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1. Using Conversational Agents to Foster Young Children’s Science Learning from Screen Media

   Xu, Ying (January 2020, Proceedings of the 19th ACM International Conference on Interaction Design and Children (IDC ’20))

   My dissertation project leverages an intelligent conversational agent embodied in an on-screen character to add social contingency into children’s science video watching experiences. This conversational agent has been developed in an iterative process and embedded in a new PBS KIDS science show, "Elinor Wonders Why." My research aims to understand the design, feasibility, and promise of virtual conversation with media characters in young children’s informal science learning through video watching. 

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2. The effects of interactions with AI-enhanced media characters on learning computational thinking

   https://doi.org/10.1016/j.learninstruc.2025.102149  

   Xu, Ying; Pan, Echo Zexuan; Levine, Julian; He, Kunlei; Thomas, Trisha; Warschauer, Mark (August 2025, Learning and Instruction)

   Background Computational thinking (CT) is a crucial domain for children to develop in their early years. To increase children's access to CT learning resources, educational programs like PBS KIDS “Lyla in the Loop” have been developed to incorporate CT concepts through narrative structures where characters solve problems using the CT cycle. However, children need explicit guidance to effectively process both educational and narrative content. Engaging children in dialogues that connect educational content with the narrative has proven to enhance comprehension. Aims This study explores the effectiveness of using AI to enable this type of dialogues between children and a media character, supporting children in learning CT by connecting these concepts with everyday situations in “Lyla in the Loop.” Method Through a between-subject randomized control study with 160 children aged four to eight, we will compare children's learning and applications of CT concepts as well as narrative comprehension from AI-assisted dialogues to those who watched the broadcast version of the show without such dialogues. The study also examines the role of children's cognitive abilities and prior CT knowledge in their learning from the show, with or without AI-assisted dialogues. Expected results The findings could enhance our understanding of AI-based scaffolding strategies in children's media and offer practical implications for improving children's learning experiences. 

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3. AI Adaptivity in a Mixed-Reality System Improves Learning

   https://doi.org/10.1007/s40593-023-00388-5  

   Yannier, Nesra; Hudson, Scott E; Chang, Henry; Koedinger, Kenneth R (January 2024, International Journal of Artificial Intelligence in Education)

   Adaptivity in advanced learning technologies offer the possibility to adapt to different student backgrounds, which is difficult to do in a traditional classroom setting. However, there are mixed results on the effectiveness of adaptivity based on different implementations and contexts. In this paper, we introduce AI adaptivity in the context of a new genre of Intelligent Science Stations that bring intelligent tutoring into the physical world. Intelligent Science Stations are mixed-reality systems that bridge the physical and virtual worlds to improve children’s inquiry-based STEM learning. Automated reactive guidance is made possible by a specialized AI computer vision algorithm, providing personalized interactive feedback to children as they experiment and make discoveries in their physical environment. We report on a randomized controlled experiment where we compare learning outcomes of children interacting with the Intelligent Science Station that has task-loop adaptivity incorporated, compared to another version that provides tasks randomly without adaptivity. Our results show that adaptivity using Bayesian Knowledge Tracing in the context of a mixed-reality system leads to better learning of scientific principles, without sacrificing enjoyment. These results demonstrate benefits of adaptivity in a mixed-reality setting to improve children’s science learning. 

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4. Co-design of analogical and embodied representations with children for child-centered AI learning experiences

   https://doi.org/10.1016/j.ijhcs.2025.103462  

   Zhou, Xiaofei; Gong, Yunfan; Zhou, Yushan; Jiang, Yufei; Bai, Zhen (May 2025, International Journal of Human-Computer Studies)

   AI recommendations shape our daily decisions and our young generation is no exception. The convenience of navigating personalized content comes with the notorious ‘‘filter bubble’’ effect, which can reduce exposure to diverse options and opinions. Children are particularly vulnerable to this due to their limited AI literacy and critical thinking skills. In this study, we explore how to engage children as co-designers to create child-centered experiences for learning AI concepts related to the filter bubble. Leveraging embodied and analogical learning theories, we co-designed an Augmented Reality (AR) application, BeeTrap, with children from underrepresented backgrounds in STEM. BeeTrap not only raises awareness of filter bubbles but also empowers children to understand recommendation system mechanisms. Our contributions include (1) insights into child-centered AI learning using embodied metaphors and analogies as educational representations of AI concepts; and (2) implications for enhancing children’s understanding of AI concepts through co-design processes. 

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5. Understanding Parents’ Roles in Children’s Learning and Engagement in Informal Science Learning Sites

   https://doi.org/10.3389/fpsyg.2021.635839  

   Joy, Angelina; Law, Fidelia; McGuire, Luke; Mathews, Channing; Hartstone-Rose, Adam; Winterbottom, Mark; Rutland, Adam; Fields, Grace E.; Mulvey, Kelly Lynn (March 2021, Frontiers in Psychology)

   Informal science learning sites (ISLS) create opportunities for children to learn about science outside of the classroom. This study analyzed children’s learning behaviors in ISLS using video recordings of family visits to a zoo, children’s museum, or aquarium. Furthermore, parent behaviors, features of the exhibits and the presence of an educator were also examined in relation to children’s behaviors. Participants included 63 children (60.3% female) and 44 parents in 31 family groups. Results showed that parents’ science questions and explanations were positively related to children observing the exhibit. Parents’ science explanations were also negatively related to children’s science explanations. Furthermore, children were more likely to provide science explanations when the exhibit was not interactive. Lastly there were no differences in children’s behaviors based on whether an educator was present at the exhibit. This study provides further evidence that children’s interactions with others and their environment are important for children’s learning behaviors. 

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