积极推断进入学校:在大型语言模型时代,积极学习的重要性
Laura Desirèe Di Paolo1,2, Ben White3, Avel Guénin-Carlut1
1Department of Engineering and Informatics, The University of Sussex , Brighton, UK.
概括
人类的学习是有形的,但生成人工智能 (AI) 是无形的. 我们探索将人工智能整合到教育实践中,将学习视为认识系统的食和预测错误最小化.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 教育教育教育教育教育教育.
背景情况:
- 人类的学习基本上是体现的,依赖于与世界的物理相互作用.
- 无形生成型人工智能 (AI) 的兴起为教育带来了新的挑战和机遇.
- 了解如何将AI整合到学习环境中至关重要.
研究的目的:
- 探索生成性AI如何被理解和纳入教育实践.
- 调查鼓励在准备好的学习环境中与人工智能体现互动的方法.
- 提出一个框架,在体内和无体内智能之间进行协同学习.
主要方法:
- 利用主动推理框架来建模人类的学习.
- 将学习概念化为认识论的食和预测错误最小化.
- 检查蒙特利教育作为准备学习环境的模型.
主要成果:
- 生成性AI可以集成到准备好的学习环境中.
- 人工智能可以促进精确的预测错误,使学习过程中的自我纠正成为可能.
- 在有形和无形的智能形式之间可以出现新的协同效应.
结论:
- 生成型人工智能为增强体现式学习过程提供了新的机会.
- 人工智能可以作为自我纠正和知识获取的催化剂.
- 这项研究预示着,在未来,有形智能和人工智能将在教育领域进行合作.
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