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.
Frontiers in artificial intelligence
|October 30, 2024
概括
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.
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.
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