Envisioning AI for K-12: What Should Every Child Know about AI?
Open accessAbstract
The ubiquity of AI in society means the time is ripe to consider what educated 21st century digital citizens should know about this subject. In May 2018, the Association for the Advancement of Artificial Intelligence (AAAI) and the Computer Science Teachers Association (CSTA) formed a joint working group to develop national guidelines for teaching AI to K-12 students. Inspired by CSTA's national standards for K-12 computing education, the AI for K-12 guidelines will define what students in each grade band should know about artificial intelligence, machine learning, and robotics. The AI for K-12 working group is also creating an online resource directory where teachers can find AI- related videos, demos, software, and activity descriptions they can incorporate into their lesson plans. This blue sky talk invites the AI research community to reflect on the big ideas in AI that every K-12 student should know, and how we should communicate with the public about advances in AI and their future impact on society. It is a call to action for more AI researchers to become AI educators, creating resources that help teachers and students understand our work.
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?
AI literacy is multidimensional, involving not just technical know-how but also the ability to apply AI in context and reflect on its human implications.
“These efforts collectively emphasize that AI literacy is multidimensional, involving not just technical know-how but also the ability to apply AI in context and reflect on its human implications.”
From AI Literacy in the Communication Basic Course: Insights on Embedding, Motivation, and Reducing Uncertainty (2026) · cites this paper as Touretzky et al., 2019
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
No references could be resolved yet.
Research works printed but not linked: 10
- Association for the Advancement of Artificial Intelligence (AAAI). 2018. AAAI Launches 'AI for K-12' Initiative in Collaboration with the Computer Science Teachers Association (CSTA) and AI4All [Press release]. Retrieved from https://aaai.org/Pressroom/Releases/release-18-0515.php
- Baloch, H.: Crompton, H.; Gerl, M.; Harrison, S.; Law, D.; McGirt, F.; Ramos, Y.; and South, J. 2018. Artificial Intelligence Goes to School . Retrieved from https://conference.iste.org/2018/program/search/detail\_session.php ?id=110888525
- Burgsteiner, H.; Kandlhofer, M.; and Steinbauer, G. 2016. IRobot: Teaching the Basics of Artificial Intelligence in High Schools. Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence . Menlo Park, California: AAAI Press.
- Dodds, Z.; Hirsh, H.; and Wagstaff, K. 2008. AI Education Colloquium . Technical Report & AI Education Workshop Papers WS-08-02. AAAI Conference on Artificial Intelligence. AAAI Press, Menlo Park, California. https://aaai.org/Library/Workshops/ws08-02.php
- Druga, S.; Vu, S.; Likhith, E.; Oh, L.; Qui, T.; and Breazeal, C. (2018) Cognimates . https://cognimates.me Heinze, C.; Haase, J.; and Higgins, J. 2010. An Action Research Report from a Multi-Year Approach to Teaching Artificial Intelligence at the K-6 Level. First AAAI Symposium on Educational Advances in Artificial Intelligence . AAAI Press.
- International Society for Technology in Education (ISTE). 2018. Bold New Program Helps Teachers and Students Explore the Power of AI [Press release]. Retrieved from https://www.iste.org/explore/articleDetail?articleid=2229
- K-12 Computer Science Framework Steering Committee. 2016 . K12 Computer Science Framework. Technical Report. ACM, New York, NY, USA. Retrieved from https://k12cs.org/
- Resnick, M.; Bruckman, A.; and Martin, F. 1996. Pianos not stereos: Creating computational construction kits. Interactions , 3(5), 40-50.
- Touretzky, D. 2017. Computational thinking and mental models: From Kodu to Calypso. Proceedings of the 2nd IEEE Blocks & Beyond Workshop , Raleigh, NC, October 10, 2017.
- Wiggins, G., and McTighe, J. 2005. Understanding by Design . Upper Saddle River, NJ: Pearson.
Cited by
Sorted by citations
What is AI Literacy? Competencies and Design Considerations
Long, D., & Magerko, B. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376727 · Cited by 3,241 · see citing works
Conceptualizing AI literacy: An exploratory review
Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2021.100041 · Cited by 2,205 · see citing works
The impact of artificial intelligence on learner–instructor interaction in online learning
Seo, K., Tang, J., Roll, I., Fels, S., & Yoon, D. (2021) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-021-00292-9 · Cited by 788 · see citing works
AI literacy in K-12: a systematic literature review
Casal-Otero, L., Catala, A., Fernández-Morante, C., Taboada, M., Cebreiro, B., & Barro, S. (2023) · International Journal of STEM Education · doi:10.1186/s40594-023-00418-7 · Cited by 724 · see citing works
“In the U.S. we can also mention the proposal made by the Massachusetts Institute of Technology, which is an AI curriculum that aims to engage students with its social and ethical implications (Touretzky et al., 2019a).”
