Use of artificial intelligence in university education: understanding through epistemic filters in the process of reducing qualitative evidence

Abstract

The purpose of this article was to understand the perception of the use of AI in university education. Methodologically, it was developed from the interpretivist paradigm, qualitative methodological perspective, and the hermeneutic phenomenology method of Heidegger and Gadamer. It involved 32 epistemic companions who participated in in-depth interviews, focus groups, and unstructured surveys. The analysis was carried out using the technique: epistemic filters in the process of reducing qualitative evidence, which revealed 23 semantic domains belonging to 5 semantic fields that facilitated the understanding of the central category: Use of AI. Among the results, the emerging theorization called AI Sensors in University Education was presented. Finally, the following reflection was reached: university professors recognize the broad contributions that AI has in the field of education, as they are aware that the way of teaching and learning has changed. However, they appeal to requirements that arise to avoid possible risks, focusing on respect for copyright, privacy, and data security, the use and validation of various sources, corroborating AI-generated responses, and maintaining a permanent questioning attitude.