Designing AI-Agents with Personalities: A Psychometric Approach

Understand

We introduce a methodology for assigning quantifiable and psychometrically validated personalities to AI-Agents using the Big Five framework.

• Across three studies, we evaluate its feasibility and limitations.
• In Study 1, we show that large language models (LLMs) capture semantic similarities among Big Five measures, providing a basis for personality assignment.
• In Study 2, we create AI-Agents using prompts designed based on the Big Five Inventory-2 (BFI-2) in different format, and find that AI-Agents powered by new models align more closely with human responses on the Mini-Markers test, although the finer pattern of results (e.g., factor loading patterns) were sometimes inconsistent.