聊天GPT事实检查:利用生成AI的局限性,在大学科学课程中开发基于证据的推理技能
Ursula Holzmann1, Sulekha Anand1, Alexander Y Payumo1
1Department of Biological SciencesSan Jose State UniversitySan JoseCaliforniaUnited States.
Advances in physiology education
|January 17, 2025
概括
大学科学学生可以使用生成人工智能 (AI) 工具进行主题探索. 一个新的任务教学生批判性地评估人工智能产生的信息,确保基于证据的怀疑和负责任的AI在教育中的使用.
科学领域:
- 教育技术的教育技术
- 人工智能在科学教育教育中的作用
背景情况:
- 像ChatGPT这样的生成性大语言模型 (LLM) 可以产生信息内容,但通常包含事实错误.
- 人工智能产生的信息的不可靠性对其在学术环境中的使用提出了挑战,特别是在科学教育中.
研究的目的:
- 为大学科学课程引入一个可适应的作业,即ChatGPT事实检查.
- 教学生利用LLM进行主题探索,同时强调基于证据的AI产生的索赔的验证.
主要方法:
- 学生利用ChatGPT生成科学主题的论文.
- 学生批判性地评估人工智能产生的来源,并评估科学主张与初级实验证据的有效性.
- 该作业侧重于负责任的人工智能使用,区分证据类型和基于证据的索赔评估.
主要成果:
- 该作业有效地教导学生平衡人工智能对探索的好处与必要的怀疑.
- 学生通过评估人工智能生成的科学信息来培养批判性思维能力.
- 该活动加强了基于证据的评估在面对人工智能产生的内容的重要性.
结论:
- 聊天GPT事实检查任务是科学课程的一个可适应的工具,以促进负责任的AI使用.
- 它为学生提供了必要的技能,以批判性地参与人工智能生成的科学信息,促进基于证据的学习.
- 这种方法利用AI的能力,同时减轻与其不准确性相关的风险.
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