Zhorai: Designing a Conversational Agent for Children to Explore Machine Learning Concepts


  • Phoebe Lin Harvard University
  • Jessica Van Brummelen Massachusetts Institute of Technology
  • Galit Lukin Massachusetts Institute of Technology
  • Randi Williams Massachusetts Institute of Technology
  • Cynthia Breazeal Massachusetts Institute of Technology




Understanding how machines learn is critical for children to develop useful mental models for exploring artificial intelligence (AI) and smart devices that they now frequently interact with. Although children are very familiar with having conversations with conversational agents like Siri and Alexa, children often have limited knowledge about AI and machine learning. We leverage their existing familiarity and present Zhorai, a conversational platform and curriculum designed to help young children understand how machines learn. Children ages eight to eleven train an agent through conversation and understand how the knowledge is represented using visualizations. This paper describes how we designed the curriculum and evaluated its effectiveness with 14 children in small groups. We found that the conversational aspect of the platform increased engagement during learning and the novel visualizations helped make machine knowledge understandable. As a result, we make recommendations for future iterations of Zhorai and approaches for teaching AI to children.




How to Cite

Lin, P., Van Brummelen, J., Lukin, G., Williams, R., & Breazeal, C. (2020). Zhorai: Designing a Conversational Agent for Children to Explore Machine Learning Concepts. Proceedings of the AAAI Conference on Artificial Intelligence, 34(09), 13381-13388. https://doi.org/10.1609/aaai.v34i09.7061



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