Adaptive Questionnaire

Adaptive questionnaires dynamically adjust the questions asked based on a respondent's previous answers, aiming to improve efficiency and data quality compared to traditional static questionnaires. Current research focuses on leveraging machine learning, particularly Bayesian networks and large language models, to create adaptive question sequences, often employing techniques like multi-armed bandits or encoder-decoder architectures to optimize question selection and prediction accuracy. This approach holds significant promise for enhancing various applications, including surveys, voting advice applications, clinical decision support systems, and longitudinal studies, by reducing respondent burden, improving data accuracy, and enabling more efficient data collection.