What is the influence of psychosocial factors on artificial intelligence appropriation in college students?

• Autores: Guzmán Valle, María de los Ángeles, Acosta-Enriquez, Benicio Gonzalo, Arbulú Ballesteros, Marco, Arbulú Castillo, Julie Catherine, Arbulu Perez Vargas, Carmen Graciela, Saavedra Torres, Isaac, Silva León, Pedro Manuel, Saavedra Tirado, Karina
• Formato: artículo
• Fecha de Publicación: 2025
• Institución: Universidad Tecnológica del Perú
• Repositorio: UTP-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.utp.edu.pe:20.500.12867/14541
• Enlace del recurso: https://hdl.handle.net/20.500.12867/14541; https://doi.org/10.1186/s40359-024-02328-x
• Nivel de acceso: acceso abierto
• Materia: Artificial intelligence; Higher education; University students; Technology adoption; https://purl.org/pe-repo/ocde/ford#5.01.00

Background: In recent years, the adoption of artificial intelligence (AI) has become increasingly relevant in various sectors, including higher education. This study investigates the psychosocial factors influencing AI adoption among Peruvian university students and uses an extended UTAUT2 model to examine various constructs that may impact AI acceptance and use.  Method: This study employed a quantitative approach with a survey-based design. A total of 482 students from public and private universities in Peru participated in the research. The study utilized partial least squares structural equation modeling (PLS-SEM) to analyze the data and test the hypothesized relationships between the constructs.  Results: The findings revealed that three out of the six hypothesized factors significantly influenced AI adoption among Peruvian university students. Performance expectancy (β=0.274), social influence (β=0.355), and AI learning self-efficacy (β=0.431) were found to have significant positive effects on AI adoption. In contrast to expectations, ethical awareness, perceived playfulness, AI readiness and AI anxiety did not have significant impacts on AI appropriation in this context.  Conclusion: This study highlights the importance of practical benefits, the social context, and self-confidence in the adoption of AI within Peruvian higher education. These findings contribute to the understanding of AI adoption in diverse educational settings and provide a framework for developing effective AI implementation strategies in higher education institutions. The results can guide universities and policymakers in creating targeted approaches to enhance AI adoption and integration in academic environments, focusing on demonstrating the practical value of AI, leveraging social networks, and building students’ confidence in their ability to learn and use AI technologies.