACM Conference on Learning@Scale (L@S). Atlanta, GA, USA.
Sucholutsky, I., Collins, K. M., Malaviya, M., Jacoby, N., Liu, W., Sumers, T. R., ... Pardos, Z.A., Weller, A., & Griffiths, T. L. (2024). Representational Alignment Supports Effective Machine Teaching(link is external). arXiv preprint arXiv:2406.04302
Sheel, S., Anastasopoulos, I., and Pardos, Z.A. (2024). Comparing Authoring Experiences with Spreadsheet Interfaces vs GUIs(link is external). In Proceedings of the 14th Learning Analytics and Knowledge Conference (LAK '24). Association for Computing Machinery, New York, NY, USA, 598–607.
Xu, Y. and Pardos, Z.A. (2024). Extracting Course Similarity Signal using Subword Embeddings(link is external). In Proceedings of the 14th Learning Analytics and Knowledge Conference (LAK '24). Association for Computing Machinery, New York, NY, USA, 857–863.
Bhandari, S., Liu, Y., Pardos, Z.A. (2023) Evaluating ChatGPT-generated Textbook Questions using IRT (PDF file)(link is external). Presented at the Generative AI for Education Workshop (GAIED) at the Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS). New Orleans, LA.
Pardos, Z. A., Borchers, C., & Yu, R. (2023). Credit hours is not enough: Explaining undergraduate perceptions of course workload using LMS records(link is external). The Internet and Higher Education, 53, 100882.
Borchers, C., & Pardos, Z. A. (2023). Insights into undergraduate pathways using course load analytics(link is external). In Proceedings of the 13th International Learning Analytics and Knowledge Conference (LAK). Association for Computing Machinery, New York, NY, USA, 219–229. *best paper nominated*
Pardos, Z. A., & Bhandari, S. (2023). Learning gain differences between ChatGPT and human tutor generated algebra hints(link is external). arXiv preprint arXiv:2302.06871.
Xu, L., Pardos, Z. A., & Pai, A. (2023). Convincing the Expert: Reducing Algorithm Aversion in Administrative Higher Education Decision-making(link is external). In Proceedings of the Tenth ACM Conference on Learning@ Scale. Copenhagen, DK. ACM. Pages 215-225.
Xu, Y., Pardos, A.Z. (2023). Mining Detailed Course Transaction Records for Semantic Information(link is external). In Proceedings of the 16th International Conference on Educational Data Mining. Bengaluru, India. Pages 388-395.
Condor, A., Pardos, Z., Linn, M. (2022). Representing Scoring Rubrics as Graphs for Automatic Short Answer Grading(link is external). In Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (Eds.) Artificial Intelligence in Education. LNCS, vol 13355. Springer, Cham. *best paper nominated*
Condor, A. and Pardos, Z. A. (2022) A deep reinforcement learning approach to automatic formative feedback (PDF file)(link is external). In A. Mitrovic and N. Bosch (Eds.) Proceedings of the 15th International Conference on Educational Data Mining. Durham, UK.
McFarland, D. A., Khanna, S., Domingue, B. W., & Pardos, Z. A. (2021) Education Data Science: Past, Present, Future(link is external). AERA Open, 7.
Shao, E., Guo, S., & Pardos, Z. A. (2021). Degree Planning with PLAN-BERT: Multi-Semester Recommendation Using Future Courses of Interest(link is external). Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 14920-14929.
Jiang, W., Pardos, Z.A. (2021) Towards Equity and Algorithmic Fairness in Student Grade Prediction(link is external). In B. Kuipers, S. Lazar, D. Mulligan, & M. Fourcade (Eds.) Proceedings of the Fourth AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES). ACM. Pages 608–617.
Pardos, Z. A., Rosenbaum, L. F., & Abrahamson, D. (2021). Characterizing learner behavior from touchscreen data(link is external). International Journal of Child-Computer Interaction, 100357.
Badrinath, A., Wang, F., Pardos, Z.A. (2021) pyBKT: An Accessible Python Library of Bayesian Knowledge Tracing Models (PDF file)(link is external). In S. Hsiao, & S. Sahebi (Eds.) Proceedings of the 14th International Conference on Educational Data Mining (EDM). Pages 468-474.
