关于大型多式联络基础模型在教育中的机遇和挑战
Stefan Küchemann1, Karina E Avila2, Yavuz Dinc2
1Chair of Physics Education, Faculty of Physics, Ludwig-Maximilians-Universität München, Munich, Germany. s.kuechemann@lmu.de.
大型多式联网基础模型可以处理各种数据类型,如文本,音频和视频. 本概述探讨了这些先进的人工智能模型所带来的新的教育机会和挑战.
科学领域:
- 人工智能的人工智能
- 教育技术的教育技术
- 计算机科学 计算机科学
背景情况:
- 大型语言模型 (LLM) 现在充当中间件,连接各种AI工具.
- 这种整合刺激了大型多式联运基础模型 (LMFMs) 的创建.
- LMFM具有处理各种数据格式的能力,包括口语文本,音乐,图像和视频.
研究的目的:
- 为大型多式联运基础模型的新兴景观提供概述.
- 识别和解释LMFMs在教育环境中提供的新机会.
- 突出与将LMFMs纳入教育相关的挑战.
主要方法:
- 对人工智能和法学士近期进展的文献综述.
- 分析大型多式联运基础模型的能力.
- 对教育的潜在应用和影响的综合分析.
主要成果:
- LMFMs提供了用于处理各种数据类型 (文本,音频,视觉) 的高级功能.
- 对于提高教育工具和方法的发展,存在显著的机会.
- 整合带来了与实施,道德和教学相关的挑战.
结论:
- 大型多式联运基础模型代表了人工智能的重大进步.
- 他们融入教育提供了变革的潜力,但需要仔细考虑相关的挑战.
- 需要进一步的研究,以充分理解和利用LMFMs在教育背景下.
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