Year: 2025
Type: conference-paper
Abstract: Accurately assessing internal human states is key to understanding preferences, offering personalized services, and identifying challenges in real-world applications.Originating from psychometrics, adaptive testing has become the mainstream method for human measurement and has now been widely applied in education, healthcare, sports, and sociology.It customizes assessments by selecting the fewest test questions .However, current adaptive testing methods face several challenges.The mechanized nature of most algorithms leads to guessing behavior and difficulties with open-ended questions.Additionally, subjective assessments suffer from noisy response data and coarsegrained test outputs, further limiting their effectiveness.To move closer to an ideal adaptive testing process, we propose TestAgent, a large language model (LLM)-powered agent designed to enhance adaptive testing through interactive engagement.This is the first application of LLMs in adaptive testing.TestAgent supports personalized question selection, captures test-takers' responses and anomalies, and provides precise outcomes through dynamic, conversational interactions.Experiments on psychological, educational, and lifestyle assessments show our approach achieves more accurate results with 20% fewer questions than state-of-the-art baselines, and testers preferred it in speed, smoothness, and other dimensions.
Institution: University of Science and Technology of China
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Awards: G3552964096, G4862937960, G6991849872
OpenAlex ID: W4412888904