ArticleBehavior research methods2026
Generative psychometrics via AI-GENIE: Automatic item generation and validation with network-integrated evaluation.
Article in Behavior research methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- The trust in AI-generated health advice (TAIGHA) scale and short version (TAIGHA-S): Development and validation study.PLOS digital health · 2026Article
- Article
- SEMbeddings: how to evaluate model misfit before data collection using large-language models.Frontiers in psychology · 2024Article
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Authors and funding
3 authors.
Funding
Abstract
The rapid advancement of artificial intelligence (AI), particularly large language models (LLMs), has introduced powerful tools for various research domains, including psychological scale development. This study presents a methodology for efficiently generating and selecting high-quality, non-redundant items for psychological assessments using LLMs and network psychometrics. Our approach, termed Automatic Item Generation and Validation with Network-Integrated Evaluation (AI-GENIE), reduces reliance on expert intervention by integrating generative AI with the latest network psychometric techniques. The efficacy of AI-GENIE was evaluated through Monte Carlo simulations using the Mixtral, Gemma 2, Llama 3, GPT-3.5, and GPT-4o models to generate item pools that mimic Big Five personality assessments. Additionally, items from AI-GENIE were empirically tested with five nationally representative U.S. samples (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.