Inclusive AI literacy for kids around the world
Open accessAbstract
We observed how 102 children (7-12 years old), from four different countries (U.S.A, Germany, Denmark, and Sweden), imagine smart devices and toys of the future and how they perceive current AI technologies. Children outside of U.S.A were overall more critical of these technologies and less exposed to them. The way children collaborated and communicated while describing their AI perception and expectations were influenced both by their social-economical and cultural background. Children in low and medium SES schools and centers were better are collaborating compared to high SES children, but had a harder time advancing because they had less experience with coding and interacting with these technologies. Children in high SES schools and centers had troubles collaborating initially but displayed a stronger understanding of AI concepts. Based on our initial findings we propose a series of guidelines for designing future hands-on learning activities with smart toys and AI devices for K8 students.
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In K-12 education, Druga et al. (2019) and Lin et al. (2021) designed learning curriculums and activities that foster AI literacy focusing on how learners gain AI concepts.
“In K-12 education, Druga et al. (2019) and Lin et al. (2021) designed learning curriculums and activities that foster AI literacy that focuses on how learners gain AI concepts.”
From Conceptualizing AI literacy: An exploratory review (2021) · cites this paper as Druga et al. (2019)
Section 8 proposes three hands-on activities for K–8 AI literacy, and Section 2.2 links such activities to children’s understanding of how AI agents work. The paper offers guidelines and activities, however, rather than a designed curriculum; it describes the activities as initial proposals requiring further testing and development. (LLM, conf=0.86)
Experimental — accuracy not yet measuredIn K-12 education, studies that involved the design of learning curricula and activities focus on how learners gain AI concepts, and how they apply AI to contexts of their interests.
“In K-12 education, studies that involved the design of learning curricula and activities focus on how learners gain AI concepts, and how they apply AI to contexts of their interests (e.g., Druga et al., 2019;Lin et al., 2021).”
From Conceptualizing AI literacy: An exploratory review (2021) · cites this paper as Druga et al., 2019
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BackgroundChi-squared and Fisher-Irwin tests of two-by-two tables with small sample recommendations
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“For example, Druga et al. found that students from outside the United States were more critical of AI compared to those from the United States, and that higher socioeconomic status (SES) students were more knowledgeable about AI than their lower SES peers [24].”
- Authors
- Druga, S., Vu, S. T., Likhith, E., & Qiu, T.
- Affiliations
- MIT Media Lab (US)
- Massachusetts Institute of Technology (US)
- Boston University (US)
- ORCID
- Druga, Stefania
- Venue
- Proceedings of FabLearn 2019
- Publisher
- ACM
- Published
- 2019-03-09
- Pages
- 104-111
- OpenAlex
- W2956066828
- Cited by
- 318
- References
- 4
- Citations by year
- 2026: 57 · 2025: 72 · 2024: 70 · 2023: 41 · 2022: 37 · 2021: 28 · 2020: 12 · 2019: 1
- FWCI
- 22.58
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- FabLearn Conference (FabLearn) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Social Sciences › Education › Child Development and Digital Technology
- Social Sciences › Gender Studies › Gender and Technology in Education
- Keywords
- AI literacy, smart toys, smart devices, socioeconomic status, cultural background, collaboration, K-8 students, hands-on learning, artificial intelligence in education, cross-cultural comparison
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