This paper presents a dialogue framework designed to capture human-robot interactions enriched with human-level situational awareness. The system integrates advanced large language models with real-time human-in-the-loop control. Central to this framework is an interaction manager that oversees information flow, turn-taking, and prosody control of a social robot’s responses. A key innovation is the control interface, enabling a human operator to perform tasks such as emotion recognition and action detection through a live video feed. The operator also manages high-level tasks, like topic shifts or behaviour instructions. Input from the operator is incorporated into the dialogue context managed by GPT-4o, thereby influencing the ongoing interaction. This allows for the collection of interactional data from an automated system that leverages human-level emotional and situational awareness. The audio-visual data will be used to explore the impact of situational awareness on user behaviors in task-oriented human-robot interaction.