Simulated Souls: Investigating the Emotional Fallacy in Large Language Models

The emergence of generative artificial intelligence has ushered in an era where machines produce text with such coherence and emotional resonance that they increasingly blur the line between simulation and sentience. This paper investigates the emotional fallacy-the tendency of users and even developers to anthropomorphize language models, attributing genuine emotions to systems that operate purely on statistical probabilities and pattern recognition. While Large Language Models (LLMs) such as ChatGPT, Claude, Gemini, and Meta AI are designed to generate emotionally evocative responses, their outputs are often misinterpreted as evidence of internal states, self-awareness, or affective reasoning. This anthropomorphic illusion has profound implications-not just for user interaction and trust-but also for ethical frameworks surrounding AI responsibility, accountability, and emotional labour. Through a novel empirical study, responses of multiple LLMs were evaluated to a carefully curated set of emotionally charged and ethically sensitive prompts. The outputs were analysed qualitatively across three axes: linguistic style, affective mimicry, and ethical stance. Our comparative analysis highlights both the superficial coherence and the underlying inconsistency of AI-generated emotional responses, reinforcing the notion that LLMs do not "feel" but only simulate affect with remarkable linguistic finesse. Building on these findings, an ethical framework that addresses emotional deception in AIhuman communication was proposed. This includes the necessity of disclosure protocols, affective transparency, and regulation against emotional manipulation in public-facing AI systems. Furthermore, this work argues that the emotional fallacy, if left unchecked, may distort social expectations from AI and influence mental health, empathy fatigue, and societal norms surrounding machine consciousness. By integrating philosophical inquiry, empirical testing, and ethical design, this paper contributes to the growing discourse on responsible AI. A paradigm shift from emotional realism to emotional accountability in generative systems is called for-an urgent need as LLMs increasingly infiltrate education, healthcare, and emotional support domains. The findings serve as both a cautionary lens and a research frontier in the study of human-AI emotional interaction.