Generative artificial intelligence and algorithmic problem-solving: A comparative analysis of Gemini, Socratic, and Photomath in university-level mathematics education
DOI:
https://doi.org/10.53485/ret.v5i3.778Keywords:
Artificial intelligence, mathematics education, LLMAbstract
The advancement of emerging digital technologies has transformed the didactics of exact sciences. The objective of this article is to comparatively analyze the impact of generative artificial intelligence (Gemini) versus algorithmic resolution applications (Photomath and Socratic) in the teaching and learning process of mathematics at the university level. The methodology used under a mixed approach and explanatory sequential design, included a sample of 60 students taking the Mathematics I, II and III courses. In the quantitative phase, the precision and procedural efficacy of the tools in differential and integral calculus problems were measured, while the qualitative phase evaluated the development of the students' heuristic reasoning. The results reveal that, although algorithmic solvers offer high efficacy in procedural numerical assessment, they generate mechanistic dependence. In contrast, Gemini acts as a cognitive orchestrator that, through dialogue, facilitates the epistemological transition from instruction to mathematical self-discovery (PAH Continuum). It is concluded that the integration of Large Language Models (LLM) in higher mathematical education overcomes the paradigm of the simple immediate result, demanding a restructuring of assessment strategies and fostering an analytical, critical learning adapted to contemporary virtual environments.
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