Changing Behaviour By Designing Intelligent Adaptive Interventions

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Changing Behaviour By Designing Intelligent Adaptive Interventions

Popis výsledku v původním jazyce

How can we change people's behaviour to help them achieve their goals? I present tools and methods for optimizing a product or intervention by making it Intelligent and Adaptive, including: (1) Messages/Prompts in Mobile Apps; (2) Explanations of concepts in websites; (3) Texts & Emails for Marketing & Health. We use the AdapComp framework (1st in $1M Xprize competition for future of experimentation) to transform components of an interface into Intelligent agents, that integrate human intelligence (crowdsourcing) and artificial intelligence (LLMs). These components become Adaptive by using perpetually MicroExperiments to test out these different actions, and using qualitative and quantitative data to optimize what to show to a user/customer, in different contexts. We show how to use AdapComps to integrate AI/LLMs, Machine Learning for adaptive experiments, theories of psychology, and human-computer interaction/user experience design. Bio: Joseph Jay Williams is an Assistant Professor at the University of Toronto in Computer Science, with courtesy appointments to supervise PhD students in Statistical Science, Psychology, and the Vector Institute for Artificial Intelligence, and courtesy appointments in Economics & Industrial Engineering. He directs the Intelligent Adaptive Interventions lab, which aims to transform any user interface into an Intervention to help people change their behaviour and learn, by reimagining randomized "A/B" experiments as a tool for Intelligent Adaptation. His lab's work is represented in over 80 papers (www.intadaptint.org/papers), 2 Best Paper Awards (1 at CHI), 4 Runner Up/Honorable Mention for Best Paper (CHI, EDM, LAS), and 1st place in a $1M Xprize competition for the future of experimentation technology in education. They've received over $2M in grant funding, enabling interventions impacting over 500 000 people.

Název v anglickém jazyce

Changing Behaviour By Designing Intelligent Adaptive Interventions

Popis výsledku anglicky

How can we change people's behaviour to help them achieve their goals? I present tools and methods for optimizing a product or intervention by making it Intelligent and Adaptive, including: (1) Messages/Prompts in Mobile Apps; (2) Explanations of concepts in websites; (3) Texts & Emails for Marketing & Health. We use the AdapComp framework (1st in $1M Xprize competition for future of experimentation) to transform components of an interface into Intelligent agents, that integrate human intelligence (crowdsourcing) and artificial intelligence (LLMs). These components become Adaptive by using perpetually MicroExperiments to test out these different actions, and using qualitative and quantitative data to optimize what to show to a user/customer, in different contexts. We show how to use AdapComps to integrate AI/LLMs, Machine Learning for adaptive experiments, theories of psychology, and human-computer interaction/user experience design. Bio: Joseph Jay Williams is an Assistant Professor at the University of Toronto in Computer Science, with courtesy appointments to supervise PhD students in Statistical Science, Psychology, and the Vector Institute for Artificial Intelligence, and courtesy appointments in Economics & Industrial Engineering. He directs the Intelligent Adaptive Interventions lab, which aims to transform any user interface into an Intervention to help people change their behaviour and learn, by reimagining randomized "A/B" experiments as a tool for Intelligent Adaptation. His lab's work is represented in over 80 papers (www.intadaptint.org/papers), 2 Best Paper Awards (1 at CHI), 4 Runner Up/Honorable Mention for Best Paper (CHI, EDM, LAS), and 1st place in a $1M Xprize competition for the future of experimentation technology in education. They've received over $2M in grant funding, enabling interventions impacting over 500 000 people.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů