Intelligent Adaptive Interventions Lab
How can you turn any user interface into an intelligent, perpetually improving system? Innovations in A/B Experimentation and interfaces to LLMs.
One example of our work is to transform ubiquitous explanations and prompting questions (e.g. text in a webpage, email, SMS) into an Intelligent Adaptive Intervention. We do this by innovating in uses of A/B experimentation, like inventing the MOOClet toolkit that enhances & personalizes explanations/prompts by A/B testing alternative versions that are generated by human & artificial intelligence (coordinating contributions from designers, social-behavioural scientists, users, chatGPT-like systems), and using adaptive experiments that automatically analyze data and use it to give better versions to future users. Our adaptive experiments apply and advance machine learning algorithms and statistical tests.
Our papers (www.intadaptint.org/papers) span publications in HCI (Human Computer Interaction), applied Machine Learning & Artificial Intelligence, Statistics, Cognitive/Social/Clinical Psychology, Digital Education, Mental Health, and other areas.
Joseph Jay Williams directs the Intelligent Adaptive Interventions lab, as an Assistant Professor in Computer Science (8 Grad Students), with courtesy appointments in Psychology (2 Grad Students) and Statistics (2 Grad Students), as well as Mechanical and Industrial Engineering, Economics, and the Vector Institute for Artificial Intelligence. More information on lab members is at www.intadaptint.org/people, and about Joseph at his CV (https://www.josephjaywilliams.com/additional-information/academic-cv).
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.
For more information, check out our Lab Vision page.
We gratefully acknowledge support from our sponsors: