Ethical Use of Generative Artificial Intelligence Among Ecuadorian University Students

Jorge Buele (), Ángel Ramón Sabando-García, Bosco Javier Sabando-García and Hugo Yánez-Rueda
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Jorge Buele: Centro de Investigación en Mecatrónica y Sistemas Interactivos (MIST), Facultad de Ingenierías, Universidad Tecnológica Indoamérica, Ambato 180103, Ecuador
Ángel Ramón Sabando-García: Facultad de Ciencias Sociales, Económicas y Humanitarias, Pontificia Universidad Católica del Ecuador Sede Santo Domingo, Santo Domingo de los Tsáchilas, Santo Domingo 230150, Ecuador
Bosco Javier Sabando-García: Programa de Odontología, Enfermería y Medicina, Universidad San Gregorio de Portoviejo—USGP, Portoviejo 130105, Ecuador
Hugo Yánez-Rueda: Facultad de Jurisprudencia y Ciencias Políticas, Universidad Tecnológica Indoamérica, Ambato 180103, Ecuador

Sustainability, 2025, vol. 17, issue 10, 1-16

Abstract: Artificial intelligence has transformed educational environments by facilitating processes such as information retrieval, assisted writing, automated feedback, and personalized tutoring. Within university settings, the adoption of technologies capable of autonomously generating content has increased rapidly, becoming a common academic resource for students. However, this accelerated integration poses ethical challenges, particularly when such tools are used without a clear understanding of their implications. This study aimed to examine how students’ emotional attitudes (affective), understanding (cognitive), and practical use (behavioral) of AI relate to their ethical engagement with these technologies. A structured questionnaire was administered to 833 university students in Ecuador. The instrument showed excellent internal consistency (α = 0.992; Ω = 0.992), and the validity analyses confirmed that the dimensions measured distinct but related constructs. ChatGPT was reported as the most used tool (62.2%), followed by Gemini and Siri. The structural model indicated that emotional and cognitive dimensions substantially influenced ethical behavior (β = 0.413 and β = 0.567, respectively), whereas frequent use alone exhibited no significant effect (β = −0.128; p = 0.058). These results suggest that ethical engagement with AI is primarily driven by reflection and knowledge rather than habit. This study contributes to the literature by modeling how different learning dimensions shape ethical behavior in AI use and underscores the relevance of aligning academic practices with socially responsible uses of emerging technologies.

Keywords: generative artificial intelligence; academic ethics; higher education; affective learning; digital literacy (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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