Artificial Intelligence Competence of K-12 Students Shapes Their AI Risk Perception: A Co-occurrence Network Analysis

This paper surveys 163 Finnish K-12 upper secondary students about their self-perceived AI competence and concerns regarding AI risks across systemic, institutional, and personal domains, using co-occurrence network analysis to examine relationships between competence levels and risk perceptions. The study finds that lower-competence students emphasize personal risks (reduced creativity, critical thinking) while higher-competence students focus on systemic risks (bias, inaccuracy).

As artificial intelligence (AI) becomes increasingly integrated into education, understanding how students perceive its risks is essential for supporting responsible and effective adoption. This research aimed to examine the relationships between perceived AI competence and risks among Finnish K-12 upper secondary students (n = 163) by utilizing a co-occurrence analysis. Students reported their self-perceived AI competence and concerns related to AI across systemic, institutional, and personal d

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