¿Puede la IA identificar los rasgos dominantes de personalidad de una persona a partir de una muestra de escritura de 30 segundos con una precisión comparable a la de psicólogos capacitados ?

Los modelos de lenguaje grande analizan patrones lingüísticos para inferir rasgos de Myers-Briggs o Big Five. Estudios muestran una fuerte correlación con rasgos autoinformados y valoraciones de observadores. La precisión mejora cuando aumenta la longitud del texto.

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