Students’ Attitudes toward a Didactic Experience Mediated by Generative Artificial Intelligence in the Learning of Rational Functions

Abstract

The use of emerging technologies in mathematics education represents a valuable opportunity to strengthen students’ conceptual understanding of complex structures. This article analyzes a didactic experience of guided autonomous learning mediated by generative artificial intelligence (GAI), focused on the structural understanding of asymptotes in rational functions. The activity was designed with the aim of enabling students to deduce classification rules for vertical, horizontal, and oblique asymptotes through graphical exploration, strategic question formulation, and guided interaction with an AI system, integrating numerical validation and algebraic reasoning, without initially relying on formal definitions or the direct provision of solutions.

The study adopted a quantitative, non-experimental, descriptive research design with a sample of 119 students from six groups of the General Mathematics course at the Costa Rica Institute of Technology (first semester, 2025). To assess students’ attitudes toward the experience, a 10-item Likert-type scale, psychometrically designed and validated, was administered. Results revealed a highly positive overall attitude toward the activity, with a statistically significant difference compared to the theoretical midpoint of the scale and a large effect size.

The findings suggest that generative AI, when used as a cognitive mediator rather than a substitute for the teacher, can foster guided autonomy, critical reflection, and the relational understanding of mathematical concepts. From a pedagogical and technological perspective, this experience provides insights into the design of inquiry-based activities supported by AI that promote the responsible use of these tools and strengthen metacognitive processes in university-level mathematics education.