Eric Jia Luo Lu ¹ and Rodrigo Onate ² , ¹ USA, ² California State Polytechnic University, USA

Science museums struggle to provide personalized, age-appropriate experiences for diverse family audiences, leading to suboptimal learning outcomes and visitor engagement. This paper presents an AI-powered mobile companion application for Discovery Cube Orange County that addresses these challenges through intelligent personalization and interactive guidance. Built with Flutter and OpenAIs GPT-4, the system integrates three core components: a profile management system storing child demographics, an AI recommendation engine generating personalized exhibit suggestions, and an interactive tour system combining QR code scanning with conversational AI assistance [8]. Implementation challenges included managing API response latency, ensuring age-appropriate content accuracy, and handling variable network conditions. Experimental evaluation across 60 age-appropriateness trials achieved a mean rating of 4.45/5.0, with strongest performance for ages 8-10 [1]. Network performance testing revealed bandwidth as the critical factor, with response times ranging from 1.8 to 5.4 seconds. The system demonstrates that AI-driven personalization makes museum experiences more accessible and educationally effective than traditional AR-based or static content approaches, offering a scalable model for informal STEM education enhancement.

AI-powered personalization, Museum companion application, GPT-4 recommendation engine, QR code exhibit navigation, Child profile management