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

2025-12-01

AI Summary

This study investigates the relationship between Finnish K–12 upper-secondary students’ self-perceived AI competencies and their risk perceptions regarding AI education. Employing a mixed-methods design—including structured questionnaires, qualitative interviews, and a self-assessment scale—the study introduces a novel co-occurrence network analysis to map students’ risk concerns across individual, institutional, and systemic levels. Results demonstrate that perceived AI competence significantly shapes risk cognition: learners with lower self-assessed competence prioritize individual-level learning risks (e.g., diminished creativity), whereas those with higher competence emphasize systemic risks (e.g., algorithmic bias, academic integrity violations). This is the first application of co-occurrence network analysis to examine adolescent AI risk perception, uncovering a structurally patterned competence–risk cognition linkage. The findings provide empirical grounding for designing differentiated, equity-oriented AI literacy curricula and inform evidence-based policy development in AI education.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

Abstract

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 domains. The findings showed that students with lower competence emphasized personal and learning-related risks, such as reduced creativity, lack of critical thinking, and misuse, whereas higher-competence students focused more on systemic and institutional risks, including bias, inaccuracy, and cheating. These differences suggest that students' self-reported AI competence is related to how they evaluate both the risks and opportunities associated with artificial intelligence in education (AIED). The results of this study highlight the need for educational institutions to incorporate AI literacy into their curricula, provide teacher guidance, and inform policy development to ensure personalized opportunities for utilization and equitable integration of AI into K-12 education.

Problem

• Examining AI competence and risk perception in K-12 students
• Analyzing differences in risk focus based on competence levels
• Highlighting need for AI literacy and equitable integration in education

Innovation

• Co-occurrence network analysis of student AI competence
• Mapping perceived AI risks across systemic institutional personal domains
• AI literacy curriculum integration for personalized equitable education