AI Goes to School for Math-Instruction Study
Researchers to Create Database of A+ Classroom Teaching
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
. Jing Liu, Brendon Krall, Sarah Montana, Ariel Chung, Heather Hill. Center for Educational Data Science and Innovation and Research Partnership for Professional Learning
The limits of large language models and the necessity of human cognition in K-12 education
. Jiseung Yoo, Campbell F. Scribner. Theory Into Practice. https://doi.org/10.1080/00405841.2025.2528553
. Michael L. Chrzan, Francis A. Pearman, II, Benjamin W. Domingue. EdWorkingPapers.com. https://edworkingpapers.com/ai25-1210
. Ahmed Adel Attia, Dorottya Demszky, Jing Liu, Carol Espy-Wilson. arXiv. https://arxiv.org/abs/2505.17088
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.