心にあることを言えば、AIはあなたがどんな人間かを知ることができます(Say what’s on your mind, and AI can tell what kind of person you are)

2026-01-30  ミシガン大学

新しい研究で、一般に利用可能な生成AI(例:ChatGPT、Claude、LLaMaなど)が、短い言葉や日常の気持ちの記述から個人の性格や行動傾向、日々の感情を予測できることが示された。これは、友人や家族の評価と同等か、それ以上の精度である可能性があり、自己理解や心理的特性の把握に役立つ。研究では160人以上の実データ(ビデオ日記や思考の記録)をAIに読み込ませ、各被験者の性格尺度をAIが推定した結果、人間の自己評価と非常に近く一致した。従来の古いテキスト解析法よりも、最新の生成AIシステムが優れた予測精度を示し、AIを新たな「人格判定者」として活用する可能性が示唆される。

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短い自由記述テキストのゼロショット生成AIスコアリングを用いた性格評価 Assessing personality using zero-shot generative AI scoring of brief open-ended text

Aidan G. C. Wright,Whitney R. Ringwald,Colin E. Vize,Johannes C. Eichstaedt,Mike Angstadt,Aman Taxali & Chandra Sripada
Nature Human Behaviour  Published:30 January 2026
DOI:https://doi.org/10.1038/s41562-025-02389-x

心にあることを言えば、AIはあなたがどんな人間かを知ることができます(Say what’s on your mind, and AI can tell what kind of person you are)
Fig. 1: Diagram of procedures across samples.

Abstract

Contemporary personality assessment relies heavily on psychometric scales, which offer efficiency but risk oversimplifying the rich and contextual nature of personality. Recognizing these limitations, this study explores the use of commercially available generative large language models (LLMs), such as ChatGPT, Claude and so on, to assess personality traits from open-ended qualitative narratives. Across two distinct samples and methodologies (spontaneous streams of thought and daily video diaries), we used seven commercial, generative LLMs to score Big-Five personality traits, achieving convergence with self-report measures comparable to or exceeding established benchmarks (for example, self–other agreement, ecological momentary assessment, and bespoke machine learning models). Although results differed across different LLMs, we found that using the average LLM score across models provided the strongest agreement with self-report. Further, LLM-generated trait scores also demonstrated predictive validity regarding daily behaviours and mental health outcomes. This LLM-based approach achieved quantitative rigour based on qualitative data and is easily accessible without specialized training. Importantly, our findings also reaffirm that personality is expressed ubiquitously, in that it is carried in the stream of our thoughts and is woven into the fabric of our daily lives. These results encourage broader adoption of generative LLMs for psychological assessment and—given the new generation of tools—stress the value of idiographic narratives as reliable sources of psychological insight.

1603情報システム・データ工学
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