UMD Researchers to Build AI Database
to Improve Math Learning Outcomes
Improving education systems and outcomes
through more effective use of data science, technology, and AI
M-Powering Teachers
Enhancing the professional growth and effectiveness of teachers
Improving education systems and outcomes
through more effective use of data science, technology, and AI
Kimberly A. Griffin
Professor and Dean of the College of Education
The Center for Educational Data Science and Innovation, also known as EDSI, aligns well with UMD's commitment to innovation and exploration in the areas of data science and AI. EDSI will be a unique interdisciplinary center, committed to research, practice, and dissemination of critical information to ensure the ethical use of AI to enhance equity, effectiveness, and efficiency in education.
Jennifer King Rice
Senior Vice President and Provost
This center holds great potential for improving education systems and outcomes through more effective use of data science, technology, and AI. I am particularly excited about the cutting edge research that this center will enable, and the potential for that research to drive data-informed policy and practice, technological advances, and ultimately, more effective and equitable educational opportunities for all learners.
Publications
Recent Publications
. Sarah Chung, Claudia Persico, Jing Liu. NBER. http://www.nber.org/papers/w33549
. Dorottya Demszky, Jing Liu, Heather C. Hill, Shyamoli Sanghi, Ariel Chung. EdWorkingPapers.com. https://doi.org/10.26300/8pnw-5q67
. Ahmed Adel Attia, Jing Liu, Wei Ai, Dorottya Demszky, Carol Espy-Wilson. arXiv. https://doi.org/10.48550/arXiv.2309.07927
. Ahmed Adel Attia, Dorottya Demszky, Tolulope Ogunremi, Jing Liu, Carol Espy-Wilson. arXiv. https://doi.org/10.48550/arXiv.2405.13018
Events
Inaugural International Workshop on Rethinking Children's Automatic Speech Recognition for Education
September 10, 2025, 8:00am - 5:00pm
University at Buffalo
In K–12 education, Automatic Speech Recognition (ASR) is increasingly being applied to support learning, engagement, and accessibility. However, developing ASR systems for children continues to pose as a monumental challenge, especially considering the stringent requirement resulting from various use cases in children’s education settings. Moreover, representative speech datasets from these populations are particularly scarce, while data collection is further complicated by ethical considerations such as privacy and informed consent.