SUNSPOT PREDICTION WITH A NEURAL NETWORK: AN APPLICATION OF ADVANCED ARTIFICIAL INTELLIGENCE IN SECONDARY EDUCATION

Abstract

Artificial Intelligence (AI) constitutes a central tool in contemporary education and science, as it enables the analysis of complex data, the development of predictive models, and the cultivation of 21st-century skills, such as computational thinking and critical evaluation. The integration of AI into learning environments allows students to gain an experiential understanding of how computers “learn” and adapt to data, strengthening inquiry-based learning and connecting theory with practice. Within the framework of the 5th “HERON” Summer School, implemented by the High Frequency, Metamaterials and Nonlinear Waves Research Laboratory (HERON LAB) of the University of Thessaly, Greece, the present study presents the implementation of a multilayer perceptron neural network (MLP) for predicting the number of sunspots, linking physics with computer science. Students experimented with real data, observing how the parameters of the system affect prediction accuracy and developing an understanding of both the potential and the limitations of AI in the analysis of natural phenomena.

Keywords: AI, solar observation, secondary education

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