Evan MikaiLu¹ and Tyler Boulom², ¹USA, ²California State Polytechnic University, USA

This project addresses the critical need for realistic, accessible, and scalable lifeguard and CPR training to better prepare responders for aquatic emergencies. To solve this, an AI-driven interactive simulation was developed using Unity, integrating first-person perspectives, checkpoint-based decision-making, and real-time AI-generated feedback to enhance skill retention and decision-making capabilities. The key technologies utilized include Unity for simulation environments and OpenAI's natural language processing for context-specific response generation. Challenges included ensuring AI accuracy and optimizing the user interface across diverse platforms. These were mitigated by expanding AI training datasets and refining interface graphics. Experiments demonstrated high AI accuracy in matching authoritative standards, though minor inaccuracies indicated areas for dataset improvement. Interface responsiveness tests revealed the need for better optimization on mobile platforms. Ultimately, this project provides an effective training method combining interactive realism and intelligent feedback, making CPR and lifeguard training more engaging, effective, and widely accessible, ultimately improving emergency preparedness outcomes.

AI-driven training, Lifeguard simulation, CPR skill retention, Unity-based emergency preparedness