AIGC Using Behavior Analysis from the Perspective of Artificial Intelligence Literacy - Yuhong CUI, Jintao ZHAO (Nongye tushu qingbao xuebao)
[Purpose/Significance] The development of artificial intelligence generated content (AIGC) technology has engendered novel prospects for the establishment of creating inclusive and expansive learning environments. In light of the potential risks associated with the misuse of AIGC tools, the present study analyzes the factors influencing students' use of AIGC tools within the context of artificial intelligence literacy. It constructs a conceptual model framework and explores the relational paths among influencing variables, aiming to provide a theoretical basis for the advancement of AI literacy education in libraries and other educational institutions. [Method/Process] This study adopts a mixed-method approach that primarily integrates Structural Equation Modeling (SEM) and mediation analysis to explore the relationships between the factors that influence AIGC tool usage. A conceptual relationship model was constructed based on the Technology Acceptance Model (TAM), which is widely utilized model for assessing users' acceptance of new technologies. The study builds on this model by adding AI literacy as a key variable to examine its moderating role in shaping the students' use of AIGC tools. The data were collected via a survey disseminated to university students who have used AIGC tools. The survey incorporated a series of inquiries designed to assess constructs such as effort expectancy, performance expectancy, behavioral intention, AI literacy, and actual usage of the tools. The SEM approach was employed to assess the proposed hypotheses and to validate the relationships between the identified factors. Mediation analysis was employed to assess indirect effects between variables. [Results/Conclusions] The findings indicate that effort expectancy exerts a direct impact on the actual use of AIGC tools by students, and indirectly promotes usage behavior through performance expectancy and behavioral intention. Furthermore, AI literacy plays a crucial role in improving the conversion rate from intention to actual usage. Specifically, AI literacy significantly enhances students' acceptance of AIGC tools, especially in terms of increasing their practical ability to use these tools effectively. The research also identifies key factors that influence students' use of AIGC tools, such as performance expectancy, effort expectancy, and behavioral intention, and highlights the significant moderating effect of AI literacy on the relationships among these factors. This study provides empirical evidence for the effective integration of AIGC technology into the education sector and offers theoretical guidance for libraries and educational organizations on how to design AI literacy education programs that help students adapt to a digitally driven society. Future research may encompass a more extensive examination of the utilization of AIGC tools across different academic disciplines, with a particular emphasis on their implementation in specialized domains. Additionally, the proposed model may be refined to better accommodate a wider range of educational contexts and learning scenarios.