Artificial Intelligence as a Catalyst for Academic and Research Development in the University Health Sciences Community

Abstract

Research problem: Deficient integration of Generative Artificial Intelligence in health sciences presents challenges in precision, academic ethics, and absence of institutional regulatory frameworks. General objective: To analyze AI's potential as a catalyst in academic training and research development within the university health sciences community. Classification: Quantitative, descriptive, cross-sectional research with primary source through structured survey. Population and sample: 185 participants from the Health Sciences Area at Universidad Autónoma de Nayarit, predominantly Nutrition students (82.2%). Instruments: "Perception and Use of Generative Artificial Intelligence" questionnaire applied via Google Forms with 25 items. Validation: Pilot test with 30 students. Data analysis: Descriptive statistics (frequencies and percentages). Results: 75.1% regularly use generative AI, mainly for summaries (12.4%) and academic writing (11.2%), with positive perception (70.3%) but identifying limitations in precision (24.5%) and ethical concerns about plagiarism (29.6%). Conclusions: Generative AI represents a valuable tool but requires training, clear ethical frameworks, and critical supervision for responsible integration.