AI-Play: A Framework for Teaching Artificial Intelligence Concepts through Unplugged Activities in Early Childhood Education

Synthesis: Malallah and colleagues (2026) introduce AI-Play, a play-based, unplugged framework for teaching AI concepts to young children (Pre-K–K2), responding to a gap in developmentally grounded AI literacy guidance for early childhood. Synthesizing widely referenced standards and frameworks (AI4K12 Five Big Ideas, Long & Magerko, Digital Promise, CSTA, UNESCO, OECD, EU, TeachAI), they organize AI literacy into four play-based components — AI Body (AI as a system built from parts), AI Food (AI learns from examples), AI Brain (AI improves through patterns and feedback), and a Pre/Post-AI ethical lens emphasizing human responsibility, fairness, and agency. Implemented through Hour of Code family activities, child reflections and parent surveys indicated high engagement and emerging understanding that AI learns from examples, demonstrating the framework's translation into practical, developmentally appropriate lessons for non-technical educators and families.

The gap: developmentally grounded AI literacy for early childhood

AI is increasingly present in young children's lives, yet most AI literacy guidance targets older students and relies on technical approaches unsuitable for early childhood. An exploratory review of 145 papers and documents (reduced to 23 key sources) confirmed that recurring AI literacy frameworks — AI4K12's Five Big Ideas (Perception, Representation & Reasoning, Learning, Natural Interaction, Societal Impact), Long & Magerko's competency framework, Digital Promise, CSTA K-12 CS Standards, UNESCO, OECD, European Commission, TeachAI, and the RITEC child-wellbeing framework — describe what learners should understand but provide little developmentally appropriate guidance for how to introduce these ideas through play to Pre-K–K2 learners.

The AI-Play framework

AI-Play reorganizes AI literacy into four play-based, developmentally appropriate components:

• AI Body — AI is a system built from parts (sensors, data, processors), helping children see AI as constructed rather than magical.
• AI Food — AI learns from examples (data), teaching children that AI is trained rather than inherently knowing.
• AI Brain — AI improves through patterns and feedback, connecting to how AI learns and adapts.
• Pre/Post-AI ethical lens — a cross-cutting emphasis on human responsibility, fairness, and agency in relation to AI.

The framework was developed through an iterative five-stage process: exploratory investigation of the field, synthesis and grouping of recurring competencies, framework construction, expert validation with early childhood educational technologists, and implementation and evaluation. It functions as a conceptual and pedagogical bridge that translates AI literacy competencies into early-childhood learning progressions and teacher moves, accessible to non-technical educators and families.

Implementation and evaluation

AI-Play was implemented through a family-centered Hour of Code event using unplugged activities. Parent surveys and child reflection sheets examined engagement, perceived learning, and usability of the activities for at-home replication. Results indicated high engagement and emerging understanding that AI learns from examples, with positive feedback demonstrating that the framework can be translated into practical, play-based lessons that remain accessible to non-technical educators and families.

Three contributions

1. Synthesizes recurring AI literacy competencies from widely referenced standards and identifies a persistent lack of developmentally grounded guidance for Pre-K–K2 learners.
2. Presents AI-Play as a conceptual and pedagogical bridge translating those competencies into early-childhood learning progressions and teacher moves.
3. Reports an initial implementation and evaluation through a family-centered Hour of Code event.

Implications

• Early childhood deserves its own AI literacy pedagogy: unplugged, play-based activities can make AI concepts developmentally appropriate, building on Game Based Learning and learning theory.
• Ethics from the start: embedding a Pre/Post-AI ethical lens (responsibility, fairness, agency) introduces ethical AI use alongside foundational understanding.
• Accessible to families and non-technical educators: the framework lowers the barrier to AI literacy beyond formal classrooms, extending the reach of K 12 AI education and connecting to Computational Thinking and CS Education.

Connected Concepts

• Early Childhood Elementary AI Education

• AI Literacy

• K 12

• Computational Thinking

• Game Based Learning

• CS Education

• Learning Theories

• AI Education

• Equity In AI Education

Connected Articles

• Aaai2026 Prompting Literacy K12 — Teaching Responsible Use of AI Chatbots to K-12 Students
• AI Literacy Power Knowledge — AI Literacy: An Exercise in Power-Knowledge
• Posthumanist AI Literacy 2025 — A Posthumanist Approach to AI Literacy
• Community Centered AI Education Adults — Co-Designing Community-Centered AI Education for Adults
• Computational Thinking AI Agent Creation — Computational Thinking Development in AI Agent Creation

Citation

Malallah, S. A., Shamir, L., David, A. S., Weese, J. L., Feldhausen, R., Bean, N., Osiobe, E., & James, F. E. (2026). AI-Play: A Framework for Teaching Artificial Intelligence Concepts through Unplugged Activities in Early Childhood Education. ASEE Annual Conference & Exposition, Paper ID #51553.