Repurpose, Reimagine, Receptionist: The Badr AI Mannequin Project

Introduction: Academic environments encounter significant challenges in managing front-desk

operations, especially in specialized fields like medicine, where budget constraints and multilingual

requirements are prevalent. This paper presents Badr, an AI-enabled robot receptionist created by

repurposing an existing medical simulation mannequin for use at the Institute of Learning within an

academic health system.

Methods: We implemented a distributed hardware architecture that utilizes dual

Raspberry Pi computing units, embedded AI components such as facial recognition and natural language

processing, and integrated departmental-specific knowledge management systems. The implementation

process included structural adaptation of the mannequin, development of multilingual capabilities

supporting English and Arabic (including the Emirati dialect), and customization of interaction protocols

for the Institute's four departments: Health Professions Education, Healthcare Simulation, Organizational

Learning, and Research Center.

Results: Preliminary evaluations during the pilot phase indicated

promising performance, with the system showcasing its capabilities in face recognition, motion detection,

and speech recognition, even under varying environmental conditions. Initial tests suggest the potential

for performance comparable to commercial alternatives at a significantly lower implementation cost,

though a thorough long-term evaluation is still necessary.

Discussion: The initial implementation

showcases the potential viability of repurposing existing simulation equipment for administrative

functions, creating a framework for cost-effective AI reception systems in academic settings. Although still

in the pilot phase, this approach expands the utility of institutional assets beyond their original training

purposes and provides a model for similar implementations at other institutions with underutilized

simulation resources.