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A systematic review of AI education in K–12 classrooms from 2018 to 2023: Topics, strategies, and learning outcomes
Open accessAbstract
AI education aims to teach AI concepts, essential knowledge, and skills related to the fundamental ideas in AI. As AI becomes increasingly prevalent in our daily lives, schools and educators have started to recognize the importance of AI education in K-12 schools. However, there have been a limited number of studies reporting on the implementation of AI education in classrooms. This systematic review aimed to provide an overview of the current state of AI education in K-12 schools, exploring topics, instructional approaches, and learning outcomes. Twenty-five peer-reviewed journal articles published between 2018 and 2023 were selected for this systematic review. The findings highlighted that various topics were covered in K-12 AI education, including fundamental AI concepts, different types of AI, AI applications, and ethical considerations related to AI. To facilitate meaningful learning experiences, educators frequently integrated hands-on activities and project-based learning. The findings supported the benefits of AI education in enhancing students' AI literacy, problem-solving skills, and ethical reflections on AI's societal impact. Furthermore, it fostered motivation, positive attitudes toward AI, and an interest in technology while inspiring career aspirations. It is recommended to develop tailored AI curricula, instructional strategies, and appropriate tools and resources that seamlessly integrate into various subjects within the standard school curriculum.
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“A separate stream of reviews surveys GenAI's administrative, technical, and outcome-focused uses in teaching [35-38].”
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 [37]
In the citing paper
“Past studies confirm this trend: most learners-whether in K-12 or higher education-defined AI pragmatically rather than conceptually (Casal-Otero et al., 2023;Lee et al., 2024).”
From Understanding AI Literacy Among College Students: Voices from a Historically Black College and University (2026) · cites this paper as Lee et al., 2024
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Metadata
- Authors
- Lee, S. J., & Kwon, K.
- Affiliations
- Mississippi State University (US)
- Indiana University Bloomington (US)
- Corresponding author
- Lee, Sang Joon
- Venue
- Computers and Education: Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2024-06
- Volume
- 6
- Pages
- 100211
- ISSN
- 2666-920X (ISSN-L)
- OpenAlex
- W4391792217
- Cited by
- 169
- References
- 57
- Citations by year
- 2026: 75 · 2025: 69 · 2024: 25
- FWCI
- 9.70
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computers and Education Artificial Intelligence (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- US Department of Defense
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Social Sciences › Education › Problem and Project Based Learning
- Keywords
- artificial intelligence in education, K-12 education, AI literacy, AI ethics education, project-based learning, hands-on activities, learning outcomes, student motivation, attitudes toward AI, career aspirations, AI curriculum, teaching methods
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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