Jason Yip’s research is interdisciplinary and collaborative, intersecting human-computer interaction, learning sciences, and information sciences. His research focuses on how digital technologies support collaborative learning in children and families. Prior research demonstrates that children’s joint media engagement around digital technologies with their families can play a positive role in learning. He investigates how to build family-based collaborative learning technologies, develop new methods for design, and examining how families utilize current technologies for learning together. One major change in technology is artificial intelligence (AI)’s rapid integration into everyday technologies and AI’s effect on family learning. Jason Yip’s team will examine AI literacy, that is, the skills and necessary tools that will help people successfully navigate the present and future landscape of AI.
My plans for the fellowship period
Our team will advance Cognimates, the first open-source platform of AI education for families & children (ages 6–14). Partnering with Stefania Druga (the original developer), Cognimates allows users to train custom machine learning models with images/text to teach computers to play games and recognize specific text/images. During the fellowship period, our team aims to achieve three goals.
DEVELOPMENT. We will advance the Cognimates platform with KidsTeam UW, a team of adults and children (ages 7-11), to support smartphone integration, online/offline versions, and resource development. Data scientist Benjamin Mako Hill will develop the data analytics features to examine AI literacy for a larger scale.
DEPLOYMENT. To examine AI literacy internationally, our team will deploy Cognimates to different regions. Kids Code Jeunesse will make Cognimates accessible across Canada into elementary schools for teachers and students and their 730 Code Clubs in community centers. David Moinina Sengeh (education minister) will partner with us to deploy Cognimates to Sierra Leone. Michael Preston (CS4All) will support online distribution to networks in Europe and Asia.
ANALYSIS. Our team will use mixed methods to interpret AI literacy. Qualitative data includes artifact analysis, interviews, video/screen recordings, learner documentation, and surveys pre/post usage of the platform. The quantitative analysis focuses on online natural experiments, learning analytics, social network analysis, and public data sets creation.
How will my work change children’s and youth’s lives?
Integrating AI literacy into family collaborative learning processes is timely, as there are over 100M Amazon Echo devices that have been sold throughout the world, with advanced AI embedded in the design. Despite the popularity of AI embedded consumer devices, people (e.g., families, policymakers, designers) have little understanding of how AI plays a role in these ubiquitous devices and what considerations families need to take into account. Without AI literacy, families (particularly historically marginalized groups) risk falling prey to misinformation, fear, and not taking advantage of future potentials for learning.
Our research aims at ages 6-14, a critical time period in which digital literacies are developing with families. Our focus is on children because they are a gateway for their families to better understand AI literacy. Our research aims to provide new generations growing up with AI with the tools and literacy skills needed to better integrate AI into families learning together. With this research, we also want to examine how current design patterns both support and limit how families develop AI literacy. We need to develop curriculum, digital learning tools, and education policies that help introduce these literacies to children and their families. We believe that this research can support policymakers and designers develop best practices on how to introduce AI literacy to the larger population of children and families internationally.