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

2026-01-19

AI Summary

This study investigates whether personality traits exhibited by AI conversational agents can induce shifts in users’ self-concepts. In a randomized controlled experiment, participants engaged in personal-topic dialogues with a chatbot powered by GPT-4o’s default personality profile, and changes in their self-concept were assessed using standardized personality inventories. The findings provide the first empirical evidence that users’ self-concepts significantly assimilate toward the AI’s personality traits following interaction, with the degree of assimilation increasing as dialogue duration lengthens. Moreover, this convergence effect is positively associated with higher levels of conversational enjoyment, highlighting the potential of large language models to shape human self-perception and underscoring the attendant risks of cognitive and identity-level alignment with artificial personas.

Technology Category

• Humans and AI: User Experience and Usability
• Natural Language Processing: Conversational AI/Dialog Systems
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

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.

Problem

• AI personality
• self-concept
• human-AI interaction
• personality alignment
• large language models

Innovation

• AI personality traits
• self-concept alignment
• human-AI interaction
• large language models
• behavioral experiment