Beata Beigman Klebanov is a principal research scientist at EduSoft, a subsidiary of ETS. She received a Ph.D. in computer science with computational linguistics in 2008 and a B.S. (magna cum laude) in computer science in 2000 from the Hebrew University of Jerusalem, Israel. She received an M.S. with distinction in cognitive science from the University of Edinburgh, UK in 2001. Before joining ETS, she was a postdoctoral fellow at the Northwestern Institute for Complex Systems and Kellogg School of Management, where she researched computational approaches to political rhetoric.
Since joining ETS in 2011, Beata has led and contributed to research projects on literacy and language related skills. Since 2017, she has led the Relay Reader project to develop a tool for fostering reading development through interactive oral reading of literature. To date, the tool has been used in the innovative summer literacy program in the Children’s Defense Find’s Freedom School in Camden, New Jersey, as well as in additional summer programs in Bellport, New York; Washington, DC; Highstown, New Jersey; and in the tutoring program of the New Jersey Tutoring Corps. Relay Reader has spurred new research on assessing oral reading published in venues such as Journal of Educational Psychology and the AI in Education and Learning and Knowledge Analytics conferences.
Beata has contributed to the development of the automated scoring and feedback capabilities for writing, including reflective writing, source-based writing, and argumentative writing. Together with Dr. Nitin Madnani of the ETS AI and Product Engineering group, she wrote the monograph Automated Essay Scoring, published in the prestigious Synthesis Lectures on Human Language Technologies series. She serves as an action editor for the International Journal of AI in Education.
Currently Beata co-leads a project on developing generative AI-supported teaching simulations that allow pre-service and in-service teachers to practice critical teaching competences and receive automated formative feedback. The work has recently been recognized with a 2026 National Technology Leadership Initiative Award from the Association of Mathematics Teacher Educators.
Last updated: 2/12/2026