The Effectiveness of Deep Learning Modules Based on Artificial Intelligence on the Mathematical Logic Understanding of Vocational High School Students
Abstract
Students at vocational high schools (SMK) demonstrate low understanding of mathematical logic due to conventional, procedural instruction that lacks adaptive technology. Yet, logical reasoning is essential for programming and problem-solving in technical fields. This study developed and tested a deep-learning module powered by Artificial Intelligence (AI) to improve SMK students’ mastery of mathematical logic. The module delivers adaptive content, automatic feedback, and real-time error-pattern analysis. A quasi-experimental pretest–posttest control-group design was employed with 60 Grade-10 SMK students divided into an experimental group (AI module) and a control group (regular teaching). The instrument was a 20-item test (essay and reasoned multiple-choice) validated and reliable (α > 0.70). Data were analyzed using an independent-samples t-test. Results showed a significant gain in the experimental group (N-gain = 0.62, high category) versus the control group (N-gain = 0.38, medium category). Statistical tests confirmed a significant difference (p < 0.05), indicating the AI-based module effectively enhanced understanding of mathematical logic. the AI-driven deep-learning module is effective for teaching mathematical logic in SMK, supporting the development of adaptive learning technologies aligned with twenty-first-century vocational education needs.
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