Adaptive Testing

Adaptive testing dynamically adjusts assessment parameters, such as question difficulty or type, based on a test-taker's performance, aiming for efficient and accurate evaluation with minimal questions. Current research emphasizes the integration of machine learning, particularly reinforcement learning and techniques from item response theory, to optimize question selection and improve test security while maintaining accuracy. This approach finds applications across diverse fields, from education and healthcare to cybersecurity and the evaluation of large language models, offering significant improvements in efficiency and the identification of systematic model weaknesses. The development of statistically sound methods for comparing adaptive test results across multiple algorithms is also a key focus.