SWOT Analysis of Early Exposure to Artificial Intelligence Competencies, Illustrated by an Example of Reinforcement Learning Accessible to Lower Secondary School Students

This paper explores the process of early exposure to Artificial Intelligence (AI) competencies for students across various educational levels, focusing on its strengths, weaknesses, opportunities, and threats (SWOT). While the integration of AI into education presents both significant opportunities and challenges, its potential risks remain a critical area of ongoing research. In this contribution, we synthesize and explain findings from research on competencies, associated risks, and general experiences with AI in education at different levels, in order to develop a comprehensive SWOT analysis of the proposed process. The paper presents a lower secondary school research project as a case study, illustrating three key aspects: (1) the practical implementation of the proposed process, (2) competencies that students aged 12 to 18 can acquire through this method, and (3) the risks inherent in integrating AI into pedagogical practices. Additionally, we demonstrate the accessibility of reinforcement learning concepts to primary school students through an elementary example, showcasing how foundational AI principles can be effectively introduced at an early age. The findings highlight both the transformative potential and the challenges of equipping younger generations with essential AI competencies.