一个生成的人工智能驱动的交互式倾听评估任务
Andrew Runge1, Yigal Attali1, Geoffrey T LaFlair1
1Duolingo, Pittsburgh, PA, United States.
Frontiers in artificial intelligence
|November 19, 2024
概括
大型语言模型允许自动生成项目,用于交互式语言评估. 这种人工智能驱动的方法在规模上创造了复杂的任务,改善了在英语水平测试中交互能力的评估.
科学领域:
- 自然语言处理自然语言处理.
- 教育技术的教育技术
- 教育中的人工智能
背景情况:
- 传统的语言评估很难在规模上评估交互能力.
- 自动化评估往往缺乏复杂性来捕捉真实的对话能力.
- 现有的数字格式具有从纸质任务中继承的局限性.
研究的目的:
- 描述使用生成人工智能来创建大规模的交互式听力任务.
- 加强在英语能力测试中对交互能力的评估.
- 开发更真实的自动测量听力能力.
主要方法:
- 利用大型语言模型进行自动化项目生成 (AIG).
- 开发了Duolingo英语测试 (DET) 的交互式听力任务.
- 采用了人工智能驱动的评估创建的人在循环系统.
主要成果:
- 使用生成性AI成功生成了713个复杂的任务.
- 试点结果表明,大规模创建教育评估的可行性.
- 人类审查证实了人工智能驱动方法的有效性.
结论:
- 生成型人工智能为创建复杂,交互式语言评估任务提供了可扩展的解决方案.
- 人工智能驱动的方法可以克服传统和现有的自动化评估的局限性.
- 这种方法支持对交互能力的更丰富,更真实的评估.
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