AI-assisted item development evaluators are not neutral, study finds

A new arXiv paper investigates the critical role of computational evaluators in AI-assisted item development, particularly in fields like psychometrics. The study, which analyzed 32,000 Big Five items, found that representation and structural screening significantly influence which items reach expert review. Despite apparent stability in global summaries, the specific content presented to psychometricians varied considerably based on embedding configurations and selection policies, highlighting that these evaluators are integral parts of the measurement design process, not neutral intermediaries. AI

影响 Highlights the critical, non-neutral role of AI evaluators in content generation, impacting how AI-assisted research and development are designed.

排序理由 The item is an academic paper published on arXiv detailing research findings. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

• alphaXiv
• arXiv
• Big Tech
• CatalyzeX
• CORE Recommender
• DagsHub
• Gotit.pub
• Hugging Face
• Influence Flower
• ScienceCast

• 论文
• 其他

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →