Increasing technologies and innovations are rapidly transforming our world by impacting our day-to-day activities, including the transformation of the educational sector at all tiers. Artificial Intelligence (AI) and Machine Learning (ML) are shaping the global educational landscape with important relevance for early childhood education (ECE). This module thus provides the basic foundational concepts of Machine Learning (ML) to pre-service early childhood educators. The module is mainly organized from an educational and pedagogical perspective. The module has a simple design to explore the basic conceptual framework of ML, how it can be understood, the related ethical issues, and practical relevance to early childhood education (ECE). Hence, the focus is not on advanced technicalities, mathematical complexity, or large models, but rather to stimulate ML technologies literacy, critical thinking, and pedagogical readiness among future teachers in pre-school educational settings. The contents of this module are designed in accordance with internationally recognized frameworks for AI, digital education, and teacher preparation, particularly those developed by UNESCO and the European Union. This is done to ensure ethical compliance as well as pedagogical relevance in the ECE context.

  • Context and Relevance: Rising AI and ML technologies have implications for education at all levels, including early childhood education. Pre-service teachers need a basic understanding of how these technologies function and how they shape learning, assessment, inclusion, and the general development of children.
  • Module Overview: This module provides an introductory, non-technical exploration of ML and its pedagogical implications for ECE over three weeks. The contents cover core ML concepts, main types of ML, and how ML systems learn from data. The opportunities and challenges of ML within ECE settings and the foundational principles of AI pedagogy were also introduced with a focus on practical relevance rather than technical proficiency among pre-service teachers with no or little backgrounds in computing.
  • Module Goals: The aim is to support pre-service ECE educators in building foundational ML literacy to help them understand how ML systems function. To enhance participants’ evaluation of ML-based educational tools via child-focused and developmentally appropriate principles. The module also aims to introduce AI pedagogy frameworks that support teaching experience, thereby fostering future teachers’ capacity to ethically and responsibly engage with ML technologies in ECE contexts. 

Module 1 Learning Flow

Module 1 consists of four topics and is designed to be completed over four weeks. Participants are expected to complete one topic per week, following a gradual and accessible introduction to Machine Learning, its basic concepts, and its pedagogical relevance in early childhood education.

The estimated total learning time for this module is 15 hours. Topic 1 and Topic 2 each require approximately 3 hours, Topic 3 requires approximately 4 hours, and Topic 4 requires approximately 5 hours. Each topic includes reading materials, practical tasks, reflection activities, and short structured quizzes. Learners should complete all required activities within each topic before progressing to the next part of the course.

After completing Module 1, participants will move to Module 4 and complete its first project-based lesson. This lesson is directly connected to the knowledge gained in Module 1 and forms the first step of the learner’s final project. Once the first Module 4 activity is completed and marked as done, Module 2 will become available.

1. What is Machine Learning? Core Concepts for Pre-service Teachers
2. Types of Machine Learning (Supervised, Unsupervised, and Reinforcement Learning)
3. How ML Models Work: Data, Features & Opportunities for Application in Early Childhood Education
4. General Introduction to AI Pedagogy