Alfabetización digital y ética en el uso de la inteligencia artificial generativa: percepciones estudiantiles y desafíos académicos en una universidad mexicana
Introduction: This study explored university students' perceptions of the use of Generative Artificial Intelligence and the digital and ethical competencies required in response to the excessive use of this tool, which poses educational challenges. Methods: This paper employed a qualitative methodology based on hermeneutics, analyzing 58 texts written by undergraduate students (17 men and 41 women). The methodological validation was grounded in the objective hermeneutics model, which includes theoretical-empirical validation, levels of validation, and criteria for contextual applicability. Results: Students expressed concern about the loss of research skills, particularly critical thinking, source evaluation, and, most notably, the ethical management of digital technologies to prevent plagiarism and foster meaningful learning. They acknowledged the need for digital literacy that goes beyond technical skills, emphasizing the importance of incorporating digital literacy into the university curriculum to promote intellectual virtues such as curiosity and critical thinking. They also highlighted the need for clear institutional policies to regulate the use of generative artificial intelligence in higher education settings, with the aim of enhancing meaningful learning. Conclusions: This study provides empirical evidence from a humanistic perspective, contributing to the development of educational strategies and public policies that address the challenges posed by generative artificial intelligence in university contexts.
Authors
- Manuel Alejandro Gutiérrez González (ORCID: https://orcid.org/0000-0002-0799-5421)
- Andrés Ocádiz Amador (ORCID: https://orcid.org/0009-0007-4354-9561)
Institutions
- Universidad Anáhuac Querétaro (MX)
Publication Details
- Journal
- Espergesia
- Published
- 2025-06-06
- DOI
- https://doi.org/10.18050/rev.espergesia.v12i1.3503
- Primary Topic
- E-Learning and Knowledge Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00