Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Bridging the Gap: How AI Literacy Shapes Perceptions of Usefulness, Ease of Use, and Risks in AI Adoption among University Students
0
Zitationen
5
Autoren
2026
Jahr
Abstract
The rapid integration of artificial intelligence tools, especially ChatGPT, into educational settings has generated heated discussions among educators, students, and policymakers. This study explores the University students' attitudes towards the use of ChatGPT in education by examining the impact of perceived usefulness (PU), perceived ease of use (PEOU), and perceived risks (PR) on users' overall perception (OP) while AI literacy (AIL) is considered a moderating variable. Through a quantitative research design based on the Technology Acceptance Model (TAM), this study surveys the university students enrolled in different degree levels and fields of study to gain insights into their attitudes, behaviors, and concerns about the implementation of ChatGPT in educational contexts. Smart PLS (SEM) was utilized to validate the hypotheses. The research findings revealed that perceived usefulness, ease of use, and perceived risks are three major factors that have significantly changed users' overall perception of AI. Nevertheless, AI literacy was not a significant moderator of these relationships, indicating that people's perceptions are mainly determined by the features of the technology rather than the level of user literacy. The results add value to the existing literature on the application of AI in education and provide practical implications for educational institutions that aim to develop policies and guidelines for responsible use of AI tools. This research, on one hand, explores the virtual literacy component and, on the other, it delves into the functioning of the balance between technological innovation and educational integrity.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.303 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.155 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.555 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.453 Zit.