Adaptive User Interfaces for the Era of Intelligent Interaction

Abstract

This study aims to develop a cost-effective Adaptive User Interface (AUI) model to support personalized education, particularly in resource-limited contexts such as Indonesia, where accessibility and inclusivity remain major challenges. The proposed model integrates the Levenshtein distance algorithm to quantify behavioral discrepancies and a Reinforcement Learning (RL) framework to enable real-time interface adaptation based on user interactions. Official secondary data from the Central Statistics Agency (BPS) were utilized to simulate and validate system performance, demonstrating that the combined algorithms achieve efficient, accurate, and ethically responsible personalization without the need for direct field data collection. The findings indicate that the AUI model dynamically adjusts to learner behavior patterns, improves digital engagement, and can be scaled to broader educational systems. Overall, this research provides a resource-efficient, data-driven, and ethically grounded framework for developing intelligent adaptive learning environments that promote educational equity and technological inclusiveness.