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Karumbaiah studies human-centered AI for teaching and learning with the aim to augment human intelligence. Her current research focuses on constructing a scientific and critical understanding of equitable and responsible use of AI in classrooms. After being a computer scientist for over ten years, she earned a PhD in learning sciences from the University of Pennsylvania. Her dissertation empirically investigated sources of biases in AI-based learning systems. Before joining UW-Madison, she spent a year as a postdoc fellow at Carnegie Mellon University where she studied ways to augment teacher practices in human-AI partnered instruction.
Education
- PhD Learning Sciences, University of Pennsylvania, 2021
- MS Computer Science, University of Massachusetts Amherst, 2017
- BE Computer Science, Sri Jayachamarajendra College of Engineering, 2011
Select Publications
- Karumbaiah, S., Borchers, C., Shou, T., Falhs, A., Liu, P., Nagashima, T., Rummel, N., & Aleven, V. (2023). A Spatiotemporal Analysis of Teacher Practices in Supporting Student Learning and Engagement in an AI-enabled Classroom. Proceedings of the 24th International Conference on Artificial Intelligence in Education (AIED)
- Karumbaiah, S., Borchers, C., Falhs, A., Holstein, K., Rummel, N., & Aleven, V. (2023). Teacher Noticing and Student Learning in Human-AI Partnered Classrooms: A Multimodal Analysis. Proceedings of the 18th International Conference of the Learning Sciences (ICLS)
- Karumbaiah, S., Baker, R., Tao, Y., & Liu, Z. (2022). How does Students’ Affect in Virtual Learning Relate to Their Outcomes? A Systematic Review Challenging the Positive-Negative Dichotomy.. Proceedings of the 12th International Learning Analytics and Knowledge Conference (ACM LAK)
- Karumbaiah, S., Baker, R. S., Ocumpaugh, J., & Andres, J. (2021). A Re-Analysis and Synthesis of Data on Affect Dynamics in Learning. IEEE Transactions on Affective Computing (IEEE TAC)
- Karumbaiah, S., Baker, R., & Ocumpaugh, J. (2021). Context Matters: Differing Implications of Motivation and Help-Seeking in Educational Technology. International Journal of Artificial Intelligence in Education (IJAIED).
- Karumbaiah, S., & Brooks, J. (2021). How Colonial Continuities Underlie Algorithmic Injustices in Education.. Proceedings of the IEEE Research in Equity and Sustained Participation in Engineering, Computing, and Technology (IEEE RESPECT)
- Karumbaiah, S., Lan, A., Nagpal, S., Baker, R., Botelho, A., & Heffernan, N. (2021). Using Past Data to Warm Start Active Machine Learning: Does Context Matter?. Proceedings of the 11th International Learning Analytics and Knowledge Conference (ACM LAK) [Nominated for Best Paper Award]
- Karumbaiah, S., & Baker, R. (2020). Studying Affect Dynamics using Epistemic Networks. Proceedings of the 2nd International Conference on Quantitative Ethnography (ICQE). [Nominated for Best Paper Award]
- Karumbaiah, S., Baker, R., Barany, A., & Shute, V. (2019). Using Epistemic Networks with Automated Codes to Understand Why Players Quit Levels in a Learning Game. Proceedings of the 1st International Conference on Quantitative Ethnography (ICQE)
- Karumbaiah, S., Baker, R., & Shute, V. (2018). Predicting Quitting in Students Playing a Learning Game. Proceedings of the 11th International Conference on Educational Data Mining (EDM). [Nominated for Best Paper Award]
Select Presentations
- (2023, February). Teacher in Action with AI Tutors. Invited Seminar presented at the Interdisciplinary Training Program for Predoctoral Research in the Education Sciences (ITP), University of Wisconsin-Madison.
- (2022, April). Algorithmic Injustices in Education. Invited Talk presented at the , Pace University.
- (2021, September). The Upstream Sources of Bias in Educational Adaptive Systems. Invited Talk presented at the , Microsoft PROSE Research Team.
- (2020, April). Re-Analysis and Synthesis of Data on Affect Dynamics in Learning. Invited Talk presented at the , University of California Irvine.
- (2019, April). Machine Learning and Learning Sciences. Featured Talk presented at the Catalyst with ProjectEd, University of Pennsylvania.
Select Awards and Honors
- Nellie McKay Fellowship, University of Wisconsin–Madison, 2025
- Best Paper Nomination (as first author), ACM LAK, 2021
- Rising Stars in EECS, Massachusetts Institute of Technology, 2021
- Best Paper Nomination (as first author), ICQE, 2020
- Best Paper Nomination (as first author), EDM, 2018
- Best Paper Nomination (as first author), ICCE, 2018
- Dean’s Fellowship, University of Pennsylvania, (2017–2021)