6 citations · 9 across the 3 of their papers we have counts for
3 papers
BanditCAT and AutoIRT: Machine Learning Approaches to Computerized Adaptive Testing and Item Calibration
James Sharpnack, Kevin Hao, Phoebe Mulcaire +4
In this paper, we present a complete framework for quickly calibrating and administering a robust large-scale computerized adaptive test (CAT) with a small number of responses. Cal…
AutoIRT: Calibrating Item Response Theory Models with Automated Machine Learning
James Sharpnack, Phoebe Mulcaire, Klinton Bicknell +2
Item response theory (IRT) is a class of interpretable factor models that are widely used in computerized adaptive tests (CATs), such as language proficiency tests. Traditionally,…
Responsible AI for Test Equity and Quality: The Duolingo English Test as a Case Study
Jill Burstein, Geoffrey T. LaFlair, Kevin Yancey +2
Artificial intelligence (AI) creates opportunities for assessments, such as efficiencies for item generation and scoring of spoken and written responses. At the same time, it poses…