Enhancing the Continuing Education of Science Teachers with Unplugged Machine Learning Activities

Aligned with the principles of justice, peace, and sustainable development, Artificial Intelligence (AI) has the potential to make learning more equitable, fair, accessible, and inclusive. To achieve this, teachers need specialized training and low-cost, easily accessible educational resources that facilitate their incorporation into pedagogical practice. This article aims to contribute to this goal, specifically in the context of natural science education. To this end, it presents the design, implementation, and evaluation of a proposal for educational activities intended to enhance the teaching strategies of natural science teachers by incorporating Supervised Machine Learning. We evaluated the activities in two workshops involving a total of 56 science teachers; the first workshop was conducted online, and the second was held in person. During the evaluation, we examined changes in teachers' self-perception through surveys, with assessments conducted at the beginning and end of both workshops. The results highlight significant improvements in the science teachers' perceptions in key areas, such as knowledge about Machine Learning, the selection of resources to support their teaching, and more positive attitudes towards integrating Machine Learning in the science classroom. Challenges related to the conceptualization and application of Machine Learning in the educational environment were also identified. This study underscores the need for additional support and specific preparation to overcome digital gaps in the adoption of AI in multidisciplinary education. The findings are discussed in light of recent professional development trends based on AI teacher training strategies.