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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

AAAI 2025 · Large language models › AI and society · approach: Teacher needs and AI curricula · 2 citations (OpenAlex)

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Content-analyzes 47 AI/ML-related Hour of Code activities for middle and high school youth against the five big ideas of AI, ML aspects, societal impact, and instructional approach.

Problem

Whether short introductory Hour of Code activities adequately teach AI and ML to K-12 youth, in terms of the five big ideas of AI, ML concepts, societal impact, and hands-on engagement.

Approach

Applies the Touretzky et al. five big ideas framework and five ML aspects as deductive coding categories to a census of HoC AI-labeled activities, adding inductive coding of societal impact topics and instructional modality (telling vs. hands-on).

Key results

• Of 37 activities with at least one big idea (28 HoC-labeled, 9 added by authors), perception was in 83.33%, learning 75%, natural interaction 41.67%, societal impact 41.67%, and representation and reasoning 13.89%.
• Only 2 of the activities integrated all five big ideas: 'Data Literacy in the AI Era Workshop' and 'Build Your Own Chatbot in Python'.
• AI-related HoC activities grew from 6 in 2021 to 47 by April 2024, while only 38 of 557 beginner activities (6.82%) were labeled AI by HoC.

Surprising findings

• Many activities labeled as AI by Hour of Code did not engage with any of the five big ideas, e.g., a generative art activity with no AI/ML content.
• Instructional approach was dominated by telling (videos, teacher explanations) rather than hands-on doing, which the authors find surprising given computing education evidence on designing applications.

Limitations

• Content analysis of activity descriptions and completion by researchers, not observation of classroom implementation with students.
• Small sample of 47 activities; coding prioritized unanimous consensus over inter-rater reliability.
• Activity set changed over time (Dec 2023 vs April 2024 snapshots), and some activities were excluded due to broken links.

Experimental setup

Datasets: Hour of Code beginner activities for middle and high school (AI-related subset, n=47)

Metrics: percentage of activities addressing each big idea count of activities per ML aspect hands-on vs telling classification

Builds on: five big ideas of AI (Touretzky et al.) Hour of Code conceptual, creative, critical framework

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