Faculty

Dora Demszky

Assistant Professor in Education Data Science, Assistant Professor (By courtesy) in Computer Science

Dr. Demszky is an Assistant Professor in Education Data Science at the Graduate School of Education at Stanford University. She works on developing natural language processing methods to support equitable and student-centered instruction. She has developed tools to give feedback to teachers on dialogic instructional practices, to analyze representation in textbooks, measure the presence of dialect features in text, among others. Dr Demszky has received her PhD in Linguistics at Stanford University, supervised by Dr Dan Jurafsky. Prior to her PhD, Dr. Demszky received a BA summa cum laude from Princeton University in Linguistics with a minor in Computer Science.

Publications

Demszky, D., Liu, J., Hill , H., Sanghi, S., & Chung, A. (2025). Automated feedback improves teachers’ questioning quality in brick-and-mortar classrooms: Opportunities for further enhancement. Computers & Education, 227. https://doi.org/10.1016/j.compedu.2024.105183
Malik , R. ., Abdi, D., Wang, R. ., & Demszky, D. (2025). Scaffolding Middle-School Mathematics Curricula With Large Language Models. British Journal of Education Technology. https://doi.org/10.1111/bjet.13571
Wang, R., & Demszky, D. (2024). An Open-Source Library for Education Conversation Data. NAACL System Demonstrations. https://arxiv.org/pdf/2402.05111
Wang, R., & Demszky , D. (2024). An Open-Source Library for Education Conversation Data. NAACL System Demonstrations. https://arxiv.org/pdf/2402.05111
Wang, R., Zhang, Q., Robinson, C., Loeb, S., & Demszky, D. (2024). Bridging the Novice-Expert Gap via Models of Decision-Making: A Case Study on Remediating Math Mistakes. Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL). https://arxiv.org/pdf/2310.10648
Demszky, D., Wang, R., Geraghty, S., & Yu, C. (2024). Does Feedback on Talk Time Increase Student Engagement? Evidence from a Randomized Controlled Trial on a Math Tutoring Platform. The 14th Learning Analytics and Knowledge Conference (LAK). https://spaces-cdn.owlstown.com/blobs/ttf4t8cpptbt692mxzao4p3d50af
Wang, R., Wirawarn, P., Khattab, O. ., Goodman, N., & Demszky, D. (2024). Backtracing: Retrieving the Cause of the Query. Findings of the Association for Computational Linguistics: EACL. https://doi.org/doi.org/10.48550/arXiv.2403.03956
Demszky, D., Williams, C., Brady, S., Subrahmanya, S., Gaudiello, E., Walton, G., & Eichstaedt, J. (2024). Computational Language Analysis Reveals that Process-Oriented Thinking About Belonging Aids the College Transition. https://doi.org/10.26300/5mm8-7m81
Attia, A., Demszky, D., Ogunremi, T., Liu, J., & Espy-Wilson, C. . (2024). Continued Pretraining for Domain Adaptation of Wav2vec2. 0 in Automatic Speech Recognition for Elementary Math Classroom Settings. arXiv preprint arXiv, 2405. https://arxiv.org/pdf/2405.13018
Lee, V., Abdi, D., Coelho, R., Bywater, C., Levine, S., & Demszky, D. (2024). Identifying Pedagogical Opportunities for Text Data Visualizations in English Language Arts through Co-Design. Proceedings of the 18th International Conference of the Learning Sciences-ICLS 2024, Pp. 2201-2202. International Society of the Learning Sciences. https://repository.isls.org/bitstream/1/10935/1/ICLS2024_2201-2202.pdf
Attia, A., Liu, J., Ai, W., Demszky, D., & Espy-Wilson, C. (2024). Kid-Whisper: Towards bridging the performance gap in automatic speech recognition for children vs. adults. AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES).
Wang, R., Wirawarn, P., Lam, K., Khattab, O., & Demszky, D. (2024). Problem-Oriented Segmentation and Retrieval: Case Study on Tutoring Conversations. Findings of the Association for Computational Linguistics: EMNLP.
Wang, R., Ribeiro, A., Robinson, C., Loeb, S. ., & Demszky, D. (2024). Tutor CoPilot: A Human-AI Approach for Scaling Real-Time Expertise. arXiv preprint arXiv:2410.03017. https://arxiv.org/abs/2410.03017
Demszky , D., Bush, J., D’Mello, S., Jacobs, J., Hau, I., Hill, H., Liu, J., Loeb, S., Maples, B., Pepper, K., Pokorny, R., Rascoff, M., Robinson, J., Yeager, D., & Wentworth, L. (2023). Empowering Educators via Language Technology. https://ddemszky.github.io/papers/Empowering%20Educators%20via%20Language%20Technology.pdf
Demszky, D., Yang, D., Yeager, D., Bryan, C., Clapper, M., Chandhok, S., Eichstaedt, J., Hecht, C., Jamieson, J., Johnson, M., Jones, M., Krettek-Cobb, D., Lai, L., JonesMichell, N., Ong, D., Dweck, C., Gross, J., & Pennebaker, J. (2023). Using large language models in psychology. Nature Reviews Psychology. https://doi.org/10.1038/s44159-023-00241-5
Mah, C., Tan, M., Phalen, L. ., Sparks, A., & Demszky , D. From Sentence-Corrections to Deeper Dialogue: Qualitative Insights from Llm and Teacher Feedback on Student Writing. SSRN. https://doi.org/10.2139/ssrn.5213040