Development of a Standalone Adaptive Mobile Learning Management System with AI-Based Personalized Learning Recommendations
Keywords:
Adaptive Learning, Artificial Intelligence, Personalized Learning, Mobile Learning, Learning Management System.Abstract
This study focuses on the development of a Standalone Adaptive Mobile Learning Management System with AI-Based Personalized Learning Recommendations for higher education students. The proposed system aims to provide personalized learning support by using learner performance and engagement information to recommend appropriate learning materials, practice activities, and feedback. The system will integrate common Learning Management System functions, including user account management, subject and enrollment management, learning materials, assessments, assignment submission, progress tracking, notifications, offline access with synchronization, and QR-based attendance. An AI-based recommendation module will process available learner information to generate recommendations based on the learner’s current progress and knowledge state. The study will use a Descriptive-Developmental Research Design and Quantitative Approach, with Agile Scrum as the system development methodology. The developed system will undergo functional testing and user evaluation focusing on usability, effectiveness, and user satisfaction. The expected output is a functional standalone mobile learning platform that integrates conventional LMS functions with adaptive recommendations and mobile-oriented learning support. The study aims to provide a more responsive and accessible learning environment while supporting the educational technology goals associated with Sustainable Development Goal 4, Quality Education.
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Copyright (c) 2026 Lebron James S. Santos, Karl Allen B. Dy, Ashlie C. Cabigting, Christan Mark M. Santos, Carl Andrew C. Pestaño (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
