A Replicable Robotics Awareness Method Using LLM-Enabled Robotics Interaction: Evidence from a Corporate Challenge

TL;DR

This study demonstrates that challenge-based interactions with LLM-enabled humanoid robots effectively introduce non-expert users to robotics and AI in organizational settings, fostering engagement and understanding.

Contribution

The paper presents a novel, replicable challenge-based method for robotics awareness using LLM-enabled humanoid robots in real-world organizational environments.

Findings

High user satisfaction and increased interest in robotics and AI.

Participants reported improved understanding of human-robot collaboration.

Interaction was perceived as natural and easier over time.

Abstract

Large language models are increasingly being explored as interfaces between humans and robotic systems, yet there remains limited evidence on how such technologies can be used not only for interaction, but also as a structured means of introducing robotics to non-specialist users in real organizational settings. This paper introduces and evaluates a challenge-based method for robotics awareness, implemented through an LLM-enabled humanoid robot activity conducted with employees of AD Ports Group in the United Arab Emirates. In the event, participants engaged with a humanoid robot in a logistics-inspired task environment using voice commands interpreted through an LLM-based control framework. The activity was designed as a team-based, role-driven experience intended to expose participants to embodied AI and human-robot collaboration without requiring prior robotics expertise. To evaluate the approach, a post-event survey remained open for 16 days and collected 102 responses. Results indicate strong overall reception, with high satisfaction (8.46/10), increased interest in robotics and AI (4.47/5), and improved understanding of emerging forms of human-robot collaboration (4.45/5). Participants who interacted directly with the robot also reported natural interaction (4.37/5) and a strong sense that interaction became easier as the activity progressed (4.74/5). At the same time, lower ratings for reliability and predictability point to important technical and design challenges for future iterations. The findings suggest that challenge-based, LLM-enabled humanoid interaction can serve as a promising and replicable method for robotics awareness in industrial and operational environments.