Full Text (PDF)
Abstract
This research evaluates how students’ perceptions of artificial intelligence (AI)-based learning tools affect calculus education by examining the middle factors between cognitive and emotional student responses. Research built upon cognitive load theory and social cognitive theory evaluates how students perceive AI adaptability, feedback accuracy, trust, and pedagogical value because these perceptions alter their learning processes as well as their academic outcomes. University professors from nine Rajamangala Universities of Technology in Thailand provided data through structured questionnaires to the research study. The researchers used partial least squares structural equation modeling (PLS-SEM) to evaluate their proposed theoretical connections. Results show that student perceptions regarding AI-based learning directly affect cognitive-emotional responses, and these responses powerfully determine academic achievements. The predictive model detects 75.7% of learning outcome variance in student performance (R² = 0.757), which demonstrates robust model specification and documentation. Research revealed that emotional and cognitive outcomes serve to bridge AI-based learning perceptions and educational outcomes, thereby demonstrating their importance in the learning achievement process. The research emphasizes the need to build AI learning platforms that enhance students’ engagement alongside their resilience and confidence because these elements drive academic achievement in calculus and STEM subjects.
License
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Article Type: Research Article
EURASIA J Math Sci Tech Ed, Volume 21, Issue 11, November 2025, Article No: em2738
https://doi.org/10.29333/ejmste/17390
Publication date: 09 Nov 2025
Article Views: 17
Article Downloads: 10
Open Access References How to cite this article