Modeling AI Dependency in Calculus Education: A Predictive Analysis for Students at Pangasinan State University

Vol. 3 No. 1 (2026): Vol. 3 No. 1 (2026): June 2026 Issue

Abstract

This study explored the level of Artificial Intelligence (AI) dependency in Calculus education among undergraduate students at Pangasinan State University (PSU), Lingayen Campus. As AI tools like ChatGPT, Photomath, and WolframAlpha become increasingly integrated into academic life, concerns arise over student overreliance on these technologies—termed AI dependency—which may undermine independent learning and critical thinking in complex subjects such as Calculus.

Guided by Social Learning Theory, Constructivist Learning Theory, Cognitive Load Theory, and the Technology Acceptance Model (TAM), this research adopted a descriptive-correlational design. It involved 54 BS Mathematics students who recently completed Calculus, selected through complete enumeration. A validated survey instrument collected data on profile variables including SHS strand, Calculus grade, screentime, number of social media accounts, number of AI tools used, and online game engagement. AI dependency was measured using a Likert-type scale. Data analysis included frequency distribution, weighted mean, and Classification and Regression Tree (CRT) analysis using SPSS.

Findings revealed a high level of AI dependency, particularly for tasks involving accuracy checks and solving complex problems. The decision tree model identified number of social media accounts, academic performance, and screentime as key predictors of AI dependency. Notably, students with more than five social media accounts exhibited lower dependency, suggesting a link between diversified digital engagement and academic autonomy.

The study highlights the dual role of AI in education—as a supportive tool and a potential crutch. It recommends AI literacy integration, critical thinking activities, and targeted interventions based on digital and academic profiles. These findings offer valuable insights for educators, curriculum developers, and policymakers in fostering responsible and balanced AI use in STEM education.