使用对话式人工智能来减少对科学的怀疑
Matthew J Hornsey1, Aimee E Smith1, Samuel Pearson1
1The University of Queensland, Queensland, Australia.
Current opinion in psychology
|November 27, 2025
概括
大型语言模型 (LLM) 在打击科学错误信息方面表现有前途. 研究表明,与LLM的对话通常会减少怀疑和对错误信息的认可,帮助科学传播.
科学领域:
- 科学传播是科学传播.
- 人工智能的人工智能
- 公众对科学的信任.
背景情况:
- 在气候变化和疫苗接种等关键问题上对科学共识的广泛不信任阻碍了全球威胁应对.
- 包括大型语言模型 (LLM) 在内的生成人工智能存在大规模传播错误信息的风险.
- 此外,LLM还为减少错误信息和阴谋论提供了机会,有可能改变科学传播.
研究的目的:
- 审查使用对话式人工智能 (如ChatGPT) 的实证研究,以解决对科学的不信任.
- 评估LLMs在消除有关有争议的科学话题的怀疑主义方面的潜力.
主要方法:
- 总结当前关于对话式人工智能和科学交流的实证研究格局.
- 分析研究的分析,调查用户和LLMs在科学主题上的互动.
主要成果:
- 经验研究显示,对LLM复制阴谋论或错误信息的证据微不足道.
- 与LLM的对话通常会减少参与者的科学怀疑和对错误信息的信念.
- 士互动往往会促进对科学共识的更大信任.
结论:
- 目前的LLM显示出作为一种提高科学传播策略的工具的潜力.
- 保护措施是必要的,以保持信息完整性和公众的信任,当使用LLMs.
- 在努力重建公众对科学的信任方面,LLM可以成为一个有价值的补充.
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