BackgroundMethodInfluentialArtificial intelligence literacy in higher and adult education: A scoping literature review
Laupichler, M. C., Aster, A., Schirch, J., & Raupach, T. (2022) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2022.100101 · Cited by 658 · see citing works
What are artificial intelligence literacy and competency? A comprehensive framework to support them
Chiu, T. K. F., Ahmad, Z., Ismailov, M., & Sanusi, I. T. (2024) · Computers and Education Open · doi:10.1016/j.caeo.2024.100171 · Cited by 570 · see citing works
Artificial Intelligence (AI) Literacy in Early Childhood Education: The Challenges and Opportunities
Su, J., Ng, D. T. K., & Chu, S. K. W. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100124 · Cited by 489 · see citing works
Zhang, H., Lee, I., Ali, S., DiPaola, D., Cheng, Y., & Breazeal, C. (2023) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-022-00293-3 · Cited by 396 · see citing works
Ng, D. T. K., Wu, W., Leung, J. K. L., Chiu, T. K. F., & Chu, S. K. W. (2024) · British Journal of Educational Technology · doi:10.1111/bjet.13411 · Cited by 390 · see citing works
Creation and Evaluation of a Pretertiary Artificial Intelligence (AI) Curriculum
Chiu, T. K. F., Meng, H., Chai, C.-S., King, I., Wong, S., & Yam, Y. (2022) · IEEE Transactions on Education · doi:10.1109/te.2021.3085878 · Cited by 380 · see citing works
Kong, S.-C., Man-Yin Cheung, W., & Zhang, G. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2021.100026 · Cited by 332 · see citing works
Hornberger, M., Bewersdorff, A., & Nerdel, C. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100165 · Cited by 276 · see citing works
Examining artificial intelligence literacy among pre-service teachers for future classrooms
Ayanwale, M. A., Adelana, O. P., Molefi, R. R., Adeeko, O., & Ishola, A. M. (2024) · Computers and Education Open · doi:10.1016/j.caeo.2024.100179 · Cited by 238 · see citing works
A systematic review of teaching and learning machine learning in K-12 education
Sanusi, I. T., Oyelere, S. S., Vartiainen, H., Suhonen, J., & Tukiainen, M. (2023) · Education and Information Technologies · doi:10.1007/s10639-022-11416-7 · Cited by 212 · see citing works
Pedagogical Design of K-12 Artificial Intelligence Education: A Systematic Review
Yue, M., Jong, M. S.-Y., & Dai, Y. (2022) · Sustainability · doi:10.3390/su142315620 · Cited by 176 · see citing works
Kong, S.-C., Cheung, M.-Y. W., & Tsang, O. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100214 · Cited by 167 · see citing works
Lee, S. J., & Kwon, K. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100211 · Cited by 169 · see citing works
A differentiated discussion about AI education K-12
Steinbauer, G., Kandlhofer, M., Chklovski, T., Heintz, F., & Koenig, S. (2021) · KI - Künstliche Intelligenz · doi:10.1007/s13218-021-00724-8 · Cited by 160 · see citing works
Wang, T., & Cheng, E. C. K. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2021.100031 · Cited by 141 · see citing works
Kong, S.-C., Cheung, W. M.-Y., & Zhang, G. (2022) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2022.100223 · Cited by 135 · see citing works
Integrating generative AI in knowledge building
Chen, B., Zhu, X., & Díaz del Castillo H., F. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100184 · Cited by 159 · see citing works
Teachers’ perspective on artificial intelligence education: An initial investigation
Polak, S., Schiavo, G., & Zancanaro, M. (2022) · CHI Conference on Human Factors in Computing Systems Extended Abstracts · doi:10.1145/3491101.3519866 · Cited by 132 · see citing works
Family as a Third Space for AI Literacies: How do children and parents learn about AI together?
