Didactic strategies with Artificial Intelligence to detect emotions in the virtual learning of indigenous students

Abstract

This research aims to analyze how teaching strategies based on artificial intelligence can detect and analyze the emotions of indigenous students, thus improving the personalization of the educational process and promoting inclusion and equity. Two instruments adapted from Márquez and Sandoval (2024) were used: one to measure students' perception of teaching strategies (12 items) and another to evaluate their emotions in virtual environments (11 items). The population studied includes 400 indigenous university students, of which a representative sample of 197 was selected through simple random sampling, guaranteeing anonymity and privacy. For data analysis, non-parametric statistics were applied, evidencing a significant correlation between perceptions of teaching strategies and students' emotions. This suggests that a positive perception of strategies fosters constructive emotions, while negative perceptions can cause frustration and demotivation.