# AI-exhibited Personality Traits Can Shape Human Self-concept through Conversations

arXiv: https://arxiv.org/abs/2601.12727 · HTML: https://mlsanity.com/paper/2601.12727/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 130 references that are in the map.
- First posted on arXiv 19 Jan 2026.

## Builds on (most cited first, 30 of 32)

- [Persona Vectors: Monitoring and Controlling Character Traits in Language Models](https://mlsanity.com/paper/2507.21509/) (2025)
- [Character-LLM: A Trainable Agent for Role-Playing](https://mlsanity.com/paper/2310.10158/) (2023)
- [PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traits](https://mlsanity.com/paper/2305.02547/) (2023)
- [Homogenization Effects of Large Language Models on Human Creative Ideation](https://mlsanity.com/paper/2402.01536/) (2024)
- [Human-Centered Explainable AI (XAI): From Algorithms to User Experiences](https://mlsanity.com/paper/2110.10790/) (2021)
- [Evaluating and Inducing Personality in Pre-trained Language Models](https://mlsanity.com/paper/2206.07550/) (2022)
- [To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making](https://mlsanity.com/paper/2102.09692/) (2021)
- [Who is GPT-3? An Exploration of Personality, Values and Demographics](https://mlsanity.com/paper/2209.14338/) (2022)
- [Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task](https://mlsanity.com/paper/2506.08872/) (2025)
- [The Dark Side of AI Companionship: A Taxonomy of Harmful Algorithmic Behaviors in Human-AI Relationships](https://mlsanity.com/paper/2410.20130/) (2024)
- [AI Suggestions Homogenize Writing Toward Western Styles and Diminish Cultural Nuances](https://mlsanity.com/paper/2409.11360/) (2024)
- [Do LLMs Have Distinct and Consistent Personality? TRAIT: Personality Testset designed for LLMs with Psychometrics](https://mlsanity.com/paper/2406.14703/) (2024)
- [Do LLMs Possess a Personality? Making the MBTI Test an Amazing Evaluation for Large Language Models](https://mlsanity.com/paper/2307.16180/) (2023)
- [Beyond prompt brittleness: Evaluating the reliability and consistency of political worldviews in LLMs](https://mlsanity.com/paper/2402.17649/) (2024)
- [Estimating the Personality of White-Box Language Models](https://mlsanity.com/paper/2204.12000/) (2022)
- [Large Language Model Psychometrics: A Systematic Review of Evaluation, Validation, and Enhancement](https://mlsanity.com/paper/2505.08245/) (2025)
- [The Effects of Generative AI on Design Fixation and Divergent Thinking](https://mlsanity.com/paper/2403.11164/) (2024)
- [LLM Agents in Interaction: Measuring Personality Consistency and Linguistic Alignment in Interacting Populations of Large Language Models](https://mlsanity.com/paper/2402.02896/) (2024)
- [Large Language Models Can Infer Psychological Dispositions of Social Media Users](https://mlsanity.com/paper/2309.08631/) (2023)
- [Open Models, Closed Minds? On Agents Capabilities in Mimicking Human Personalities through Open Large Language Models](https://mlsanity.com/paper/2401.07115/) (2024)
- [As Confidence Aligns: Exploring the Effect of AI Confidence on Human Self-confidence in Human-AI Decision Making](https://mlsanity.com/paper/2501.12868/) (2025)
- [Multi-Agents are Social Groups: Investigating Social Influence of Multiple Agents in Human-Agent Interactions](https://mlsanity.com/paper/2411.04578/) (2024)
- [LMLPA: Language Model Linguistic Personality Assessment](https://mlsanity.com/paper/2410.17632/) (2024)
- [Self-assessment, Exhibition, and Recognition: a Review of Personality in Large Language Models](https://mlsanity.com/paper/2406.17624/) (2024)
- [Humanizing LLMs: A Survey of Psychological Measurements with Tools, Datasets, and Human-Agent Applications](https://mlsanity.com/paper/2505.00049/) (2025)
- [How Does Conversation Length Impact User's Satisfaction? A Case Study of Length-Controlled Conversations with LLM-Powered Chatbots](https://mlsanity.com/paper/2404.17025/) (2024)
- [Timing Matters: How Using LLMs at Different Timings Influences Writers' Perceptions and Ideation Outcomes in AI-Assisted Ideation](https://mlsanity.com/paper/2502.06197/) (2025)
- [Letters from Future Self: Augmenting the Letter-Exchange Exercise with LLM-based Agents to Enhance Young Adults' Career Exploration](https://mlsanity.com/paper/2502.18881/) (2025)
- [Synthetic Human Memories: AI-Edited Images and Videos Can Implant False Memories and Distort Recollection](https://mlsanity.com/paper/2409.08895/) (2024)
- [Learning Through AI-Clones: Enhancing Self-Perception and Presentation Performance](https://mlsanity.com/paper/2310.15112/) (2023)

## Built on by (most cited first, 3 of 3)

- [Personalities at Play: Probing Alignment in AI Teammates](https://mlsanity.com/paper/2603.00429/) (2026)
- [Assessing mentalization in humans and large language models](https://mlsanity.com/paper/2608.26291/) (2026)
- [InnerPond: Fostering Inter-Self Dialogue with a Multi-Agent Approach for Introspection](https://mlsanity.com/paper/2603.27563/) (2026)

## Abstract (arXiv)

Recent Large Language Model (LLM) based AI can exhibit recognizable and measurable personality traits during conversations to improve user experience. However, as human understandings of their personality traits can be affected by their interaction partners' traits, a potential risk is that AI traits may shape and bias users' self-concept of their own traits. To explore the possibility, we conducted a randomized behavioral experiment. Our results indicate that after conversations about personal topics with an LLM-based AI chatbot using GPT-4o default personality traits, users' self-concepts aligned with the AI's measured personality traits. The longer the conversation, the greater the alignment. This alignment led to increased homogeneity in self-concepts among users. We also observed that the degree of self-concept alignment was positively associated with users' conversation enjoyment. Our findings uncover how AI personality traits can shape users' self-concepts through human-AI conversation, highlighting both risks and opportunities. We provide important design implications for developing more responsible and ethical AI systems.