Pedagogical framework for deep mathematical learning with generative artificial intelligence in engineering education (#1607)

Authors

Quiroz-Chavil, Helga

Capuñay-Uceda, Carlos

Nauca Torres, Enrique

Capuñay-Uceda, Oscar

Abstract

The rapid incorporation of generative artificial intelligence into higher education has opened up new possibilities for teaching mathematics in engineering programs; however, its unstructured use poses risks associated with superficial learning, cognitive dependency, and lack of conceptual transfer. Despite the growing volume of studies on generative AI in education, there remains a gap in terms of explicit pedagogical frameworks that guide its integration toward deep mathematical learning. In this context, the present study aims to design and conceptually ground a pedagogical framework to promote deep mathematical learning through the use of generative artificial intelligence in engineering education. The research adopts a qualitative approach with a non-experimental theoretical-conceptual design, based on an integrative synthesis and critical analysis of recent scientific literature. As a result, a framework structured in five interrelated layers is proposed: deep mathematical learning intentions, pedagogical roles of generative AI, human-AI interaction patterns, didactic sequences oriented towards deep reasoning, and criteria for authentic assessment and ethical management. The framework emphasizes the role of AI as a regulated cognitive mediator, subordinate to pedagogical and metacognitive principles. It is concluded that the proposal constitutes a solid conceptual basis for guiding future research aimed at the implementation and empirical validation of the pedagogical use of generative AI in the teaching of mathematics in engineering.