大型语言模型在教育中的不可避免和不可预测的作用:对Huettig和Christiansen的评论 (2024)
Ernesto Guerra1,2, Marcela Peña3,4, Roberto Araya1,2
1Institute of Education, Universidad de Chile.
Cognitive science
|August 13, 2025
概括
大型语言模型 (LLM) 提供了教育效益,但可能会降低文本参与度,可能会损害年轻学习者的识字技能. 需要谨慎的教学策略来平衡LLM的使用与基本的阅读实践.
科学领域:
- 认知科学 认知科学
- 教育技术的教育技术.
- 应用语言学 应用语言学
背景情况:
- 识字率的下降是一个重大的教育挑战.
- 大型语言模型 (LLM) 被提出作为一种支持识字的工具.
- 法律士整合对阅读技能的认知影响需要仔细检查.
研究的目的:
- 批判性地评估在教育中使用LLMs的潜在权衡.
- 探索LLM设计对用户与书面文本直接互动的影响.
- 确定开发有效教学干预措施所需的考虑因素.
主要方法:
- 这是一个评论,而不是经验研究;它综合了现有的知识和理论观点.
- 对LLMs在认知科学中用于识字的建议使用的批判性分析.
- 讨论潜在的认知后果和教学影响.
主要成果:
- 无意中,LLM可能会减少与书面文本的直接接触.
- 这种较低的参与度对更深层次的识字能力的发展构成风险,特别是在年轻学习者中.
- 在识字教育中采用LLM的潜在好处和风险之间存在着显著的紧张关系.
结论:
- 在教学上,合理的干预需要承认LLM实用性和直接文本参与之间的权衡.
- 认知科学家和教育工作者必须合作,以预测认知技能减弱.
- 教学策略应该旨在在新兴的多式联络环境中,保持与书面语言持续互动的认知益处.
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