Adaptive Learning Paths: Implementing Machine Learning to Personalize Curriculum Development in Multicultural Classrooms - Rahmawati Rahmawati; Amiruddin A; Siska Armalivia (Journal of Paddisengeng Technology, 2025)
Background. The integration of technology into education has revolutionized how learning experiences are designed and delivered, especially in culturally diverse classrooms. Machine learning (ML) offers a promising approach to personalizing curriculum development by dynamically adapting content, pace, and instructional strategies based on student data. This study focuses on the application of adaptive learning paths powered by ML in multicultural classroom settings to enhance engagement, equity, and academic achievement. Purpose. This study aims to explain the effectiveness of implementing adaptive learning paths using machine learning algorithms in multicultural classrooms, particularly in enhancing personalized curriculum delivery for diverse student populations. Method. This research used a mixed-methods approach, combining quantitative analysis through SmartPLS modeling and qualitative data from teacher interviews and classroom observations. The quantitative data involved student performance records and interaction logs from 3 culturally diverse high school classrooms, while qualitative data explored the perceived relevance and inclusivity of the adaptive learning system. The path coefficients and thematic analysis were used to evaluate the impact of the adaptive system. Results. The results showed that adaptive learning paths supported by ML significantly improve student engagement and academic performance. The SmartPLS model revealed strong correlations between pedagogical constructs (Alpha), system intelligence (Beta), and outcome evaluation (Gamma). Positive responses from teachers and students also confirmed the system’s effectiveness in delivering personalized and culturally relevant learning experiences. However, the study also noted challenges related to data bias, algorithm transparency, and teacher readiness. Conclusion. Based on the findings, it can be concluded that adaptive learning paths powered by machine learning are effective in personalizing curriculum for multicultural classrooms. They promote inclusive learning by accommodating diverse student needs and cultural backgrounds, although successful implementation requires thoughtful alignment between technology, pedagogy, and context