M{AI}L helps Greek students explore, test and reflect on Artificial Intelligence

AI and Machine Learning were introduced through hands-on activities across different subjects in two primary schools in Chania

What happens when students are given the opportunity to build and test their own Artificial Intelligence models?

In Greece, the M{AI}L project brought AI and Machine Learning into the classroom through an experiential and student-centred approach, giving students the opportunity not only to learn about AI, but also to experiment with how AI systems learn from data.

The implementation took place at the 15th and 10th Primary Schools of Chania, involving 11 teachers, 100 students and 46 classroom sessions. The activities were integrated into subjects including Greek Language, Science, History, English, Computer Science, Art and Music, demonstrating the potential for AI education to extend across different areas of the curriculum.

Exploring AI through real classroom activities

Teachers adapted the pedagogical scenarios included in the M{AI}L handbook to the age, needs and curricular objectives of their students.

A central element of the implementation was Teachable Machine, which enabled students to create, train, test and evaluate simple Machine Learning models using images, sounds and body poses.

Through these activities, students explored concepts such as data collection, classification, model training, accuracy and AI decision-making while gaining practical experience with AI technologies.

Understanding why AI sometimes gets things wrong

The activities went beyond simply making an AI model work.

Students designed datasets, trained models, tested different variables and analysed the results of their experiments. Teachers used these experiences to introduce age-appropriate discussions about data quality, accuracy, bias and ethical considerations.

This helped students understand that AI systems do not simply “know” the answer: their results depend on the data and the way models are trained.

High engagement and collaborative learning

Teachers reported high levels of student engagement and motivation throughout the implementation. The hands-on nature of the activities generated curiosity and enthusiasm, encouraging students to experiment, collaborate and improve their models.

Interestingly, teachers also observed that some students who were usually less engaged in traditional classroom activities participated more actively during the AI sessions.

At the same time, introducing AI to younger students required clear explanations, scaffolding and teacher support. Some students initially found concepts such as training data, classification and model accuracy difficult to understand, but repeated experimentation and collaboration helped them become more confident.

AI across the curriculum

The Greek experience demonstrated how AI can be introduced not only as a topic in Computer Science, but also as a learning tool across different subjects.

By combining experimentation, creativity, problem-solving and reflection, the M{AI}L activities supported students in developing digital literacy while encouraging them to think critically about both the possibilities and limitations of Artificial Intelligence.

The Greek implementation forms part of the wider M{AI}L classroom experience across Greece, Italy, Portugal and Türkiye, bringing practical AI education into real school environments.