Artificial Intelligence in University Teaching: Opportunities and Challenges in Academic Education
DOI:
https://doi.org/10.70577/asce.v5i2.670Keywords:
Artificial Intelligence, Higher Education, University Teaching, Academic Training, Pedagogical Innovation, Grounded Theory.Abstract
The emergence of artificial intelligence in university environments rapidly transformed the conditions under which teaching was practiced and learning was produced, generating a field of tension between its pedagogical possibilities and the obstacles its implementation faced in heterogeneous institutional contexts. This article aimed to analyze the opportunities and challenges posed by the application of artificial intelligence in university teaching, with emphasis on its implications for academic training. From a qualitative methodological perspective, the study adopted the grounded theory approach proposed by Glaser and Strauss, applied to the constant comparative analysis of a corpus of 35 academic studies selected from indexed databases. The open, axial, and selective coding process allowed the emergence of five substantive categories: mediated pedagogical transformation, institutional and cultural barriers, teacher identity in transition, structural equity constraints, and ethical governance of artificial intelligence. The findings showed that the most significant opportunities—learning personalization, formative feedback, enriched assessment, and inclusive access to knowledge—could only materialize when the formative, ethical, and structural challenges that conditioned them were simultaneously addressed. The article contributes to the theoretical understanding of the phenomenon and proposes guidelines for a responsible, critical, and pedagogically grounded integration of artificial intelligence in Latin American higher education.
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