Development of Multimodal Generative AI Models for Adaptive Education Personalization in the Era of Quantum Machine Learning - Amin, Muhammad; Rizal, Chairul; Muslem R, Imam (Cattleya Darmaya Fortuna, 2025)
The rapid advancement of Artificial Intelligence (AI) has transformed the educational landscape, making it increasingly crucial to develop adaptive and personalized learning systems. This study explores the development of a multimodal Generative AI model designed for adaptive educational personalization, enhanced by Quantum Machine Learning (QML). The model integrates various data types, including text, images, and voice, to create customized learning content tailored to individual student needs and learning styles. By combining the power of generative AI with quantum-inspired optimization techniques, this model aims to offer a more responsive and efficient learning experience. The research employs a mixed-methods approach, combining both quantitative and qualitative data to evaluate the effectiveness of the model in improving learning outcomes. The findings suggest that this hybrid approach holds significant potential for revolutionizing adaptive education, especially in resource-limited environments, and aligns with current educational trends such as the Merdeka Curriculum in Indonesia. The study concludes by highlighting the impact of quantum machine learning in enhancing personalization and overcoming the challenges posed by traditional educational models.