Alim Sumarno, Hirnanda Dimas Pradana, Fajar Arianto, Ainur Rofik, Gesita Septafi
The increasing adoption of asynchronous online learning has intensified the need for intelligent and responsive learning support within learning management systems (LMS). However, most existing LMS platforms remain limited to content management and administrative functions, providing minimal real-time and contextual assistance for learners. This study presents the design, integration, and technical evaluation of an artificial intelligence (AI)–based online assistant integrated directly into an LMS to support asynchronous learning activities. The proposed system, named ANSIA, was developed using a systems engineering approach that enables seamless integration without modifying the core LMS architecture. ANSIA utilizes learning context data from the LMS to generate relevant and context-aware responses to learners’ queries. The system was implemented as a web-based module and evaluated through technical performance testing focusing on response accuracy, response time, and system reliability under realistic asynchronous learning scenarios. The results demonstrate that the AI assistant achieves high response accuracy, acceptable response times under varying loads, and stable operation during continuous use. These findings indicate that integrating an AI assistant into an LMS can enhance learning support while maintaining system stability and scalability. This study contributes a practical and technically validated approach for developing adaptive, modular, and sustainable AI-assisted online learning systems. © School of Engineering, Taylor’s University.
Universitas Negeri Surabaya, Jl. Lidah Wetan, Surabaya, Indonesia; Yayasan Bunga Bangsa Surabaya, Jl. Pakal Pejuang, Surabaya, Indonesia
