Kan AI de dominante persoonlijkheidskenmerken van een persoon identificeren op basis van een 30-seconden schrijfmonster met een nauwkeurigheid die gelijkstaat aan die van getrainde psychologen ?

Grote taalmodellen analyseren taalpatronen om Myers-Briggs- of Big Five-trekjes af te leiden. Onderzoeken tonen een sterke correlatie met zelfgerapporteerde trekjes en observatorbeoordelingen. De nauwkeurigheid neemt toe naarmate de tekstlengte groter wordt.

Background

Large language models analyze language patterns to infer Myers-Briggs or Big Five traits. Studies show strong correlation with self-reported traits and observer ratings. Accuracy improves when text length increases.

--- Current AI systems can infer broad personality traits such as the Big Five from brief text samples, and in some studies they match or exceed the accuracy of human experts when predicting traits like neuroticism, conscientiousness, or extraversion on samples as short as a few sentences. Techniques typically combine large language models fine-tuned on personality-annotated corpora with psycholinguistic features like LIWC categories, achieving around 0.3–0.4 correlation with ground-truth scales—comparable to inter-rater reliability between trained psychologists. However, these models rely on self-report questionnaires for training labels, which may not capture unconscious or context-sensitive traits, and performance drops when the writing sample contains atypical vocabulary, sarcasm, or cultural references not well represented in the training data. Ethical and privacy concerns also limit real-world deployment without explicit consent and robust safeguards.

— Enriched May 12, 2026 · Source: Matz et al., “Deep learning reveals predictive models of human language for personality assessment,” PNAS Nexus, 2023