In line with the principles of peace, justice, and sustainability, Artificial Intelligence (AI) and its subfields, such as Machine Learning, have the potential to make learning more equitable, accessible, and inclusive. To achieve this, it is important to promote educational experiences from early ages that help children understand how AI works, using low-cost, easily accessible resources that are contextualized to real-world problems. This study seeks to contribute to that goal in an underexplored age group by presenting the design of a sequence of learning activities for early childhood education. The sequence combines strategies based on Computational Thinking (CT) and play, focusing on the care of endangered fish species in Chile. Methodologically, a four-stage design-based research approach was followed, including two rounds of evaluation and redesign: first, five experts assessed the content and format of the proposed activities through a closed-question survey and provided open-ended feedback on their choices; then, a second evaluation was carried out in which the activities were implemented with 15 children aged 6–8, and the sessions were recorded and photographed. Descriptive, statistical, and content analyses were conducted on the collected data. Overall, the results indicate that the experts positively validated the educational resources in terms of format and content, while also identifying difficulties related to children’s understanding of AI. Across the board, the implementation with children revealed a strong interest in the environmental issue and in its combination with CT-based activities.
Alé et al. (2026) studied this question.