Pedagogical framework for deep mathematical learning with generative artificial intelligence in engineering education

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.