ETS’s Research Institute is one of the key drivers behind ETS’s continued growth and innovation in the fields of assessment and measurement. Our researchers play a critical role in supporting accurate, fair evaluations of learners’ skills while promoting ethical AI use, with work spanning a wide range of focus areas, from shaping policy to examining how cultural and personal experiences influence learning outcomes. Not only are ETS researchers leading the future of assessment and measurement science, but they are also helping to develop the next generation of researchers and scientists.
Through the Ida Lawrence Internship Program, the ETS Research Institute welcomed its summer cohort to the research team. The program recognizes the career and contributions of its namesake, Dr. Ida Lawrence, who spent 38 years at ETS and led the Research & Development area for more than 20 years. Dr. Lawrence had a special bond with interns and successfully brought many of them to ETS after the completion of their internships. Naming the internship program in her honor recognizes the significant impact she had on dozens of junior scholars who are now leaders in the field of measurement.
The talented emerging scholars selected for this program are supporting active research projects while also exploring new questions within their areas of expertise. Each intern brings not only a strong academic background, but also fresh perspectives, curiosity, and a drive to contribute to impactful work.
Meet the Interns
Yucheng Chu is a Ph.D. student in Computer Science at Michigan State University. Under the guidance of mentors Lei Liu and Field Watts, she is focused on developing LLM-based systems for automated grading and feedback. Yucheng is excited to contribute to meaningful work at the intersection of artificial intelligence, education, and assessment.
Gözde Durgut is a Ph.D. candidate in the Second Language Acquisition and Teaching with a minor in Theoretical Linguistics, at the University of Arizona, and is also completing a Master of Science in Data Science. Gözde will work with Tenaha O’Reilly, Caitlin Tenison, and Mikyung Wolf to conduct research on designing guidelines for performance-based AI literacy assessment.
Xiaomeng Huang is pursuing her Ph.D. in Educational Communication and Technology at New York University. Working with mentors Jiangang Hao, Pat Kyllonen, Teresa Ober, and Lei Liu, Xiaomeng is researching automated assessment of communication skills, particularly AI agent behavior and validation in AI-enabled communication assessment.
Miryeong Koo is a Ph.D. candidate in Educational Psychology at the University of Illinois Urbana–Champaign. Miryeong will work with Usama Ali to conduct research evaluating the impact of contextual information in IRT latent regression models for large-scale assessments.
Xinyi Lu, a Ph.D. candidate in Computer Science and Engineering at the University of Michigan, is working on generating and evaluating feedback for argumentative essays alongside her mentor Mo Zhang. She looks forward to collaborating with her team and gaining deeper insight into writing assessment and evaluation.
Xiaoxiao Liu is pursuing her Ph.D. in Measurement, Evaluation, and Data Science at the University of Alberta. Her research explores the potential benefits of AI chatbots to support high school students’ scientific reasoning. Under the guidance of mentors Jessica Andrews Todd and Yang Jiang, Xiaoxiao seeks to develop her research skills while expanding her understanding of conversation-based assessments.
Nanyu Luo is pursuing a Ph.D. in Psychometrics at the University of Toronto. He is working with mentors Michael Fauss and Xiang Liu on projects related to validity, fairness, and AI ethics.
Anna Nasyrova is a Ph.D. candidate in Educational Measurement and Psychometrics at the University of Massachusetts Amherst. Working with mentors Burcu Arslan and Sandip Sinharay, she is exploring measurement methodology in culturally responsive assessments.
Zhen Xu is a Ph.D. candidate in Cognitive Science at Columbia University. Zhen will work with Jamie Mikeska and Beata Beigman Klebanov to conduct research on validating AI-enabled performance assessments to support teacher learning of high-leverage teaching practices.
Shan Zhang is a Ph.D. candidate in the Educational Technology program at the University of Florida, with a minor in Research, Evaluation, and Methodology. Shan will work with Caitlin Tenison, Tenaha O’Reilly, and Teresa Ober to conduct research on measuring AI literacy using AI-supported argumentative writing tasks. She is excited to further explore ways to assess AI literacy processes.