The low-intrusion and automated personality assessment is receiving increasing attention in psychology and human-computer interaction fields. This study explores an interactive approach for personality assessment, focusing on the multiplicity of personality representation. We propose a framework of Gamified Personality Assessment through Multi-Personality Representations (Multi-PR GPA). The framework leverages Large Language Models to empower virtual agents with different personalities. These agents elicit multifaceted human personality representations through engaging in interactive games. Drawing upon the multi-type textual data generated throughout the interaction, it achieves personality assessments with interpretable insights. Grounded in the classic Big Five personality theory, we developed a prototype system and conducted a user study to evaluate the efficacy of Multi-PR GPA. The results affirm the effectiveness of our approach in personality assessment and demonstrate its superior performance when considering the multiplicity of personality representation. Error structure analysis further revealed systematic assessment biases in LLMs, which multi-context aggregation partially mitigated.
@article{arxiv.2507.04005,
title = {Exploring a Gamified Personality Assessment Method through Interaction with LLM Agents Embodying Different Personalities},
author = {Baiqiao Zhang and Xiangxian Li and Chao Zhou and Xinyu Gai and Juan Liu and Xue Yang and Nianlong Li and Shuai Ma and Xiaojuan Ma and Yong-jin Liu and Yulong Bian},
journal= {arXiv preprint arXiv:2507.04005},
year = {2026}
}