Intelligent Adaptive Interventions Lab

How can you turn any user interface into an intelligent, perpetually improving system? Our answer is to use adaptive experiments that quantify the impact of alternative interventions on people, combining human intelligence (collaborations between designers, scientists, and users) with artificial intelligence - machine learning algorithms and statistical tests that automatically use data from experiments to enhance the interventions future users receive. Our research agenda is to transform components of any real-world user interface into intelligent, adaptive systems that are perpetually enhancing and personalizing interventions to help people. For example, we've published papers on technology for education, learning, and mental health, by testing competing ideas about how to design components of online homework, apps, text messaging interventions, and other interface components. A distinctive focus is micro-experimentation 'in the wild', where we conduct and build tools for randomized A/B comparisons that can be used by practitioners and scientists to test design decisions and hypotheses about how to help people. Another focus is adaptive experimentation – applying and modifying machine learning algorithms and statistical tests for more rapidly yet reliably using data from experiments to change which interventions future participants receive.