Beyond One-Size-Fits-All Learning: An AI-Driven Personalized Learning Pathway Framework - Eko Risdianto; Joseline Santos; Noel Lomerio; Rita Sinthia; Tri Basuki Kurniawan; Deshinta Arrova Dewi; Laura Mahendratta Tjahjono; Mona Ardina; Desvi Wahyuni (Online Learning In Educational Research (OLER), 2026)
Artificial intelligence has accelerated the evolution of personalized learning, yet most learning management systems continue to rely on static instructional pathways that inadequately accommodate learners’ cognitive and behavioral diversity. This study evaluated an AI-driven Personalized Learning Pathway (PLP) framework that integrates learner analytics, intelligent content recommendation, and adaptive assessment within a unified learning environment to enhance student engagement and academic achievement. A mixed-methods quasi-experimental design was conducted with 240 undergraduate students from four universities across Southeast Asia. Students in the experimental group learned through an AI-augmented learning management system, while the control group used a conventional platform. The findings demonstrate that the AI-driven PLP framework consistently improved student engagement, motivation, course completion, and academic achievement compared with traditional learning management systems. Students also exhibited stronger learning adaptability and more effective responses to personalized feedback, both of which emerged as key contributors to academic success. By integrating behavioral analytics with real-time instructional adaptation, the proposed framework moves beyond content personalization toward a responsive learning ecosystem. This study contributes a scalable AI-enabled instructional framework that supports learner-centered higher education and provides practical guidance for implementing adaptive digital learning environments