Patrick M. Markey, Hanna Campbell, Samantha Goldman
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引用次数: 0
Abstract
This study explored using Large Language Models (LLMs) in early personality test construction, presenting a method to efficiently assess item relevance to psychological constructs. Study 1 generated self-esteem and Five-Factor Model (FFM) scales by analyzing AI-agent responses, resulting in scales with high internal consistency and face validity. Study 2 tested these scales with 449 human participants, finding that the AI-created self-esteem scale showed satisfactory internal consistency and strong correlations with the Rosenberg Self-Esteem Scale. The AI-created FFM scales demonstrated satisfactory internal consistency, convergent and divergent validity with the NEO Personality Inventory-Revised, and similar correlational patterns, though with some discrepancies in Agreeableness. These findings suggest LLMs can streamline item selection in personality test development.
期刊介绍:
Emphasizing experimental and descriptive research, the Journal of Research in Personality presents articles that examine important issues in the field of personality and in related fields basic to the understanding of personality. The subject matter includes treatments of genetic, physiological, motivational, learning, perceptual, cognitive, and social processes of both normal and abnormal kinds in human and animal subjects. Features: • Papers that present integrated sets of studies that address significant theoretical issues relating to personality. • Theoretical papers and critical reviews of current experimental and methodological interest. • Single, well-designed studies of an innovative nature. • Brief reports, including replication or null result studies of previously reported findings, or a well-designed studies addressing questions of limited scope.