Digital Doppelganger: AI-Powered Conversational Personality Simulation

Abstract

This study about the project presents the design and development of an AI-based Digital Doppelganger aimed at modeling and simulating an individual’s personality, communication patterns, and cognitive characteristics. The proposed system integrates natural language processing techniques, similarity analysis, and adaptive learning mechanisms inspired by transformer-based architectures such as LLaMA2. This approach prioritize consistent personality, emotional depth, and contextual awareness over simple task execution or information retrieval. By continuously learning from user interactions and real-time data, the system is able to reproduce human-like tone, intent, and linguistic behavior while preserving coherence across conversations. The Results show the unique personality of an individual by its digital twin. Experimental results indicate strong performance in intent recognition and contextual relevance, suggesting that the Digital Doppelganger framework can be effectively applied in personalized virtual assistants, digital identity representation, and research on advanced human–AI interaction.