生成型人工智能模型在分歧和收思维评估中表现优于学生
Vikram Arora1,2, Alex Thabane3, Sameer Parpia3
1Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada. v.arora@mail.utoronto.ca.
与人类相比,生成人工智能 (GenAI) 模型在分歧和融合的思维任务上表现出更高的创造力. 最先进的AI聊天机器人,如ChatGPT-4o,DeepSeek-V3和Gemini 2.0,在原创性和解决问题的评估中表现优于人类参与者.
科学领域:
- 认知心理学 认知心理学
- 人工智能的人工智能
- 创造力研究 创造力研究
背景情况:
- 生成型人工智能 (GenAI) 越来越多地用于创意领域,促使人们对其创意能力进行科学调查.
- 以前的研究已经评估了GenAI的创造力,但缺乏与人类在分歧和融合思维方面的直接比较.
- 这项研究解决了理解GenAI与人类基准相比创造性表现的差距.
研究的目的:
- 为了比较人类的创造能力和最先进的GenAI模型.
- 评估对分歧和收思维任务的表现.
- 评估GenAI目前创造力评估方法的有效性.
主要方法:
- 人类参与者 (n=46) 与三个先进的GenAI聊天机器人进行了比较:ChatGPT-4o,DeepSeek-V3和Gemini 2.0.0.
- 替代用途任务 (AUT) 用于测量分歧思维 ("平均"和"最佳"想法的原创性).
- 远程协同测试 (RAT) 用于评估融合思维 (57项表现).
主要成果:
- 所有的GenAI模型在分歧和趋同的思维任务上都显著超过了人类参与者.
- 基因人工智能产生的想法显示出更高的原创性 ("平均"和"最佳"),而不是AUT上的人类想法.
- 与人类相比,GenAI模型在RAT上表现优异,ChatGPT-4o在AI模型中获得最高分.
结论:
- 当前最先进的GenAI模型表现出了非凡的创造性潜力,在关键的认知任务中超过了人类的表现.
- 这些发现表明,现有的创造力评估方法可能需要重新评估它们在研究AI创造力方面的适用性.
- 需要进一步的研究来完善和开发适当的框架来评估人工智能的创造力.
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