핀란드 고등학생의 AI 역량 인식이 위험 인식에 미치는 차이

Artificial Intelligence Competence of K-12 Students Shapes Their AI Risk Perception: A Co-occurrence Network Analysis Ville Heilala University of Jyväskylä Jyväskylä, Finland Pieta Sikström University of Jyväskylä Jyväskylä, Finland Mika Setälä University of Jyväskylä Jyväskylä, Finland Tommi Kärkkäinen University of Jyväskylä Jyväskylä, Finland Abstract As artificial intelligence (AI) becomes increasingly integrated into education, understanding how students perceive its risks is essen- tial for supporting responsible and effective adoption. This research aimed to examine the relationships between perceived AI compe- tence 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 person- alized opportunities for utilization and equitable integration of AI into K-12 education. CCS Concepts • Computing methodologies →Artificial intelligence; • Ap- plied computing →Education; • Human-centered computing →Human computer interaction (HCI). Keywords Artificial Intelligence, Competence, K-12, Education, Risk Percep- tion, Outcome Expectancy ACM Reference Format: Ville Heilala, Pieta Sikström, Mika Setälä, and Tommi Kärkkäinen. 2025. Artificial Intelligence Competence of K-12 Students Shapes Their AI Risk Perception: A Co-occurrence Network Analysis . In . ACM, New York, NY, USA, 8 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. Conference’17, Washington, DC, USA © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM. ACM ISBN 978-x-xxxx-xxxx-x/YYYY/MM https://doi.org/10.1145/nnnnnnn.nnnnnnn 1 Introduction With the rise of artificial intelligence (AI), especially generative AI (GenAI), its influence on society and education is increasingly recognized. However, the integration of artificial intelligence in education (AIED) is not straightforward; on one hand, it has been shown to improve educational outcomes [57], but, on the other hand, there are also risks that can hinder the adoption [15, 21, 43, 58]. Despite the general view of AI technologies having a positive impact on student learning, their effect particularly on students’ agency and self-regulation is understudied [18]. This has raised concerns about how the integration of AI tools affects students’ learning experiences, as well as their impact on knowledge development and skill acquisition [11, 59]. AI is not perceived solely as harmful or beneficial, but simultane- ously as both a risk and an opportunity [50]. However, perceptions and preferences regarding AI-related risks have received limited scholarly attention [61]. Thereby, the relationship between risk perceptions and individuals’ willingness to adopt AI-based applica- tions is understudied [50]. Given that AI is affecting education and how teaching will be organized [e.g., 27], it is crucial to explore further the types of risks that might hinder the adoption of AI tools in learning [53]. Research on risk perception [52], especially in the context of AI, is essential for designing effective strategies that support informed decision-making [36] and promote AI literacy [11]. Because "risk is relative to the observer” [34, p. 12], individ- ual characteristics shape which factors are perceived as risks [61]. Thus, this exploratory study aims to answer two research questions: First, which of the factors do upper secondary K-12 students per- ceive as risks? And second, how do K-12 students’ self-reported AI competence shape their perceptions of potential AI-related risks? Finland has been one of the early advocates for adopting digital education solutions and p

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