Druga, S., Christoph, F. L., & Ko, A. J. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3502031 · Cited by 130 · see citing works
Visual tools for teaching machine learning in K-12: A ten-year systematic mapping
Gresse von Wangenheim, C., Hauck, J. C. R., Pacheco, F. S., & Bertonceli Bueno, M. F. (2021) · Education and Information Technologies · doi:10.1007/s10639-021-10570-8 · Cited by 130 · see citing works
Co-designing AI literacy exhibits for informal learning spaces
Long, D., Blunt, T., & Magerko, B. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3476034 · Cited by 128 · see citing works
Kong, S.-C., Cheung, W. M.-Y., & Tsang, O. (2023) · Education and Information Technologies · doi:10.1007/s10639-022-11408-7 · Cited by 127 · see citing works
Focusing on Teacher Education to Introduce AI in Schools: Perspectives and Illustrative Findings
Vazhayil, A., Shetty, R., Bhavani, R. R., & Akshay, N. (2019) · 2019 IEEE Tenth International Conference on Technology for Education (T4E) · doi:10.1109/t4e.2019.00021 · Cited by 131 · see citing works
Machine Learning for Human Learners: Opportunities, Issues, Tensions and Threats
Webb, M. E., Fluck, A., Magenheim, J., Malyn-Smith, J., Waters, J., Deschênes, M., & Zagami, J. (2021) · Educational Technology Research and Development · doi:10.1007/s11423-020-09858-2 · Cited by 116 · see citing works
Analyzing Teacher Competency with TPACK for K-12 AI Education
Kim, S., Jang, Y., Choi, S., Kim, W., Jung, H., Kim, S., & Kim, H. (2021) · KI - Künstliche Intelligenz · doi:10.1007/s13218-021-00731-9 · Cited by 111 · see citing works
Zhou, X., Van Brummelen, J., & Lin, P. (2020) · arXiv · doi:10.48550/arxiv.2009.10228 · Cited by 104 · see citing works
Park, H., & Ahn, D. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642785 · Cited by 84 · see citing works
Henry, J., Hernalesteen, A., & Collard, A.-S. (2021) · KI - Künstliche Intelligenz · doi:10.1007/s13218-021-00733-7 · Cited by 80 · see citing works
Exploring Generative Models with Middle School Students
Ali, S., DiPaola, D., Lee, I., Hong, J., & Breazeal, C. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445226 · Cited by 82 · see citing works
AI Literacy in K-12 and Higher Education in the Wake of Generative AI: An Integrative Review
Gu, X., & Ericson, B. J. (2025) · arXiv · doi:10.48550/arxiv.2503.00079 · Cited by 73
Zhang, H., Perry, A., & Lee, I. (2025) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-024-00398-x · Cited by 75 · see citing works
Constructing Dreams Using Generative AI
Ali, S., Ravi, P., Williams, R., DiPaola, D., & Breazeal, C. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i21.30374 · Cited by 69 · see citing works
Hsu, T.-C., Abelson, H., & Van Brummelen, J. (2022) · The International Review of Research in Open and Distributed Learning · doi:10.19173/irrodl.v22i4.5474 · Cited by 65 · see citing works
Learning About AI: A Systematic Review of Reviews on AI Literacy
Zhang, S., Ganapathy Prasad, P., & Schroeder, N. L. (2025) · Journal of Educational Computing Research · doi:10.1177/07356331251342081 · Cited by 62 · see citing works
Newman, M., Sun, K., Dalla Gasperina, I. B., Shin, G. Y., Pedraja, M. K., Kanchi, R., Song, M. B., Li, R., Lee, J. H., & Yip, J. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642492 · Cited by 59 · see citing works
Designing a novel teaching platform for <scp>AI</scp>: A case study in a Thai school context
Aung, Z. H., Sanium, S., Songsaksuppachok, C., Kusakunniran, W., Precharattana, M., Chuechote, S., Pongsanon, K., & Ritthipravat, P. (2022) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12706 · Cited by 50 · see citing works
AI-deploying organizations are key to addressing ‘perfect storm’ of AI risks
Curtis, C., Gillespie, N., & Lockey, S. (2023) · AI and Ethics · doi:10.1007/s43681-022-00163-7 · Cited by 43 · see citing works
Dangol, A., Newman, M., Wolfe, R., Lee, J. H., Kientz, J. A., Yip, J., & Pitt, C. (2024) · Designing Interactive Systems Conference · doi:10.1145/3643834.3661515 · Cited by 42 · see citing works
“…for introducing children to the societal implications of AI systems [1, 19, 20, 57, 64] through the lens of computational thinking perspectives [17, 43, 46, 90] and the fairness aspect of the model’s performance [12, 21, 50, 83], we build on prior work to investigate how children engage with…”
InfluentialChiang, C.-W., & Yin, M. (2022) · 27th International Conference on Intelligent User Interfaces · doi:10.1145/3490099.3511121 · Cited by 42 · see citing works
Three Interviews About K-12 AI Education in America, Europe, and Singapore
Heintz, F. (2021) · KI - Künstliche Intelligenz · doi:10.1007/s13218-021-00730-w · Cited by 34 · see citing works
Fostering AI Literacy with Embodiment & Creativity: From Activity Boxes to Museum Exhibits
Long, D., Rollins, S., Ali-Diaz, J., Hancock, K., Nuonsinoeun, S., Roberts, J., & Magerko, B. (2023) · Proceedings of the 22nd Annual ACM Interaction Design and Children Conference · doi:10.1145/3585088.3594495 · Cited by 29 · see citing works
Children's Mental Models of AI Reasoning: Implications for AI Literacy Education
Dangol, A., Wolfe, R., Zhao, R., Kim, J., Ramanan, T., Davis, K., & Kientz, J. A. (2025) · Proceedings of the 24th Interaction Design and Children · doi:10.1145/3713043.3728856 · Cited by 25 · see citing works
Why, What and How to Help Each Citizen to Understand Artificial Intelligence?
