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@article{189037,
        author = {Nikhil Kumar},
        title = {Analyzing Emotional Drift in Artificial Intelligence During Extended Human–AI Conversations},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {4414-4421},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189037},
        abstract = {Conversational AI systems are designed to generate human-like responses, yet their tone, style, and behavioural patterns often shift subtly during extended interactions. This gradual change, referred to as emotional drift, represents small but meaningful variations in how an AI responds over time. Understanding this phenomenon is essential, as users may misinterpret these shifts as signs of emotion, personality, or intentional behaviour from the AI system. 
The primary aim of this study is to determine whether conversational AI exhibits emotional drift and to identify the types of behavioural changes that occur during prolonged interactions. The research focuses on analysing variations in sentiment polarity, expressive tone, vocabulary usage, and overall communication style. 
To conduct this analysis, conversational data was examined using sentiment scoring, tone classification, linguistic pattern tracking, and behavioural drift detection methods. These analytical techniques help reveal whether the AI’s responses become increasingly friendly, formal, emotional, or inconsistent as the conversation progresses. 
The findings indicate noticeable shifts in tone and vocabulary, suggesting that AI systems may adapt their behaviour based on prior conversational context. Such patterns provide evidence of emotional drift within conversational AI. 
This study underscores the importance of recognising and monitoring emotional drift, as it can influence AI reliability, user trust, and the stability of long-term human–AI interactions. These insights can support the development of improved alignment strategies, behavioural monitoring tools, and safety frameworks for future AI systems.},
        keywords = {Emotional Drift; Conversational AI; Long-Term Interaction; Sentiment Analysis; Tone Variation; Behavioural Shift; AI Reliability; Alignment Strategies.},
        month = {December},
        }