Teresa M. Ober is a research scientist in the ETS Research Institute. Her work centers on understanding and measuring the skills that learners need to succeed in environments increasingly shaped by artificial intelligence (AI). She conducts research in three connected areas: (a) assessing complex human skills; (b) creating and validating frameworks that connect K–12 learning with skills required for post secondary education and the workforce; and (c) developing AI enabled methods to advance educational research and measurement. In her work on complex skills, she studies how learners respond to challenging situations in digital learning environments, how they progress in the face of difficulty, and how these behaviors can be assessed reliably. Her publications include research on process data, cognitive load, self report measures, and the connection between learner behaviors and performance outcomes. Recent and ongoing work includes developing and validating an adaptability framework and related short form scale, as well as investigating how teacher and student perspectives inform the definition of skills that complement the role of AI in learning and work.
In addition, Teresa’s research seeks to link K–12 content knowledge with broader competency frameworks that reflect the demands students face beyond school. She has co developed frameworks for communication, digital literacy, and AI literacy, as well as models for competency based education aligned with future skill needs. This work connects conceptual development with practical design guidance for assessments, instructional supports, and teacher facing analytics. It also informs policy oriented reports and tools used by educators and program leaders to strengthen students’ preparation for post secondary pathways.
Another line of her work focuses on advancing methods for educational research using AI tools. This includes developing protocols for using large language models to support qualitative analysis, examining the use of synthetic data for instrument development and validation, and studying the reliability of AI generated recommendations within learning systems. These efforts emphasize transparency, interpretability, and methodological soundness, and build on her prior work involving machine learning approaches to understanding student engagement, performance prediction, and analysis of assessment processes.
Teresa has contributed to multiple externally funded research projects that advance the study of learning, assessment, and the use of AI in education. Her work includes roles on projects funded by the Institute of Education Sciences and National Science Foundation , as well as collaborations with university partners and interdisciplinary research teams. Her funded work spans the development of AI enabled feedback systems for middle school science learning, investigations of student persistence during computer-based tasks, and studies that apply machine learning and advanced analytics to understand learning processes. Across these projects, she contributes expertise in complex skills assessment, learning analytics, and AI supported research methods, helping to design studies, develop instruments and protocols, analyze learner data, and translate findings into tools and practices that support instruction and assessment. These collaborations reflect an ongoing commitment to building evidence-based approaches that strengthen educational measurement and improve the design of learning environments in K–12 and beyond.
Her work appears in journals such as Journal of Educational Data Mining, Computers & Education, AERA Open, Psychological Assessment, Journal of Psychoeducational Assessment, and Computer Science Education, along with numerous conference papers in the learning sciences, educational measurement, and AI in education research communities. She has also published handbooks, book chapters, and research reports that support educators, policymakers, and organizations developing skills aligned assessment systems.
Before joining ETS, Teresa was an assistant research professor at the University of Notre Dame and completed her Ph.D. in educational psychology at the Graduate Center of the City University of New York. Across roles, her work aims to contribute to advancing measurement practices, with a more recent focus on improving how AI is used in research and assessment and strengthening the alignment between what students learn in school and the skills they will need in an AI facilitated future.
Teresa Ober | LinkedIn
Last updated: 1/23/2026