The DOI matches its Crossref record.
Artificial Intelligence in Latin American Universities: Emerging Challenges
Abstract
The integration of artificial intelligence in Latin American universities has raised ethical challenges among faculty members. Understanding and addressing these challenges is crucial for a successful implementation of artificial intelligence in the educational context. METHOD. This study was conducted using a descriptive-explanatory quantitative approach, incorporating the opinions of 665 university professors from Latin America. Data was collected through surveys, with a Cronbach's alpha coefficient of 0.91. RESULTS . Analysis of the data revealed a range of ethical concerns among the educators regarding the utilization of artificial intelligence in education. These concerns vary in magnitude and nature, but they reflect a clear need to address and understand the ethical implications of artificial intelligence in the educational sphere. DISCUSSION. The adoption of artificial intelligence in Latin American higher education has raised ethical concerns. These concerns, while valid, should not be insurmountable barriers but points of reflection to optimize the integration of artificial intelligence in education. The study provides an essential overview for institutions, educators, and developers looking to implement AI in higher education, emphasizing the urgency of addressing these ethical challenges in an anticipatory and strategic manner.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Como destacan Fernández-Miranda et al. (2024), el acceso desigual a internet, dispositivos y alfabetización digital limita el despliegue efectivo de herramientas basadas en IA, corriendo el riesgo de reproducir -o incluso agravar -las brechas educativas existentes[18].”
From Avances, Retos Éticos y Perspectivas de la Inteligencia Artificial (IA) en la Educación de América Latina (2025) · cites this paper as [18]
Citations
Cites
Still being checked.
Cited by
Acosta-Enriquez, B. G., Huamaní Jordan, O., Morales-Angaspilco, J. E., Campoverde Ventura, G., Ruiz Carrillo, J. A., Blanco- García, L. E., & Huaranga Rivera, L. (2025) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2025.100829 · Cited by 11
Cabrera-Fuentes, A., Martínez Pérez, D. Z., & Rementeria Fuentes, J. J. (2025) · Ciencia Latina Revista Científica Multidisciplinar · doi:10.37811/cl_rcm.v9i4.18603 · Cited by 4
Metadata
- Authors
- Fernández Miranda, M., Román Acosta, D., Jurado Rosas, A. A., Limón Domínguez, D., & Torres Fernandez, C.
- Venue
- Computación y Sistemas
- Published
- 2024-06-29
- Volume
- 28
- Issue
- 2
- ISSN
- 1405-5546 (ISSN-L)
- 2007-9737
- OpenAlex
- W4402076450
- Cited by
- 47
- References
- 0
- Citations by year
- 2026: 19 · 2025: 15 · 2024: 13
- FWCI
- 17.73
- Type
- journal article
- Language
- es
- Open access
- closed
- Locations
- Computación y Sistemas (publishedVersion)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- artificial intelligence in education, AI ethics, higher education, university faculty, ethical considerations, AI adoption barriers, Latin America
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review