Family as a Third Space for AI Literacies: How do children and parents learn about AI together?
Open accessAbstract
Many families engage daily with artificial intelligence (AI) applications, from conversations with a voice assistant to mobile navigation searches. While there are known ways for youth to learn about AI, we do not yet understand how to engage parents in this process. To explore parents’ roles in helping their children develop AI literacies, we designed 11 learning activities organized into four topics: image classification, object recognition, interaction with voice assistants, and unplugged AI co-design. We conducted a 5-week online in-home study with 18 children (5 to 11 years old) and 16 parents. We identify parents’ most common roles in supporting their children and consider the benefits of parent-child partnerships when learning AI literacies. Finally, we discuss how our different activities supported parents’ roles and present design recommendations for future family-centered AI literacies resources.
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In HCI, there is a substantial body of research exploring how to design technologies to promote children's and families' AI literacy.
“In HCI, there is a substantial body of research exploring how to design technologies to promote children's and families' AI literacy [1, 28, 61].”
From "When He Feels Cold, He Goes to the Seahorse"—Blending Generative AI into Multimaterial Storymaking for Family Expressive Arts Therapy (2024) · cites this paper as [28]
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- Authors
- Druga, S., Christoph, F. L., & Ko, A. J.
- Affiliations
- University of Washington (US)
- University of Michigan (US)
- Venue
- CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2022-04-29
- Pages
- 1-17
- OpenAlex
- W4225000370
- Cited by
- 130
- References
- 83
- Citations by year
- 2026: 44 · 2025: 40 · 2024: 29 · 2023: 12 · 2022: 5
- FWCI
- 169.40
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Funders
- Adobe Systems
- NSF (National Science Foundation) (1539179, 1703304, 1836813, 2031265, 2100296, 2122950, 2137834, 2137312)
- Topics
- Social Sciences › Education › Child Development and Digital Technology
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Social Sciences › Arts and Humanities › Literature and Literary Theory › Literacy, Media, and Education
- Keywords
- AI literacy, family learning, parent-child partnership, voice assistants, image classification, object recognition
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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