ARIA: Toward Human-Centered Embodied AI Instruction in Real-Time Augmented Reality

Advances in artificial intelligence and embodied interaction in augmented reality (AR) are creating new opportunities for intelligent instructional systems that dynamically adapt to individual learners. However, sustaining real-time responsiveness while preserving natural, socially meaningful interaction remains a persistent challenge. This work introduces ARIA (Augmented Reality Instructional Agent), a real-time software architecture for embodied AI instruction in augmented reality. ARIA leverages large language models (LLMs) with adaptive prompt engineering to tailor dialogue style, instructional strategy, and persona expression to each user. Its modular pipeline is optimized for robust, low-latency performance, with benchmarks reported for responsiveness and system stability. To complement the technical evaluation, user experience was assessed through standardized questionnaires, offering insights into perceived personalization, trust, and interaction quality. Quantitative and qualitative results demonstrate that ARIA achieves sub-second responsiveness, high pragmatic and hedonic usability, and a strong sense of co-presence and instructional trust. This work contributes a unified framework and reference architecture for developing adaptive embodied agents that combine technical efficiency with human-centered design, highlighting how real-time responsiveness can serve as the foundation for relational engagement in embodied AI instruction.