使用生成人工智能推进假设驱动的规模验证:识别标准措施并生成精确的先验假设.
Kyle D Austin1, Hannah K Crawley1, William Fleeson1
1Wake Forest University, Winston-Salem, NC, USA.
Assessment
|December 29, 2025
概括
人工智能 (AI) 可以为规模验证生成精确的有效性假设,匹配专家的准确性. 这种方法有效地增强了心理尺度的发展.
科学领域:
- 心理测量 心理测量
- 研究中的人工智能.
- 量化心理学 量化心理学
背景情况:
- 规模验证对于心理学研究至关重要.
- 开发精确的有效性假设是一个关键步骤.
- 目前的方法可能耗时,需要专家的意见.
研究的目的:
- 评估人工智能 (AI) 用于基于假设的规模验证.
- 评估AI产生心理上合理的有效性假设的能力.
- 将人工智能生成的假设与专家预测进行比较.
主要方法:
- 人工智能对规模验证标准的建议的定性评估.
- 使用现有数据对人工智能产生的有效性假设进行定量评估.
- 人工智能 (ChatGPT,Gemini) 假设一致性和准确性与专家预测在九个尺度/子尺度之间进行比较.
主要成果:
- 人工智能为规模验证标准提供了有用的建议.
- 人工智能生成的假设显示出高的试验间一致性,可与专家评审者间一致性相比较.
- 人工智能假设与专家假设有很强的一致性,在预测有效性相关性方面具有类似的准确性.
结论:
- 包括ChatGPT和Gemini在内的人工智能可以有效地促进对融合和歧视有效性的假设生成.
- 人工智能为规模验证提供了一种节省时间的方法,而不会损害心理或心理测量质量.
- 人工智能在推进严格的,基于假设的规模验证实践方面表现有前途.
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