Teachers facing climate change: perceptions, approach and pedagogical tools in educational institutions in the Commune of Freire, Araucanía Region, Chile (Semiárida, 2025)
Currently, climate change is one of the most pressing global environmental challenges. Teacher training and its subsequent incorporation into all levels of education are essential for fostering environmental awareness. Effectively integrating climate change education into the curriculum raises awareness, encourages the engagement of future generations, and promotes actions for mitigation, adaptation, and climate resilience. The study analyzes the perceptions, approaches, and pedagogical tools used by rural and urban teachers regarding the issue of climate change. Through a survey, the research explores teachers’ level of knowledge, the integration of climate change topics into the school curriculum, and the pedagogical strategies applied in different educational settings. The results indicate that teachers acknowledge the existence and consequences of climate change and recognize that their students show interest in the topic. However, there is a significant gap between teacher training in environmental education and the availability of resources, which hinders the promotion of effective environmental awareness among students. Both rural and urban teachers agree that the media and social networks are their primary sources of information on climate change, raising concerns about the reliability of these digital sources. Additionally, theoretical and practical activities related to climate change are limited, highlighting the low compliance with the Learning Objectives (LO) established in the National Curriculum. For this reason, it is imperative to strengthen education on climate change in the teaching staff and to have greater pedagogical tools, which will allow teaching to be enhanced and contribute to sustainability and environmental protection.
Keywords: environmental education; sustainability; climatic alterations; educators; instruments for learning