From human artefact to machine output: automating the “art” of psychological measurement

Creating psychological assessment tools is crucial for research but traditionally expensive and time-consuming. While Large Language Models (LLMs) show promise for automating this process, existing approaches lack systematic, user-friendly methodologies grounded in psychometric principles. This study presents an enhanced Psychometric Item Generator (PIG) method using conversational LLMs with Problem-Solving Plans (PSP) and Chain-of-Thought (CoT) prompting. Three demonstrations validated the approach: Gemini 1.5 Flash generated 20 “propensity to trust AI” items with strong semantic coherence; Claude 3 Opus created 20 “AI anxiety” items that outperformed human-generated versions linguistically; and a 6-item “AI adoption in online learning” scale was developed and validated with 1,233 participants using multiverse analysis. Results demonstrate that LLMs can produce psychometrically sound items. The AI-generated anxiety scale showed superior linguistic properties compared to human alternatives, while the learning scale exhibited good internal consistency, item homogeneity, and clear two-factor structure across multiple analytical teams. The study establishes a PSP-CoT framework that improves LLM output quality, offering researchers a cost-effective, accessible scale development methodology. However, findings emphasize that human oversight, rigorous validation, and ethical considerations remain essential components of the process.