Artificial Intelligence System for Predicting University Academic Performance: A Comparative Study of Methodologies
Published 2026-09-11
Keywords
- artificial intelligence,
- academic performance,
- higher education,
- predictive models,
- machine learning
How to Cite
Abstract
Higher education faces limitations in monitoring academic performance due to the predominance of traditional methodologies, which has encouraged the incorporation of artificial intelligence (AI) as a tool to optimize educational processes. In this context, the present study aimed to design and evaluate an AI-based system to predict university students’ academic performance through the analysis of educational data and the comparison between traditional methodologies and AI-supported strategies. A quantitative study with an experimental design was conducted on a sample of 80 students divided into a control group and an experimental group. Data collection was carried out through academic assessments, digital forms, and activity records, while data processing was performed using machine learning algorithms such as logistic regression, decision trees, and neural networks. The results showed higher academic performance in the experimental group (M=8.3) compared to the control group (M=7.2), with statistically significant differences (p < 0.001), as well as higher levels of participation and use of educational resources. The predictive model achieved an accuracy of 85 %, demonstrating an adequate capacity to anticipate student performance. It is concluded that AI represents a key tool for improving academic performance and supporting data-driven pedagogical decision-making in higher education.
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References
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