Integration of Generative Artificial Intelligence in Active Teaching Methodologies

Abstract

The study examined how generative artificial intelligence was incorporated into university teaching at a higher-education institution, with the aim of elucidating its pedagogical repercussions and underpinning strategic guidelines. It addressed the need to modernise the educational experience through automated tutoring, instantaneous feedback and the collaborative creation of multimodal materials. A convergent mixed-methods design linked classroom observation, digital artefact review and reflective interviews, thereby ensuring a holistic understanding of the phenomenon. Ethical protocols were applied, confidentiality was preserved, and rigorous standards were observed to guarantee methodological reproducibility. The technological integration was characterised by task personalisation, metacognitive mediation and dynamic coordination between human resources and algorithms. The findings revealed more active student participation, strengthened autonomy and a favourable attitude toward creative content exploration, while lecturers reported time savings in assessment processes and enhanced opportunities for formative feedback. Challenges emerged concerning the reliability of automated responses, dependence on connectivity and the need for critical digital literacy to prevent superficial or unethical uses. The research concluded that generative artificial intelligence acts as a catalyst for teaching innovation when aligned with clear curricular projects, institutional support and shared-responsibility frameworks. It recommended expanding longitudinal studies, developing culturally sensitive multilingual models and designing governance strategies that ensure inclusion, sustainability and equity in the adoption of this emerging technology within the contemporary, emerging Latin American region.