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相关概念视频

Learning Disabilities01:25

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Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
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整体学习者支持的大型语言模型:机遇和挑战

Amogh Mannekote1, Adam Davies2, Juan D Pinto3

  • 1Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, United States.

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大型语言模型 (LLM) 可以通过模拟认知和非认知特征来个性化教育. 关键的挑战包括LLM的解释性,适应性技术的实施,以及为整个学习者提供支持的AI导师的创作.

关键词:
人工智能和教育教育授权工具教育授权工具可以解释的解释性.大型语言模型 (LLM)学习的非认知方面的学习.为学生提供教学支持.

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科学领域:

  • 教育中的人工智能
  • 教育技术的教育技术
  • 认知科学 认知科学

背景情况:

  • 大型语言模型 (LLM) 正在迅速发展,并被整合到教育环境中.
  • 针对"整体学习者"的个性化学习环境仍然是一个公开的挑战.
  • 现有的教育技术往往不能完全解决学生的认知和非认知特征.

研究的目的:

  • 探索LLMs在创造个性化学习环境方面的潜力.
  • 识别和应对利用LLM为整个学习者提供支持的关键挑战.
  • 概述AI导师的愿景,以适应学生个体需求.

主要方法:

  • 讨论改善关于学习者表现的LLM解释性的方法.
  • 通过LLM洞察力,检查适应性技术,以提供量身定制的教学支持.
  • 强调在创作和评估基于LLM的教育代理人的方法和挑战.

主要成果:

  • 通过分析学习者的内部表征,可以提高LLM的解释性.
  • 适应性LLM技术可以提供上下文感知反和支架非认知技能.
  • 自然语言指令为指定AI导师行为提供了机会和挑战.

结论:

  • 解决可解释性,适应性和创作挑战对于有效的LLM教育应用至关重要.
  • 个性化的AI导师可以通过考虑各种学生特征来显著提高学习效率.
  • 未来的工作应该集中在开发强大的和可解释的LLM基于教育代理.