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

Ville Heilala, Pieta Sikström, Mika Setälä, Tommi Kärkkäinen

TL;DR

This study investigates how Finnish upper secondary students' self-perceived AI competence relates to their perceptions of AI-related risks in education. Using a co-occurrence network analysis of 14 binary risk items and a 4-item AI competence scale (n=163), the authors reveal that lower competence aligns with personal learning risks, while higher competence emphasizes systemic and institutional risks. The findings suggest that AI literacy initiatives should be integrated into curricula and supported by teacher guidance to foster equitable, critical, and creative use of AI in schools. By linking competence to risk perception, the work highlights pathways to mitigate negative outcome expectancies and support informed AI adoption in K-12 settings.

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.

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

TL;DR

This study investigates how Finnish upper secondary students' self-perceived AI competence relates to their perceptions of AI-related risks in education. Using a co-occurrence network analysis of 14 binary risk items and a 4-item AI competence scale (n=163), the authors reveal that lower competence aligns with personal learning risks, while higher competence emphasizes systemic and institutional risks. The findings suggest that AI literacy initiatives should be integrated into curricula and supported by teacher guidance to foster equitable, critical, and creative use of AI in schools. By linking competence to risk perception, the work highlights pathways to mitigate negative outcome expectancies and support informed AI adoption in K-12 settings.

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.

Paper Structure

This paper contains 12 sections, 2 equations, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Materials and methods
  4. Data collection and participants
  5. Instruments
  6. AI-related concerns and risks
  7. Artificial intelligence competence
  8. Co-occurrence network analysis
  9. Results
  10. Discussion
  11. Limitations and future research
  12. Conclusion

Figures (2)

  • Figure 1: Stacked bar chart of AI-related concerns. Bar height indicates the share of all respondents endorsing each item, segmented by competence group. Bar labels show within-item percentages and raw counts by competence group.
  • Figure 2: Co-occurrence network of AI-related concerns.