What Can Youth Learn About Artificial Intelligence and Machine Learning in One Hour? Examining How Hour of Code Activities Address the Five Big Ideas of AI

Source publication / research team or educational organization described in paper

Not specified in extracted text

Researchers, educators, instructors, or facilitators as described in the source publication

• In-school (K-12)

Classroom, course, or resource-based AI education activity

One Hour? Examining How Hour of Code Activities Address the Five Big Ideas of AI Luis Morales-Navarro

learn- ing in everyday life has led to efforts to foster AI literacy for all K–12 students. In this paper, we review how Hour of Code activities engage with the five big ideas of AI, in par- ticular with machin; nd frame- works. While numerous AI curricula have been developed to introduce K–12 students to these ideas (Morales-Navarro and Kafai 2024), implementing these at a wider scale will require extensive teacher pre

ML concepts / supervised learning

• Use with minors requires attention to privacy, consent, data minimization, and adult supervision.

K-12

Mixed or not explicitly specified; infer from target learner group and intervention design.

Varies by intervention; not specified unless the paper explicitly describes prerequisites.

• Document the AI education intervention, course, tool, or resource described in the source publication.
• Extract the learner context, AI role, pedagogy, outcomes, and constraints for AAB registry comparison.
• The prominence of artificial intelligence and machine learn- ing in everyday life has led to efforts to foster AI literacy for all K–12 students.

• Support AAB comparison across AI literacy, AI education, teacher training, higher education, and workforce contexts.
• Capture evidence maturity, transferability, and limitations rather than treating the publication as product endorsement.

• Not an AAB endorsement of the tool, curriculum, provider, or result.
• Not a direct replication record unless the source paper reports implementation details sufficient for replication.

ML concepts / supervised learning

Not specified in extracted text

• Learning object / concept model

• Primary interaction pattern inferred from publication: Outreach / informal learning.
• AI capability focus: ML concepts / supervised learning.

• Use age-appropriate framing and teacher/facilitator oversight for any classroom deployment.

• Review the publication’s reported context, learner group, AI tool or curriculum, implementation process, and outcome evidence.
• Map the case to AAB registry fields for comparison across educational levels and AI capability types.
• Use the source publication and PDF for any manual verification before public registry release.

• Human educators/researchers remain responsible for instructional design, supervision, interpretation, and ethical safeguards.
• AI systems or AI concepts provide the learning object, support tool, evaluator, simulator, or automation context depending on the paper.

• Hands-on / experiential learning
• Registry extraction emphasizes explicit learning goals, observed outcomes, constraints, and safety limitations.

• Use with minors requires attention to privacy, consent, data minimization, and adult supervision.

• Case classified under: Published curriculum / implementation paper.
• Pedagogical pattern: Hands-on / experiential learning.
• Any additional adaptations should be verified against the full paper before public-facing publication.

• Engagement evidence should be interpreted according to the source paper’s reported method and sample.
• In this paper, we review how Hour of Code activities engage with the five big ideas of AI, in par- ticular with machine learning and societal impact.

• In this paper, we review how Hour of Code activities engage with the five big ideas of AI, in par- ticular with machine learning and societal impact.
• We found that a large majority of activities focus on perception and ma- chine learning, with little attention paid to representation and other topics.
• A surprising finding was the increased atten- tion paid to critical aspects of computing.

The prominence of artificial intelligence and machine learn- ing in everyday life has led to efforts to foster AI literacy for all K–12 students. In this paper, we review how Hour of Code activities engage with the five big ideas of AI, in par- ticular with machine learning and societal impact.

• Use age-appropriate framing and teacher/facilitator oversight for any classroom deployment.

• Activity documentation

• Can be used as an AAB evidence record for cross-case comparison, standards drafting, and evidence-maturity mapping.
• Supports identification of recurring patterns in AI literacy, AI education implementation, teacher preparation, assessment, and responsible AI learning.

• AI literacy
• Conceptual understanding
• Engagement / motivation
• Outreach / informal learning
• ML concepts / supervised learning

• Completed

K-12

In-school (K-12)

ML concepts / supervised learning

Hands-on / experiential learning

Low to Medium

Medium

What Can Youth Learn About Artificial Intelligence and Machine Learning in One Hour? Examining How Hour of Code Activities Address the Five Big Ideas of AI

• Luis Morales-Navarro
• Yasmin B. Kafai
• Eric Yang
• Asep Suryana

Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 39 No. 28, EAAI-25

2025

10.1609/aaai.v39i28.35193

https://ojs.aaai.org/index.php/AAAI/article/view/35193

https://ojs.aaai.org/index.php/AAAI/article/view/35193/37348

030_What Can Youth Learn About Artificial Intelligence and Machine Learning in One Hour.pdf

8

The prominence of artificial intelligence and machine learn- ing in everyday life has led to efforts to foster AI literacy for all K–12 students. In this paper, we review how Hour of Code activities engage with the five big ideas of AI, in par- ticular with machine learning and societal impact. We found that a large majority of activities focus on perception and ma- chine learning, with little attention paid to representation and other topics. A surprising finding was the increased atten- tion paid to critical aspects of computing. However, we also observed a limited engagement with hands-on activities. In the discussion, we address how future introductory activities could be designed to offer a broader array of topics, includ- ing the development of tools to introduce novices to artificial intelligence and machine learning and the design of more un- plugged and collaborative activities.

• In-school (K-12)

• Use with minors requires attention to privacy, consent, data minimization, and adult supervision.

Not specified in extracted text unless noted in duration field.

Requires educators/researchers/facilitators with sufficient AI literacy and pedagogy knowledge for the target learners.

Infrastructure depends on AI tool type, learner devices, data access, and institutional policy context.

High

This entry was automatically extracted from the PDF text and manifest metadata. Fields should be manually verified before public registry publication, especially group size, location, duration, and outcome claims.