Inteligencia Artificial en la Investigación: Replanteamiento Crítico de la Enseñanza de la Metodología Investigativa

This article provides a critical analysis of the impact of Artificial Intelligence (AI) on teaching research methodology. It identifies a significant gap between high student adoption and low integration by faculty. While AI offers notable gains in efficiency and scalability across all research phases (literature review, data analysis, writing), the analysis reveals profound risks such as algorithmic biases, erosion of critical thinking, opacity of systems ("black box"), and the potential marginalization of non-Western epistemologies.In the face of these challenges, technological determinism is rejected in favor of a human-centered integration. The conclusion emphasizes that AI's true potential lies not in replacement but in freeing the researcher from routine tasks to save time to enhance their critical and creative work. Achieving this requires teacher training in "digital criticality," institutional policies that align incentives, and an expanded ethical framework that transcends the individual to address structural and governance dimensions, ensuring that technology serves fundamental pedagogical values.