Influence of perceived ethics, prejudice, and teacher concerns on artificial intelligence literacy and implementation: A cross-sectional study using path analysis

This study examines the influence of perceived ethics, bias, and teacher concerns on artificial intelligence (AI) literacy and implementation in Peruvian universities. Using a quantitative correlational approach, data were collected from 422 university professors across eight institutions in northern Peru. The research employed structural equation modeling (PLS-SEM) to test a hypothetical model comprising five constructs: perceived ethics, bias in AI, teacher concerns, AI literacy, and AI implementation. The results revealed two significant relationships: AI literacy strongly influences AI implementation, and bias in AI significantly affects AI literacy. In contrast to expectations, perceived ethics and teacher concerns had no significant influence on AI literacy. Additionally, demographic variables (age and gender) had no moderating effects on the relationships between the constructs. The findings suggest that enhancing AI literacy and addressing algorithmic bias are crucial factors for successful AI integration in higher education, whereas ethical considerations and teacher concerns may play a less significant role than previously theorized. This research contributes to understanding the dynamics of AI adoption in Latin American higher education and provides practical insights for developing more effective AI implementation strategies in educational settings.