Dynamic QTE Generation with DeepSeek: Towards AI-Enhanced Adaptive Gameplay

This paper presents an AI-driven framework for dynamic Quick Time Event (QTE) generation that adapts to real-time player behavior. Our hybrid architecture combines LSTM networks for 300ms-ahead action prediction (89.2% accuracy) with a dual-constraint GAN for event synthesis, achieving 92.4% narrative coherence. Experimental results with 120 participants demonstrate 23.6% improvement in immersion scores (p=0.017) and 17.2% higher success rates compared to static QTEs, while maintaining real-time performance (14.7ms/frame latency) on consumer GPUs. The system reduces development costs by 15-20% through automated variation generation, addressing critical challenges in adaptive content creation.