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How Human is AI? Examining the Impact of Emotional Prompts on Artificial and Human and Responsiveness

arXiv 2601.05104 ↗First posted 8 Jan 2026cs.CL econ.GN q-fin.ECcited by 0

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Abstract (arXiv)

This research examines how the emotional tone of human-AI interactions shapes ChatGPT and human behavior. In a between-subject experiment, we asked participants to express a specific emotion while working with ChatGPT (GPT-4.0) on two tasks, including writing a public response and addressing an ethical dilemma. We found that compared to interactions where participants maintained a neutral tone, ChatGPT showed greater improvement in its answers when participants praised ChatGPT for its responses. Expressing anger towards ChatGPT also led to a higher albeit smaller improvement relative to the neutral condition, whereas blaming ChatGPT did not improve its answers. When addressing an ethical dilemma, ChatGPT prioritized corporate interests less when participants expressed anger towards it, while blaming increases its emphasis on protecting the public interest. Additionally, we found that people used more negative, hostile, and disappointing expressions in human-human communication after interactions during which participants blamed rather than praised for their responses. Together, our findings demonstrate that the emotional tone people apply in human-AI interactions not only shape ChatGPT's outputs but also carry over into subsequent human-human communication.

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References · 17

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Training language models to follow instructions with human feedbackcited by 15,712
  3. 2023A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPTcited by 612
  4. 2023Evaluating Large Language Models in Theory of Mind Taskscited by 217
  5. 2023Large Language Models Understand and Can be Enhanced by Emotional Stimulicited by 143
  6. 2023ExpertPrompting: Instructing Large Language Models to be Distinguished Expertscited by 140
  7. 2024On the Conversational Persuasiveness of Large Language Models: A Randomized Controlled Trialcited by 105
  8. 2022Human-AI Collaboration Enables More Empathic Conversations in Text-based Peer-to-Peer Mental Health Supportcited by 68
  9. 2025Expressing stigma and inappropriate responses prevents LLMs from safely replacing mental health providerscited by 67
  10. 2023Building Emotional Support Chatbots in the Era of LLMscited by 62
  11. 2024The Dark Side of AI Companionship: A Taxonomy of Harmful Algorithmic Behaviors in Human-AI Relationshipscited by 61
  12. 2023Exploring the psychology of LLMs' Moral and Legal Reasoningcited by 43
5 more
  1. 2023A Wide Evaluation of ChatGPT on Affective Computing Taskscited by 30
  2. 2024RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Contentcited by 26
  3. 2024NegativePrompt: Leveraging Psychology for Large Language Models Enhancement via Negative Emotional Stimulicited by 18
  4. 2023The Good, The Bad, and Why: Unveiling Emotions in Generative AIcited by 10
  5. 2025"Only ChatGPT gets me": An Empirical Analysis of GPT versus other Large Language Models for Emotion Detection in Textcited by 3

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