Development of a WebXR-Based Collaborative LMS System with 3D Virtual Features and Artificial Intelligence
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Collaborative Learning Management System,
Immersive Learning,
Multimedia Development Life Cycle,
WebXR
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Ayung Candra Padmasari
Ahamad Tarmizi Azizan
Burieva Kibrio Ergashevna
Abstract
Background of Study: The advancement of immersive technology and artificial intelligence (AI) offers new opportunities for creating more adaptive and interactive learning systems. However, higher education institutions still face challenges such as limited industry-standard facilities and the high cost of multimedia equipment.
Aims and Scope of Paper: This study aims to develop a prototype of a WebXR-based Collaborative Learning Management System (LMS) equipped with 3D virtual features and AI integration to enhance student learning experiences.
Methods: The research employed the Multimedia Development Life Cycle (MDLC) method, which consists of six stages: conceptualization, design, material collection, assembly, testing, and distribution. The study involved 30 Multimedia Education students from Universitas Pendidikan Indonesia selected through purposive sampling.
Result: Feasibility testing using a Likert-scale questionnaire revealed that the system achieved a highly feasible category with average scores of 81,7 % for Learnability 85,6 %, for system performance 76,93%, for Efficiency 79,9%, for memorability 74,2%, satisfaction 85,4 %. Resulting in an overall feasibility of 81.7%. Semi-structured interviews confirmed that AI integration significantly supported learning personalization and provided content recommendations, although the AI feature was limited to text-based responses.
Conclusion: The results indicate that combining WebXR and AI in an LMS can address the challenges of industry-based learning by providing immersive, adaptive, and accessible learning experiences. This system demonstrates strong potential as a future-ready digital learning solution, with future research suggested to evaluate its impact on learning outcomes and improve AI capabilities for deeper contextual interaction.
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Copyright (c) 2025 Ayung Candra Padmasari, Ahamad Tarmizi Azizan, Burieva Kibrio Ergashevna
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
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