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This study explores how a conversational artificial intelligence (AI) system can be integrated into a learning management system (LMS) to support learners’ immersive interactions. It presents a chatbot model designed through a design-based research (DBR) approach, using data collected from LMS usage logs, learner interaction cases, and expert consultations to inform scenario mapping and dialogue construction. The goal of the study is to design a chatbot that provides academic guidance and responsive communication tailored to learners’ individual needs. This study applied a design-based research methodology to reflect actual educational settings. The chatbot can support various learning tasks such as understanding learning content, recommending actions, and providing encouragement by integrating rule-based functions and AI-based text generation functions. The entire system is organized around three practical areas: supporting learning activities, guiding course access, and tracking learning progress. The chatbot’s conversation patterns and interaction design were specified using representative learner scenarios. These scenarios contributed to deriving a timely and appropriate feedback provision method. Although this model has not yet been verified in an actual classroom environment, it provides a starting point for future applications and research. This study demonstrates that conversational AI can improve the quality of learner-system communication in digital education environments, and suggests that its effectiveness is greater when it is consistent with educational intent. Although this model has not yet been verified in an actual classroom environment, it is intended as a prototype foundation for future implementation studies, while the current findings contribute to conceptual validation.