Condor, A., Litster, M., Pardos, Z.A. (2021) Automatic short answer grading with SBERT on out-of-sample questions (PDF file)(link is external). In S. Hsiao and S. Sahebi (Eds.) Proceedings of the 14th International Conference on Educational Data Mining (EDM). Pages 345-352.
Yu, R., Pardos, Z.A., Chau, H., Brusilovsky, P. (2021) Orienting Students to Course Recommendations Using Three Types of Explanation(link is external). In Workshop on Explainable User Models and Personalized Systems (ExUM) in the Adjunct Proceedings of the 29th Conference on User Modeling, Adaptation and Personalization (UMAP). Pages 238–245.
Li, Z., Ren, C., Li, X., & Pardos, Z.A. (2021) Learning Skill Transfer Models Across Systems(link is external). In N. Dowell, S. Joksimovic, M. Scheffel, & G. Siemens (Eds.) Proceedings of the 11th International Conference on Learning Analytics and Knowledge (LAK). ACM. Pages 354-363.
Cockkalingam, S., Yu, R., Pardos, Z.A. (2021) Which one's more work? Predicting effective credit hours between courses(link is external). In N. Dowell, S. Joksimovic, M. Scheffel, & G. Siemens (Eds.) Proceedings of the 11th International Conference on Learning Analytics and Knowledge (LAK). ACM. Pages 599-605.
Select Prior Work:
Pardos, Z. A., & Heffernan, N. T. (2010) Modeling Individualization in a Bayesian Networks Implementation of Knowledge Tracing(link is external). In Proceedings of the International Conference on User Modeling, Adaptation, and Personalization (UMAP). Big Island, Hawaii. Springer, Berlin, Heidelberg. Pages 255-266.
Pardos, Z. A., Bergner, Y., Seaton, D., Pritchard, D.E. (2013) Adapting Bayesian Knowledge Tracing to a Massive Open Online College Course in edX (PDF file)(link is external). In S.K. D’Mello, R.A. Calvo, & A. Olney (Eds.) Proceedings of the 6th International Conference on Educational Data Mining (EDM). Memphis, TN. Pages 137-144.
Pardos, Z. A., Baker, R.S.J.d., San Pedro, M.O.C.Z., Gowda, S.M., Gowda, S.M. (2014) Affective States and State Tests: Investigating How Affect and Engagement during the School Year Predict End-of-Year Learning Outcomes(link is external). Journal of Learning Analytics, 1(1), 107–128. [conference version(link is external)]
Pardos, Z.A. (2017) Big Data in Education and the Models that Love Them(link is external). Current Opinion in Behavioral Sciences. Vol 18, 107-113.
Pardos, Z.A., Tang, S., Davis, D., Le. C.V. (2017) Enabling Real-Time Adaptivity in MOOCs with a Personalized Next-Step Recommendation Framework(link is external). In C. Thille & J. Reich (Eds.) Proceedings of the 4th Conference on Learning @ Scale (L@S). ACM. Pages 23-32.
Pardos, Z.A., Chau, H., Zhao, H. (2019) Data-Assistive Course-to-Course Articulation Using Machine Translation(link is external). In J. C. Mitchell & K. Porayska-Pomsta (Eds.) Proceedings of the 6th ACM Conference on Learning @ Scale (L@S). Chicago, IL. ACM. *Best paper award*
Jiang, W., Pardos, Z.A., Wei, Q. (2019) Goal-based Course Recommendation(link is external). In C. Brooks, R. Ferguson & U. Hoppe (Eds.) Proceedings of the 9th International Conference on Learning Analytics and Knowledge (LAK 2019). ACM. Tempe, Arizona. Pages 36-45.
Pardos, Z.A., Fan, Z., Jiang, W. (2019) Connectionist Recommendation in the Wild: On the utility and scrutability of neural networks for personalized course guidance(link is external). User Modeling and User-Adapted Interaction, 29(2), 487–525.
Fischer, C., Pardos, Z. A., Baker, R. S., Williams, J. J., Smyth, P., Yu, R., … Warschauer, M. (2020) Mining Big Data in Education: Affordances and Challenges(link is external). Review of Research in Education, 44(1), 130–160.
Pardos, Z.A., Jiang, W. (2020) Designing for Serendipity in a University Course Recommendation System(link is external). In K. Verbert, M. Scheffel, N. Pinkwart, & V. Kovanovic (Eds.) Proceedings of the 10th International Conference on Learning Analytics and Knowledge (LAK). ACM. Pages 350–359.
