概括
这项研究引入了生成性AI软件,用于自动化,定制化对文本答案进行分级,大大减少了教育和研究中的评估时间和资源. 该工具确保了高可靠性,帮助教育工作者和研究人员有效评估学生的反应.
科学领域:
- 教育技术的教育技术
- 教育中的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 在教育和行为研究中,基于文本的答案的自动评估是劳动密集型的.
- 现有的AI工具缺乏针对大数据集的具体案例评估的定制功能.
- 需要有效的,可扩展的解决方案,以基于文本的答案得分.
研究的目的:
- 开发和验证一个灵活的软件和Web应用程序,用于基于文本的答案的自动化,具体的案例分级.
- 利用尖端的人工智能,特别是大型语言模型 (LLM),进行增强的答案评估.
- 为研究人员和教育工作者提供一个易于使用的工具,用于处理大量的学生回复.
主要方法:
- 开发一个集成LLM的软件和Web应用程序,具有针对问题的具体情况,样本解决方案和评估标准的可定制参数.
- 实现一个界面,以指定根据个人研究或教育背景量身定制的分级指令.
- 通过实证研究验证自动化评分方法,将AI评级与专家评估进行比较.
主要成果:
- 在经验研究中,与专家评级相比,开发的软件显示出高可靠性.
- 该系统可对大型基于文本的答案数据集进行可定制和具体案例的自动分级.
- 该软件的开源性质有利于用户直接实施和适应.
结论:
- 增强人工智能的生成软件为促进和改进基于文本的答案评估提供了有价值的工具.
- 该应用程序解决了当前人工智能实现的局限性,允许自动化,对众多学生答案的具体案例评分.
- 这种开源解决方案使教育工作者和研究人员能够使用高效和适应性的人工智能技术进行评估.
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