CharacterMind: An AI-Powered Fictional Character Simulation System for Immersive Conversational Experiences

The rapid evolution of conversational artificial intelligence has been largely driven by advances in large-scale language models capable of generating coherent, context-aware, and linguistically rich responses. These systems demonstrate strong performance across a wide range of applications, including virtual assistance, customer support, storytelling, and knowledge retrieval. Despite these advancements, one persistent limitation continues to affect their usability in immersive and long-term interactions: the inability to maintain a consistent persona. While modern conversational models can imitate a specific tone or character in short interactions, they often fail to sustain that identity across extended dialogue. Over time, responses tend to shift toward a neutral or generic style, resulting in a loss of character authenticity and reduced user engagement. This phenomenon, commonly referred to as persona drift, highlights the absence of explicit mechanisms for enforcing personality constraints during response generation. This research presents a structured framework for developing persona-consistent conversational AI systems by integrating transformer-based language models with explicit persona encoding and retrieval-augmented memory. Unlike traditional approaches that rely primarily on prompt-based conditioning, the proposed system introduces a multi-layer architecture that reinforces persona alignment throughout the generation pipeline. A persona consistency filter is employed to evaluate generated responses and ensure alignment with predefined character attributes. The system is evaluated using both automated metrics and human-centered assessments, including persona consistency scores, contextual coherence measures, and user engagement ratings. Experimental findings indicate that the integration of persona embeddings and memory retrieval significantly improves identity retention and conversational continuity. The proposed approach demonstrates measurable improvements over baseline systems in maintaining tone, behavior, and contextual alignment.