In this talk, we present a comprehensive overview of the construction of the Duolingo English Test (DET), a high-stakes, large-scale, fully online English language proficiency test built using human-in-the-loop (HIL) AI. The DET is a computerized adaptive test where test takers respond to items designed to assess their proficiency in speaking, writing, reading, and listening. Human oversight plays a critical role in ensuring the DET's fairness, lack of bias, construct validity, and security. Additionally, AI and foundation models enable the scalability of key processes, including real-time security features and automated scoring.

We will take a tour of this process, organized into five parts. First, items are constructed using generative AI models like GPT, followed by human review to evaluate potential bias and fairness. Second, machine learning models automatically grade items by predicting expert human ratings based on construct-aligned AI features. Third, items are calibrated using explanatory item response theory models and custom item embeddings, aligning them with historical test taker performance. Fourth, tests are administered via Thompson sampling, motivated by framing computerized adaptive testing as a contextual bandit problem, and scored using a fully Bayesian approach. Finally, automated AI signals, such as eye-gaze tracking and LLM response detection, support proctors in detecting cheating and ensuring adherence to test rules. Throughout this process, humans and AI collaborate seamlessly to maintain the DET's integrity and effectiveness.