SafeInsights
Join the WaitlistContact UsLogin
SafeInsights
Join the WaitlistContact UsLogin
SafeInsights
Portrait of Debshila Basu Mallick

Debshila Basu Mallick

Scientific Director

A learning engineer and education researcher, Debshila examines how learner characteristics and learning contexts shape outcomes in digital learning environments, how to design and evaluate generative AI so it supports teaching and learning responsibly, and how to build infrastructure that lets researchers study learning at scale while keeping student data private.

Positions and Employment

  • 2024-present | Scientific Director, SafeInsights-Rice University | Houston, TX
  • 2021–present | Director of Research, OpenStax-Rice University | Houston, TX
  • 2019 - 2021 | Research Scientist, OpenStax-Rice University | Houston, TX
  • 2016 - 2019 | Postdoc, OpenStax-Rice University | Houston, TX

Other Relevant Experience, Leadership, and Service

  • 2024–2026 | Lead organizer | AI in education workshops: AI for Education at the Association for the Advancement of Artificial Intelligence (AAAI) Conference 2024; Innovation and Responsibility in AI-Supported Education (iRAISE) at AAAI 2025; and Impactful and Responsible AI Systems for Education (IRAISE) at the International Conference on Artificial Intelligence in Education (AIED) 2026
  • 2024–2026 | Co-instructor | Advanced Machine Learning (ELEC 631), Rice University, with Richard Baraniuk and Lorenzo Luzi
  • 2022-2026 | Organizing Committee, Seventh Annual Workshop on A/B Testing and Platform-Enabled Learning Engineering (PELE), Festival of Learning 2026
  • 2021–present | OpenStax Kinetic Lead, Institute of Education Sciences (IES) SEERNet Research Network
  • 2017–2018 | OpenEd Fellowship, William and Flora Hewlett Foundation
  • 2019 | Data Science Fellow | Insight Data Science

Select Publications

  • Baker, R. S., Giordano, C., Basu Mallick, D., Fletcher, K., Barany, A., Vanacore, K. P., & Hutt, S. (2026). Between privacy and usability: User challenges in a secure educational data enclave. In Proceedings of the 38th Australasian Conference on Human-Computer Interaction (OzCHI '26). Association for Computing Machinery. https://doi.org/10.1145/3847401.3857144

  • Basu Mallick, D., Woodhead, S., Wang, Z., Ananda, M., Burstein, J., & Murphy, A. (2026). Preface: Impactful and responsible AI systems for education. In Proceedings of the Impactful and Responsible AI Systems for Education Workshop (PMLR, Vol. 339, pp. i–x). https://proceedings.mlr.press/v339/basu-mallick26a.html

  • Hatchett, J., Basu Mallick, D., Bradford, B. C., & Baraniuk, R. G. (2026). Learning context matters: Measuring and diagnosing personalization gaps in LLM-based instructional design. In E. G. Blanchard, G. Chen, M. Chi, & S. Isotani (Eds.), Artificial intelligence in education: AIED 2026 (Lecture Notes in Computer Science, Vol. 16582, pp. 277–292). Springer. https://doi.org/10.1007/978-3-032-29755-6_19

  • Diaz Ramos, G., Luzi, L., Basu Mallick, D., & Baraniuk, R. (2026). Stable and privacy-preserving synthetic educational data with empirical marginals: A copula-based approach. In Proceedings of the 19th International Conference on Educational Data Mining (pp. 267–279). International Educational Data Mining Society. https://doi.org/10.5281/zenodo.21040131

  • Bradford, B. C., Basu Mallick, D., & Baraniuk, R. G. (2026). Improving financial education for U.S. college students: A mixed-methods needs assessment. Journal of Financial Counseling and Planning. Advance online publication. https://doi.org/10.1891/JFCP-2024-0048

  • Bradford, B., Basu Mallick, D., Oswald, F., Beier, M., & Baraniuk, R. G. (2026). Beyond the classroom sample: Validating a STEM biographical data measure with online adult learners [Preprint]. SSRN. https://doi.org/10.2139/ssrn.6731985

  • Liu, N., Bradford, B., Hatchett, J., Diaz, G., Luzi, L., Wang, Z., Basu Mallick, D., & Baraniuk, R. (2025). Learning context: A unified framework and roadmap for context-aware AI in education [White paper]. arXiv. https://doi.org/10.48550/arXiv.2512.24362

