abstract
- This research analyzes the impact of contextualization on the motivation and academic performance of second-semester architecture probability and statistics students. Leveraging recent advances in artificial intelligence (AI), the research explores how personalized educational content can improve learning experiences and outcomes. Using a Likert-type survey and academic performance tests, a quasi-experiment with a quantitative approach was carried out at a private university in the state of Puebla in Mexico. During the second half of the course, students were taught and assessed with examples and problems contextualized in the architectural environment using AI instead of the texts in the book that were used during the first half. The findings reveal a significant improvement in student motivation towards the subject, though no substantial increase in academic performance was observed. These results indicate that AI-powered contextualization personalizes learning and significantly enhances student motivation, though it does not substantially impact academic performance. This suggests that AI tools tailoring educational content can be effectively implemented as supplementary resources in mathematics and statistics courses, providing students with more meaningful and engaging learning experiences. This study contributes to the literature on educational technology and offers practical implications for integrating AI into pedagogical practices. © 2025 IEEE.