Pardos, Z.A., Nam, A.J.H. (2020) A university map of course knowledge(link is external). PLoS ONE 15(9): e0233207. [visualization(link is external)]
For a full publication list, including workshop and poster papers, please consult my [scholar page(link is external) , dblp(link is external), or CV (PDF file)].
Active Research Grants
(Foundation for California Community Colleges) Data-Driven Facilitation of Common Course Numbering [2023-2027]
(College Futures Foundation) Analysis of Credit Loss Reduction Potential in California Public Post-secondary Transfer [2023-2025]
(Accendium) Examining Faculty Decision Making in Course Equivalency and Transfer [2022-2024]
(Bill & Melinda Gates Foundation) Improving SUNY Transfer Policy Through a data-driven analysis comparing equivalent rigor of 2-year and 4-year courses
Past grants:
(California Community Foundation) California 100 [2021-2022]
(Ithika S+R) Improving Articulation of Credit and Transfer Student Support at CUNY [2019-2020]
(Schmidt Futures) Seeding an Undergraduate Learning Engineering Fellowship [2019-2021]
(California OPR) Community Sourced, Data-Driven Improvements to Open, Adaptive Courseware [2019-2022]
(Peder Sather Center Research) Embracing Data in Educational Systems: The Use of Learning Analytics to Support Students at Risk [2019-2020]
(Google) Scaling Cognitive Modeling to Massive Open Environments [2014-2019]
(NSF IIS) Deep Learning in Higher Education Big Data to Explore Latent Student Archetypes and Knowledge Profiles [2015-2019]
(NSF DRK-12) Personalizing Recommendations in a Large-Scale Education Analytics Pipeline [2015-2019]
General research areas:
- Representing knowledge as communicated by student behaviors
- Personalized educational supports leveraging learner process data
- Digital Learning Environments (online courses and Intelligent Tutoring Systems)
I am currently accepting grad students and undergraduate research assistants. Consult tiny.cc/zpUCB(link is external) to schedule a meeting.
Select Service / Professional Activities
- Director of Computational Approaches to Human Learning (CAHL) Research Lab(link is external)
- Director of the virtual advising project, AskOski(link is external)
- Faculty affiliate and Head Graduate Advisor in Cognitive Science(link is external)
- Organizing committee for the IJCAI Workshop on Multimodal Analytics for Understanding Human Learning(link is external)
- Artificial Intelligence in Education Executive committee member
- Program committee member (2020): ACM L@S, ACM LAK, ACM RecSys, ACM SIGCHI (AC), AIED, EDM
- AAAI - EAAI New and Future AI Educator (2018)
- Editorial Board – Journal of Educational Data Mining & Int. Journal of AI in Education
- Panelist/speaker - National Academy of Education: Big Data and Privacy(link is external) (2016)
- Program co-chair of the 2014 Educational Data Mining Conference
- Community Liaison for the International Educational Data Mining Society
- Panelist - White House/OSTP: Big Data and Privacy Workshop, Berkeley (2014)
Teaching
DATA 144(link is external)/EDUC 244(link is external): Data Mining and Analytics (every Fall online) [educ 244 syllabus(link is external)]
EDU C260F(link is external): Machine Learning in Education (every Spring except '24/'26) [educ c260f syllabus(link is external)]
EDUC W161(link is external): Digital Learning Environments (every Spring online except '25) [educ w161 website(link is external) & syllabus(link is external)]
EDUC 290A/003: Computational Approaches to Human Learning (CAHL) research group (every Fall) [educ 290a website(link is external)]
Research group class info: This group will be run as a platform for discussions on topics ranging from analysis of equity, diversity, and inclusion on campus to the role of AI in K-16 education. After the first meeting brainstorming session (and food), a list of topics will be developed that students can choose from to discuss during one meeting of class. The second expected contribution is that each student use one meeting to present work of theirs, related directly or tangentially to the group's research area. Except for the first and last meeting, the class will meet ONLINE (on Zoom) Wednesdays 11:30-1pm (CCN is TBA).
Education
Postdoctoral Associate, RLE & CSAIL - Massachusetts Institute of Technology
Doctor of Philosophy, Computer Science - Worcester Polytechnic Institute
Bachelors of Science, Computer Science - Worcester Polytechnic Institute
