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Cited by 249Artificial Intelligence in EducationTeaching and Learning ProgrammingDigital literacy in education

Artificial intelligence (AI) learning tools in K-12 education: A scoping review

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Yim, Iris Heung Yue, Su, Jiahong·2024·Journal of Computers in Education
DOI 10.1007/s40692-023-00304-9
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Abstract

Abstract Artificial intelligence (AI) literacy is a global strategic objective in education. However, little is known about how AI should be taught. In this paper, 46 studies in academic conferences and journals are reviewed to investigate pedagogical strategies, learning tools, assessment methods in AI literacy education in K-12 contexts, and students’ learning outcomes. The investigation reveals that the promotion of AI literacy education has seen significant progress in the past two decades. This highlights that intelligent agents, including Google’s Teachable Machine, Learning ML, and Machine Learning for Kids, are age-appropriate tools for AI literacy education in K-12 contexts. Kindergarten students can benefit from learning tools such as PopBots, while software devices, such as Scratch and Python, which help to develop the computational thinking of AI algorithms, can be introduced to both primary and secondary schools. The research shows that project-based, human–computer collaborative learning and play- and game-based approaches, with constructivist methodologies, have been applied frequently in AI literacy education. Cognitive, affective, and behavioral learning outcomes, course satisfaction and soft skills acquisition have been reported. The paper informs educators of appropriate learning tools, pedagogical strategies, assessment methodologies in AI literacy education, and students’ learning outcomes. Research implications and future research directions within the K-12 context are also discussed.

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  • AI research in K-12 continues to privilege traditional AI (e.g., ITS), emphasizing tool properties or AI literacy instead of tracing how AI is pedagogically enacted to enhance learning.

    “AI research in k-12 continues to privilege traditional AI (e.g., ITS) [30], emphasizing tool properties [31,32] or AI literacy [31,33,34] instead of tracing how AI is pedagogically enacted to enhance learning.”

    From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [34]

    Citation [31] is attached to the claims about tool properties and AI literacy, not the claim about traditional AI or ITS. The Abstract (page unavailable) confirms that this review examines AI learning tools in K-12 AI literacy education. But its RQ2 and Discussion and conclusion sections also examine pedagogical strategies and learning outcomes, so it does not support the implication that the research it reviews focuses on tools or literacy instead of how AI is used pedagogically. (LLM, conf=0.84)

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Cited by

Sorted by citations

Authors
Yim, I. H. Y., & Su, J.
Affiliations
  • University of Cambridge (GB)
  • University of Hong Kong (HK)
Corresponding author
Yim, Iris Heung Yue
ORCID
Venue
Journal of Computers in Education
Publisher
Springer Science and Business Media LLC
Published
2024-01-06
ISSN
  • 2197-9987 (ISSN-L)
  • 2197-9995
DOI
10.1007/s40692-023-00304-9
OpenAlex
W4390638955
Cited by
249
References
9
Citations by year
2026: 102 · 2025: 116 · 2024: 31
FWCI
93.26
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Computers in Education (publishedVersion, cc-by)
  • Apollo (University of Cambridge) (submittedVersion, cc-by-nd)
  • Apollo (University of Cambridge) (submittedVersion, cc-by)
  • Apollo
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
  • Physical Sciences › Computer Science › Information Systems › Digital literacy in education
Keywords
AI literacy, K-12 education, teaching methods, assessment methods, learning outcomes, intelligent agents, Teachable Machine, Scratch, computational thinking, project-based learning, game-based learning, constructivist learning, soft skills

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