Authors : Herbert Wanga

Volume/Issue : Volume 10 - 2025, Issue 12 - December

Google Scholar : https://tinyurl.com/ycxuvtn8

Scribd : https://tinyurl.com/5cemzvz3

DOI : https://doi.org/10.38124/ijisrt/25dec1454

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : 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.

Keywords : Conversational AI, Contextual Understanding, User Feedback, Reinforcement Learning from Human Feedback (RLHF), Adaptive Learning, Ethical AI.

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