Predictors of the ethical use of generative artificial intelligence in higher education

Abstract

Introduction Generative artificial intelligence has become increasingly integrated into higher education; however, its adoption raises challenges related to authorship, transparency, content verification, privacy, and student responsibility. The objective of this study was to identify the factors associated with and structurally contributing to the ethical use of generative artificial intelligence among students at the State University of Milagro. Methods A quantitative, applied, non-experimental, cross-sectional, correlational, explanatory-predictive, and confirmatory study was conducted. The sample comprised 980 students selected through non-probability purposive sampling. Data were collected using a 44-item questionnaire. Results The proposed model demonstrated an excellent fit (CFI = 0.998; SRMR = 0.021; RMSEA = 0.008). The six predictor constructs jointly explained 44.0% of the variance in ethical GenAI use ( R 2 = 0.440), and all structural paths were positive and statistically significant. Academic integrity and transparency exhibited the strongest effect ( β = 0.231), followed by ethical and technical literacy ( β = 0.181), critical thinking and content verification ( β = 0.177), institutional guidance and ethical education ( β = 0.159), self-regulation and academic responsibility ( β = 0.119), and data protection, privacy, and risk management ( β = 0.114). Bivariate correlations between the six predictor constructs and the ethical use of generative artificial intelligence ranged from r = 0.370 to r = 0.507 (all p < 0.001). Conclusion The ethical use of generative artificial intelligence was associated with the interaction of individual competencies, academic principles, and institutional conditions. These findings highlight the need to strengthen clear institutional policies, integrate ethics education across the curriculum, promote critical verification of AI-generated content, encourage transparent disclosure of AI use, and reinforce data protection practices. Given the cross-sectional design, the findings should be interpreted as predictive associations rather than evidence of causal relationships.