Adaptive Measurement with AI

3 Credits · Level: Graduate

This course provides an opportunity for students to engage in learning new algorithms and data science methods in measurement that is applied across all research fields. Unlike traditional one-size-fits-all assessments, how to make an adaptive test, survey, scale, or game for individuals that are tailored by their ability, interests, behavior, health status, and learning requirements is the major theme to be explored in this course. Topics will include but not limited to fundamental psychometric modeling, item response theory, item bank, item information, equating and differential item functioning, cognitive diagnostic modeling, adaptive testing, game-based assessment, sequence mining on process data, generalized models in large-scale assessment, automated item scoring with NLP, personalized assessment design, and generative AI for automated item generation. In addition, this course will invite 1-2 experts from industry to share the fresh ideas and latest products in measurement to the class. The knowledge taught in this course is emergent and arousing incredibly increasing attention in recent years, especially in high demand at organizations or government that manage large-scale assessments (e.g., World Bank, USAID, OECD, AIR, NCES), high-tech learning, education, game companies (e.g., Pearson, ETS, Duolingo, Roblox), e-commercial high tech platforms imbedded with behavioral science (e.g., Amazon, Meta, Google), public health (e.g., NIH), medical recovery services (e.g., hospital, mental health center), just name a few, and extended to general needs in interdisciplinary research and survey designs.