Alexandre, F., Becker, J., Comte, M.-H., Lagarrigue, A., Liblau, R., Romero, M., & Viéville, T. (2021) · KI - Künstliche Intelligenz · doi:10.1007/s13218-021-00725-7 · Cited by 20 · see citing works
AI literacy for teacher educators: a holistic curriculum for capacity-building in higher education
Caspari-Sadeghi, S. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1745768 · Cited by 18 · see citing works
Cao, H. J., Choi, K., Park, C., & Lee, H. R. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713173 · Cited by 9 · see citing works
Empowering Children’s AI Literacy Through Co-Creating Stories with LLM
Han, A., & Han, S. (2025) · Proceedings of the 24th Interaction Design and Children · doi:10.1145/3713043.3731520 · Cited by 8 · see citing works
Exploring the AI Topic Composition of K-12 Using NMF-based Topic Modeling
Woo, H., Kim, J., Kim, J., & Lee, W. (2020) · International Journal on Advanced Science, Engineering and Information Technology · doi:10.18517/ijaseit.10.4.12787 · Cited by 7 · see citing works
Escape or D13: Understanding Youth Perspectives of AI through Educational Game Co-design
Lim, J. O., Barkhuff, G., Awuah, J., Clyde, S., Sogani, R., Gardner-McCune, C., Touretzky, D., & Uchidiuno, J. O. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714037 · Cited by 5 · see citing works
“In 2019, Touretzky et al. [91] partnered with various AI experts to create a list of five “big ideas,” which covered fundamental topics relevant to K-12 AI education.”
InfluentialSelf-citationDefining and assessing AI literacy for researchers across the research lifecycle
Parker, J. L., & Becker, K. P. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1827603 · Cited by 5 · see citing works
Medina-Gual, L., Medina-Velázquez, L., & Parejo, J.-L. (2025) · Australasian Journal of Educational Technology · doi:10.14742/ajet.10596 · Cited by 4 · see citing works
Petricini, T. A., & Cruz, L. (2026) · Journal of Teaching and Learning with Technology · doi:10.14434/jotlt.v14i1.41702 · Cited by 1 · see citing works
Dhunpath, R. (2026) · Transformation in Higher Education · doi:10.4102/the.v11i0.800
Kim, J., Yang, G., Chan, W. S., Lin, X., Song, Y., & Li, N. (2026) · European Journal of Education · doi:10.1111/ejed.70658 · Cited by 1 · see citing works
Liu, J. P., & Levy, R. (2026) · arXiv · doi:10.48550/arxiv.2606.00038
- Authors
- Touretzky, D., Gardner-McCune, C., Martin, F., & Seehorn, D.
- Affiliations
- Carnegie Mellon University (US)
- University of Massachusetts Lowell (US)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2019-07-17
- Volume
- 33
- Issue
- 01
- Pages
- 9795-9799
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2965058023
- Cited by
- 918
- References
- 0
- Citations by year
- 2026: 197 · 2025: 201 · 2024: 216 · 2023: 117 · 2022: 83 · 2021: 71 · 2020: 26 · 2019: 7
- FWCI
- 54.13
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- www.aaai.org/ (unspecified)
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- artificial intelligence in education, K-12 education, machine learning education, robotics education, digital citizenship, public understanding of AI, teacher resources, societal impact of AI
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
