CADA: A Contextual Adaptive Dialogue Agent Integrating Dynamic Feedback for Enhanced Conversational AI

Conversational AI models have revolutionized human-computer interaction, yet challenges persist in achieving seamless, context-aware, and adaptive dialogues. This paper proposes and evaluates a novel hybrid framework designed to bridge two critical gaps: limited contextual awareness and inadequate real-time user feedback integration. The framework synthesizes multimodal contextual analysis with a dynamic, reinforcement learning-based feedback loop. I present a methodological implementation using a modified Transformer architecture augmented with a contextual memory module and a reward model trained on human preferences. Evaluation on a custom dataset simulating educational and customer service dialogues shows a 28% improvement in response appropriateness and a 32% increase in user satisfaction scores compared to a baseline GPT-3.5-turbo fine-tuned model. Key findings highlight the importance of real-time adaptation and transparent feedback mechanisms in fostering trust. The paper concludes with a critical discussion on ethical implications, specifically bias amplification in feedback loops, and provides recommendations for future research in scalability and cross-cultural generalization.