Conversational Agents in Augmented Reality: A Systematic Review

Conversational agents (CAs) are increasingly integrated into augmented reality (AR), yet evidence on how conversational capabilities are combined with AR contexts remains dispersed across application domains and device types. We conducted a systematic review of 44 peer-reviewed studies (2007–2025) using predefined search and screening procedures. Across the included systems, speech is the predominant input modality (95%), and head-mounted displays (HMDs) are most frequently used (66%). Many prototypes couple speech with contextual information such as location or object recognition (41%) to adapt conversational behaviour. We also observed a strong trend in dialogue management toward agent-based architectures powered by Large Language Models, with 12 of the 14 agent-based systems being published after 2023. Our analysis resulted in the development of a framework for AR-CA interaction, which positions systems along two dimensions: Presence Form and Context Awareness. The framework reveals an emerging trend toward dynamic, context-aware interaction. User experience reports are broadly positive, but limitations include challenges with long-term engagement, privacy risks, and hardware constraints. Our review yields four key design considerations: match an avatar’s embodiment with equally capable LLM dialogue; enrich interactions through haptics and spatial audio; preserve physical and social plausibility; and adopt user-centred, privacy-aware hardware choices. Together, these principles chart a path toward robust, contextually intelligent, and ethically responsible AR conversational agents.