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

Quiroz-Chavil H.; Capuñay-Uceda C.; Nauca Torres E.; Capuñay-Uceda O. · Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology (2026) · DOI: 10.18687/laccei2026.1.1.1607

Resumen

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.

Instituciones: Universidad Nacional Pedro Ruiz Gallo; Universidad Tecnológica del Perú