AI-Driven Curriculum Personalization System Using K-Nearest Neighbors Algorithm Based on Psychological Profiles and Interests
Somantri Somantri, Dihak Muhammad Nur Al-Ma'arif, Afrizal Maulana, Risa Aidha, Imam Sanjaya, Anggun Fergina · 2024
The rapid advancement of artificial intelligence (AI) has opened new avenues for personalized education, aligning learning experiences with individual student needs. This paper presents an AI-driven curriculum personalization system utilizing the K-Nearest Neighbors (K-NN) algorithm, integrating psychological profiles and interests to tailor educational content. Traditional curriculum design often fails to accommodate the diverse learning styles and preferences of students, leading to disengagement and suboptimal performance. By leveraging AI and machine learning, our system addresses these challenges by creating a dynamic and adaptive learning environment. The proposed system architecture encompasses data collection from psychological assessments and interest surveys, preprocessing this data to identify patterns and correlations. The K-NN algorithm is employed to categorize students based on their psychological traits and interests, enabling the generation of personalized curricula. This approach ensures that the educational content resonates with students' intrinsic motivations and cognitive styles, fostering a more engaging and effective learning experience.