Student scientific production and the 2030 Agenda: artificial intelligence-assisted lexicometric analysis in undergraduate education

Abstract

Objective:To characterize the scientific production and thematic trends of health sciences students at Lamar University (2024-2025), evaluating their alignment with the 2030 Agenda to determine the social relevance of undergraduate research.  

Methodology:A mixed-methods, descriptive, and longitudinal study. A corpus of 393 abstracts presented at the CIARTEC conference (200 in 2024 and 193 in 2025) from the degree programs of Medicine, Psychology, Nutrition, Nursing, and Physical Therapy was analyzed. The analysis was conducted using the Artificial Intelligence-Assisted Qualitative Analysis (ACAIA) model, employing NotebookLM for lexicometric systematization and cross-mapping with the official ECLAC document on the Sustainable Development Goals (SDGs). The process integrated three phases: semantic co-occurrence analysis, alignment mapping with the 17 SDGs, and identification of regional epistemological silences.  

Results:A discursive transition was identified in Medicine: from a personal-narrative approach (autoethnography) in 2024 to a clinical-epidemiological and technological one in 2025, where Artificial Intelligence emerged as a key topic. Although a robust link with SDG 3 (Good Health and Well-being, 92.1%) exists, critical "epistemological silences" were detected in priority targets for Latin America: neonatal health (2%), reproductive rights (0.5%), and total omission (0%) regarding neglected tropical diseases (Chagas/Leprosy) and international migration. Psychology showed a massive shift toward netnography, while Nursing and Nutrition maintained stable preventive and educational approaches.  

Conclusions:Student research reflects technical and scientific maturation, but a "technical blindness" toward structural problems and social determinants of global health persists. The high concentration on individual and urban pathologies, contrasted with silence on issues of social justice and regional vulnerability, suggests the need to reorient undergraduate research lines. The ACAIA model proved to be a disruptive tool for managing large volumes of data in medical education, enabling informed decision-making to enhance the social relevance of the curriculum in the face of 2030 Agenda challenges.