HORIZON-CL2-2027-01-TRANSFO-05: The effective use of artificial intelligence in learning environments in pre-primary and primary education

Evaluating artificial intelligence in pre-primary and primary education requires empirical data regarding how digital tools alter classroom dynamics and child behavior. Working through the frameworks of technology sociology, AI sociology, and educational sociology, my involvement focuses on designing field studies, selecting samples, and gathering data on human-machine interactions within early learning environments.

From the perspective of AI and technology sociology, I look at how prompt-based communication and automated systems affect daily language, social habits, and well-being. Using educational sociology, I analyze how AI tools change the traditional roles of teachers and pupils, and how these technologies affect equity and social integration within schools. The focus is on collecting concrete field data to understand how tools like tablets or intelligent software impact student attention spans, motivation, and concentration.

I can contribute to the operational and research tasks of a consortium through:

• Data Collection & Sampling: Organizing and executing data collection inside early education environments, using general surveys and experience sampling methods to capture daily interactions with AI platforms.

• Qualitative & Ethical Analysis: Conducting semi-structured interviews and focus groups with teachers, parents, and children (processed through NVIVO/MAXQDA) to analyze the ethical dimensions, potential biases, and socio-emotional risks of AI systems.

• Teacher Profile Mapping: Collecting and analyzing empirical data to map the profiles of educators using AI, focusing on their usage motivations, perceived gratifications, and pedagogical challenges.

• Processing Field Data: Translating sociological field data into practical insights to help educators and policymakers understand the social impacts of child-AI interactions.