Introducing Generative Artificial Intelligence in Early Childhood Education amid Environmental Studies

pdf4 (ελληνικά)
Published: Dec 24, 2025
Updated: 2025-12-24
DOI: https://doi.org/10.12681/dial.41752
Keywords:
Generative Artificial Intelligence, early childhood education, Environmental Studies, Gemini
Καλλιόπη Κανάκη
Πανεπιστήμιο Κρήτης
https://orcid.org/0000-0001-5122-3595
Στέργιος Χατζάκης
Σύμβουλος Εκπαίδευσης Δασκάλων
Michail Kalogiannakis
Πανεπιστήμιο Κρήτης
https://orcid.org/0000-0002-9124-2245
Abstract

Exploiting Generative Artificial Intelligence in compulsory education is increasingly of interest to educators, researchers and policymakers, as it appears to contribute significantly to improving the learning process and providing personalised learning experiences. Regarding early childhood education, several studies have shown that Generative Artificial Intelligence can effectively improve teaching and learning. Given that early childhood is a critical period for children to discover the connection between Generative Artificial Intelligence applications and basic scientific concepts, we propose the use of Generative Artificial Intelligence applications in the Environmental Studies course in second grade of primary school. In this case study, a teaching intervention is presented in the Environmental Studies course in the second grade of primary school, the design and implementation of which were based on Gemini. The purpose of the study is to investigate the attitude of second graders, as well as their teachers, regarding this teaching intervention. The main research question of this case study is: “What is the attitude of students and teachers in early childhood education regarding the use of Generative Artificial Intelligence applications to support the implementation of the Environmental Studies course?” Students and teachers of the second grade of a primary school in the city of Heraklion, Crete, participated in the research process. The qualitative research methodology was adopted, applying a strong ethical framework. The data collection methods used were observation and personal semi-structured interviews. The research findings are encouraging regarding the utilization of Generative Artificial Intelligence applications to support the implementation of the Environmental Studies course, both from the perspective of students and teachers. Furthermore, the research findings are in line with the existing literature and lead to the conclusion that Generative Artificial Intelligence applications enhance the learning experience in various ways.

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