  • Basu Mallick, D., Slavinsky, J. P., Bainbridge, K., & Baraniuk, R. (2025). Trust by design: Applying the Five Safes to responsible K–12 data [White paper]. OSF. https://osf.io/6p3xy/files/5ar68

  • Bradford, B., Tong, M. C., Pineda, C., Bainbridge, K., & Basu Mallick, D. (2025). Data to trust: Co-designing privacy-preserving research infrastructure with members of the K12 ecosystem [Final report]. OSF. https://osf.io/6p3xy/files/qkw23

  • Sonkar, S., Chen, X., Le, M., Liu, N., Basu Mallick, D., & Baraniuk, R. (2024). Code soliloquies for accurate calculations in large language models. In Proceedings of the 14th Learning Analytics and Knowledge Conference (pp. 828–835). Association for Computing Machinery. https://doi.org/10.1145/3636555.3636889

  • Sonkar, S., Liu, N., Basu Mallick, D., & Baraniuk, R. G. (2024). Marking: Visual grading with highlighting errors and annotating missing bits. In A. M. Olney, I.-A. Chounta, Z. Liu, O. C. Santos, & I. I. Bittencourt (Eds.), Artificial intelligence in education: AIED 2024 (Lecture Notes in Computer Science, Vol. 14829, pp. 309–323). Springer. https://doi.org/10.1007/978-3-031-64302-6_22

  • Sonkar, S., Liu, N., Basu Mallick, D., & Baraniuk, R. (2023). CLASS: A design framework for building intelligent tutoring systems based on learning science principles. In Findings of the Association for Computational Linguistics: EMNLP 2023 (pp. 1941–1961). Association for Computational Linguistics.

  • Basu Mallick, D., Bradford, B. C., & Baraniuk, R. (2023). Secure education and learning research at scale with OpenStax Kinetic. In Proceedings of the Tenth ACM Conference on Learning @ Scale (pp. 360–362). Association for Computing Machinery. https://doi.org/10.1145/3573051.3596187

  • Wang, Z., Valdez, J., Basu Mallick, D., & Baraniuk, R. G. (2022). Towards human-like educational question generation with large language models. In M. M. Rodrigo, N. Matsuda, A. I. Cristea, & V. Dimitrova (Eds.), Artificial intelligence in education: AIED 2022 (Lecture Notes in Computer Science, Vol. 13355, pp. 153–166). Springer. https://doi.org/10.1007/978-3-031-11644-5_13

  • McNamara, D. S., Arner, T., Butterfuss, R., Basu Mallick, D., Lan, A. S., Roscoe, R. D., Roediger, H. L., III, & Baraniuk, R. G. (2022). Situating AI (and big data) in the learning sciences: Moving toward large-scale learning sciences. In F. Ouyang, P. Jiao, B. M. McLaren, & A. H. Alavi (Eds.), Artificial intelligence in STEM education: The paradigmatic shifts in research, education, and technology (pp. 289–307). CRC Press.

  • Wang, Z., Manning, K., Basu Mallick, D., & Baraniuk, R. G. (2021). Towards Bloom's taxonomy classification without labels. In I. Roll, D. McNamara, S. Sosnovsky, R. Luckin, & V. Dimitrova (Eds.), Artificial intelligence in education: AIED 2021 (Lecture Notes in Computer Science, Vol. 12748, pp. 433–445). Springer. https://doi.org/10.1007/978-3-030-78292-4_35

  • Kim, D. Y., Scott, T., Basu Mallick, D., & Mozer, M. (2021). Using semantics of textbook highlights to predict student comprehension and knowledge retention. In Proceedings of the Third Workshop on Intelligent Textbooks at the 22nd International Conference on Artificial Intelligence in Education (CEUR Workshop Proceedings, Vol. 2895). http://ceur-ws.org/Vol-2895/paper08.pdf

  • Chaudhri, V. K., Boggess, M., Aung, H. L., Basu Mallick, D., Waters, A. C., & Baraniuk, R. (2021). A case study in bootstrapping ontology graphs from textbooks. In Proceedings of the 3rd Conference on Automated Knowledge Base Construction. https://doi.org/10.24432/C58C7T