AI Education for Young Children: Considerations for Designing an AI Curriculum and Strategies in Early Childhood Education

• In Event: Exploring the Potential of Artificial Intelligence in Early Childhood Education

Abstract

1. Objectives or Purposes
This theoretical paper presents the co-design process of an AI curriculum platform for early childhood education. Using a design-based research (DBR) framework, a university researcher collaborated with four early childhood educators (two pre-K and two kindergarten teachers) to design a curriculum aimed at introducing artificial intelligence (AI) concepts to young children in developmentally appropriate ways.

2. Conceptual or Theoretical Framework
The curriculum is grounded in multiple theoretical foundations. Developmentally Appropriate Practice (DAP) (NAEYC, 2020) provides the pedagogical base, ensuring alignment with children’s age, individual needs, and cultural backgrounds. The DBR approach (Brown, 1992; Cobb et al., 2003) structured the iterative, collaborative process of curriculum development in authentic classroom contexts. Computational Thinking (CT) (Bers, 2018; Wing, 2006) informed the cognitive goals of the curriculum, focusing on skills such as decomposition, abstraction, and pattern recognition. The AI4K12 Initiative’s “Five Big Ideas in AI”—Perception, Representation and Reasoning, Learning, Natural Interaction, and Societal Impact—served as key conceptual anchors. KinderLab Robotics’ (2023) early childhood-friendly framework, AI follows instructions, is not alive, and helps people was used translating complex AI ideas into developmentally appropriate learning goals.

3. Methods, Techniques, or Modes of Inquiry
Following DBR methodology, the project began with a systematic literature review to identify gaps in early childhood AI education, including its goals and pedagogical approaches. Four teachers were recruited to participate in the co-design process. They first attended interactive sessions to build foundational knowledge in AI, DBR, and existing curriculum gaps. Over six structured four-hour meetings, the teachers and researcher collaboratively developed the initial curriculum. This was followed by a two-week implementation phase in classrooms, after which two refinement sessions were held to revise the platform based on teachers’ feedback and classroom experiences.

4. Data Sources, Evidence, Objects, or Materials
As a theoretical and methodological paper, data sources include conceptual and design artifacts: video recordings of co-design sessions, field notes, collaborative planning documents, and resources used during literature review. These materials documented key pedagogical choices, teacher input, and theoretical alignment that shaped the curriculum.

5. Results and/or Substantiated Conclusions
The co-design process resulted in a curriculum platform featuring both unplugged and plugged AI activities for preschool and kindergarten classrooms. Activities and objectives align with AI4K12, KinderLab, DAP, and AI literacy principles. Teachers successfully adapted the curriculum to meet their students’ developmental needs and shared their refinements during follow-up sessions. These iterations reflected their ownership and creativity, while also enhancing their professional learning and capacity to integrate innovative AI instruction.

6. Scientific or Scholarly Significance
This study contributes to the growing field of early childhood AI education by providing a replicable curriculum model and collaborative design process. It offers theoretical insights into introducing AI concepts in developmentally appropriate ways and supports alignment with NAEYC and AI4K12 standards. The curriculum serves as a foundation for future research, practice, and policy in early AI literacy education.

Author

• Joohi Lee, University of Texas - Arlington