Design of Augmented Reality Learning Media with AI Chatbot Support for Computer Network Learning - Muhammad Ridho Ardiansyah; Andri Saputra; Md. Ruhul Amin (Journal Innovation in Information and Computer Technology, 2026)
Computer network learning often involves abstract concepts, invisible data flows, and physical devices that are not always available in the classroom. This condition can make it difficult for beginner students to understand network devices, topology structures, and basic communication processes. This study aims to design augmented reality learning media with AI chatbot support for computer network learning. The research used a research and development approach adapted from the Multimedia Development Life Cycle (MDLC), consisting of concept, design, material collecting, assembly, testing, and distribution stages. The developed media integrates AR visualization, learning materials, and chatbot-based assistance in one learning environment. The AR feature presents three-dimensional models of network devices and topology structures, while the AI chatbot provides simple explanations and guidance related to computer network concepts. Functional testing was conducted to ensure that the main features operated according to the expected results, including the main menu, learning material page, AR object display, topology visualization, AI chatbot interaction, instruction menu, and navigation buttons. User response evaluation was also conducted using a Likert-scale questionnaire involving 20 students. The results showed that all main features worked properly, and the user response evaluation obtained an average score of 85.2%, categorized as Very Good. These findings indicate that the developed media is feasible as a supporting tool for computer network learning. The integration of AR visualization and AI chatbot support can help students learn more independently, understand network concepts more clearly, and receive immediate learning assistance. Future development may focus on improving AR object optimization, expanding chatbot knowledge, and testing the media’s effectiveness on students’ learning outcomes.