为了提高识字率,提高教育中的平等,而不是大型语言模型
Samuel H Forbes1, Olivia Guest2,3
1Department of Psychology, Durham University.
Cognitive science
|April 1, 2025
概括
由于经济和环境成本,大型语言模型 (LLM) 可能无法解决认知能力下降的问题. 我们敦促谨慎,因为LLM可能会损害隐私,减少人与人之间的互动,并对认知产生负面影响.
科学领域:
- 认知科学 认知科学
- 教育技术的教育技术
- 环境科学 环境科学
背景情况:
- 大型语言模型 (LLM) 是一种解决认知能力下降和教育不平等问题的方案.
- 有关在教育中广泛采用LLM的可行性和潜在负面后果存在担忧.
研究的目的:
- 批判性地评估LLM对认知能力的建议好处,并对其潜在的危害进行评估.
- 警告技术解决主义,并倡导对教育技术进行更全面的评估.
主要方法:
- 对Huettig和Christiansen关于LLMs.所提出的要求进行批判性分析.
- 检查LLM技术的经济,环境和认知影响.
主要成果:
- 法律法规是资源密集型的,经济上具有挑战性,对环境有害.
- 潜在的负面影响包括侵犯隐私,减少人与人之间的互动,教师失业,以及影响认知的环境污染.
结论:
- 法律法规的潜在危害,包括环境退化和认知损害,可能会超过其所谓的好处.
- 在将LLMs整合到教育环境中时,建议非常谨慎,从历史的工业伤害中学习.
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