K-12 Education in the Age of AI: A Call to Action for K-12 AI Literacy
Open accessAbstract
The emergence of increasingly powerful AI technologies calls for the design and development of K-12 AI literacy curricula that can support students who will be entering a profoundly changed labor market. However, developing, implementing, and scaling AI literacy curricula poses significant challenges. It will be essential to develop a robust, evidence-based AI education research foundation that can inform AI literacy curriculum development. Unlike K-12 science and mathematics education, there is not currently a research foundation for K-12 AI education. In this article we provide a component-based definition of AI literacy, present the need for implementing AI literacy education across all grade bands, and argue for the creation of research programs across four areas of AI education: (1) K-12 AI Learning & Technology; (2) K-12 AI Education Integration into STEM, Language Arts, and Social Science Education; (3) K-12 AI Professional Development for Teachers and Administrators; and (4) K-12 AI Assessment.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Using a curriculum that is not grounded in research is problematic.
“as using a curriculum not grounded in research is problematic (Wang & Lester, 2023)”
From From Understanding to Creating: Bridging AI Literacy and AI Fluency in K-12 Education (2025) · cites this paper as Wang & Lester, 2023
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
Cited by 139
- Authors
- Wang, N., & Lester, J.
- Affiliations
- University of Southern California (US)
- North Carolina State University (US)
- Corresponding author
- Wang, Ning
- ORCID
- Lester, James
- Venue
- International Journal of Artificial Intelligence in Education
- Publisher
- Elsevier BV
- Published
- 2023-06
- Volume
- 33
- Issue
- 2
- Pages
- 228-232
- ISSN
- 1560-4292 (ISSN-L)
- OpenAlex
- W4382751226
- Cited by
- 139
- References
- 0
- Citations by year
- 2026: 51 · 2025: 57 · 2024: 28 · 2023: 3
- Type
- conference abstract
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International Journal of Artificial Intelligence in Education (publishedVersion, cc-by)
- Funders
- University of Southern California
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Information Systems › Digital literacy in education
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Keywords
- AI literacy, K-12 education, AI literacy curriculum, teacher professional development, AI evaluation, STEM education, language arts instruction, social studies education, labor market, curriculum development
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 ›
Are you an author, or do you see a problem with this page? Request a review
