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Action Against Hunger /Acción contra el Hambre (USA)

Market Survey: Immersive Emergency Training Solutions – Exploring the Feasibility and Demand for a Gamified and AI-supported Immersive Training Platform

Last update: Apr 29, 2025 Last update: Apr 29, 2025

Details

Location:USA
USA
Category:Consulting services
Status:Closed
Sectors:Humanitarian Aid & Emergency, Training, Information & Communication Technology
Languages:English
Contracting authority: Action Against Hunger /Acción contra el Hambre (USA)
Funding Agency:
Nonprofit organization
Contracting authority type:Nonprofit Organisation
Eligibility:Individual
Budget:N/A
Date posted: Apr 29, 2025

Attachments • 1

Associated Awards

Project cycle timeline

STAGES
EARLY INTELLIGENCE
PROCUREMENT
IMPLEMENTATION
Cancelled
Status
Programming
Formulation
Approval
Forecast
Open
Closed
Shortlisted
Awarded
Evaluation

Quick summary

AI generated
Objectives: In the face of complex and frequent humanitarian crises, effective emergency preparedness through localized humanitarian action is essential. This tender explores the development of a gamified, immersive emergency training platform aimed at enhancing emergency response capacities. The platform would use VR/video game-style simulations, AI-supported characters, and post-simulation coaching to provide scalable training. It will complement existing training systems and be flexible to various user needs. The primary objective is to identify training gaps, assess platform feasibility, and develop economic models for its development and adoption within the humanitarian sector.
Eligibility criteria: Eligible organizations for this tender include local and international NGOs, national coordination structures, and government entities operating in the humanitarian sector ...

Description

1. Background In an era of increasingly complex, frequent, and compounded humanitarian crises, effective emergency preparedness and response capacities are more essential than ever. At the same time, the sector is undergoing a shift toward more localised
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