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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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This content will become publicly available on August 1, 2026
The effects of interactions with AI-enhanced media characters on learning computational thinking
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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- Award ID(s):
- 2115382
- PAR ID:
- 10615885
- Publisher / Repository:
- Science Direct
- Date Published:
- Journal Name:
- Learning and Instruction
- Volume:
- 98
- Issue:
- C
- ISSN:
- 0959-4752
- Page Range / eLocation ID:
- 102149
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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