Sesgos en la IA y educación superior. Tipologías, impactos y mitigación para la formación universitaria de calidad
DOI:
https://doi.org/10.21703/rexe.v24i55.3062Keywords:
artificial intelligence, higher education, bias, qualitiy of education, knowledge managementAbstract
This study reflects on the presence of biases in the use of artificial intelligence, its particular characteristics and the possible effects that such use would generate as a support for assisted learning activities in current university development. To do so, a documentary analysis method was used, using journals and studies specialized in the development of Generative Artificial Intelligence and its connection with current training challenges in higher education. Subsequently, the training consequences of said biases are analyzed as well as possible mitigation alternatives to focus on specific strategies so that university students consider said mechanisms when interacting in their assisted learning processes with these resources. The results expose various types of biases associated with the use of artificial intelligence and that, although documented, are in full development; as well as the consequences that this could generate in higher education. Finally, the training scope and the need to project a use of artificial intelligence that advances from a pedagogical resource to a complementary didactic to the training processes at the university are discussed, where teachers and students interact with it as a means rather than an ultimate goal of academic-professional training.
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