Evidence map›Paper›PMID 40551914›Full record

ArticleCureus2025

Large Language Models Demonstrate Distinct Personality Profiles.

Thomas F Heston, Justin Gillette

Abstract read
In one paragraph

Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Thomas F HestonMedical Education and Clinical Sciences, Washington State University, Spokane, USA.
Justin GilletteMedical Education and Clinical Sciences, Washington State University, Spokane, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionLarge language models (LLMs) are increasingly used in clinical medicine to provide emotional support, deliver cognitive-behavioral therapy, and assist in triage and diagnosis. However, as LLMs are integrated into mental health applications, assessing their personality expression and potential divergence from expected neutrality is critical for ensuring clinical safety and therapeutic appropriateness. This study provides the first psychometric analysis of LLM personality, specifically within a medical context, characterizing personality profiles using two validated frameworks: the Open Extended Jungian Type Scales (OEJTS) and the Big Five Personality Test.

methodsFour leading LLMs publicly available in April 2024 (ChatGPT-3.5 (OpenAI, San Francisco, CA, USA), Gemini Advanced (Google Inc., Mountain View, CA, USA), Claude 3 Opus (Anthropic, San Francisco, CA, USA), and Grok-Regular Mode (xAI, Palo Alto, CA, USA)) were evaluated across both psychometric instruments. All tests were administered in a new chat session to prevent memory carryover. A one-way multivariate analysis of variance (MANOVA) was performed to assess inter-model differences in personality profiles.

resultsMANOVA demonstrated statistically significant differences across models in typological and dimensional personality traits (Wilks' Lambda = 0.115, p < 0.001). OEJTS results showed ChatGPT-3.5 most often classified as Extraverted, Intuitive, Thinking, and Judging (ENTJ) and Claude 3 Opus consistently as Introverted, Intuitive, Thinking, and Judging (INTJ), while Gemini Advanced and Grok-Regular leaned toward Introverted, Intuitive, Feeling, Judging (INFJ). On the Big Five Personality Test, Gemini scored markedly lower on agreeableness and conscientiousness, while Claude scored highest on conscientiousness and emotional stability. Grok-Regular exhibited high openness but more variability in stability. Effect sizes ranged from moderate to large across traits.  Conclusion: Distinct personality profiles are consistently expressed across different LLMs, even in unprompted conditions. Given the increasing integration of LLMs into clinical workflows, these findings underscore the need for formal personality evaluation and oversight involving mental health professionals before deployment.

Indexed as

ai ethicsai in mental healthai psychometricsartificial intelligence in medicinegenerative ai

Identifiers

PMID40551914
PMCPMC12183331

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.