生成型人工智能的法律方面以及考试和论文中的大型语言模型
Maren März1, Monika Himmelbauer2, Kevin Boldt3
1Charité - University Medicine Berlin, AG Progress Test Medicine, Teaching Division, Berlin, Germany.
GMS journal for medical education
|October 17, 2024
概括
生成型人工智能 (AI) 和大型语言模型 (LLM) 为教育提供了新的可能性,但需要明确的大学框架. 法律考虑,包括版权和数据保护,对于负责任的AI整合到教学和评估中至关重要.
科学领域:
- 教育技术的教育技术
- 法律研究 法律研究
- 人工智能伦理学 人工智能伦理学
背景情况:
- 生成性AI和LLM在考试环境中表现出很高的表现,引发了关于它们的使用,影响和风险的辩论.
- 将法学士纳入高等教育需要对法律和道德方面的考虑进行彻底审查.
研究的目的:
- 确定在教学和评估中使用LLM的关键法律方面和可能性.
- 概述必要的大学框架,以便在教育环境中负责任地实施AI.
主要方法:
- 分析相关法规和法律,包括大学法规,州高等教育法规,版权法 (UrhG),一般数据保护法规 (GDPR) 和欧盟人工智能法.
- 审查人工智能在生成考试问题和抄袭潜力的能力.
主要成果:
- 法律学士学位提供了机会,但需要明确的大学指导方针来定义允许和禁止的用途.
- 欺骗和抄袭仍然是一个重大问题,人工智能生成的抄袭是可能的,很难检测到的.
- 虽然人工智能可以生成考试问题,但由于潜在的错误,人类审查至关重要. 对考试题的版权侵权风险很低 (高达15%的受保护作品).
- 基于计算机的评估需要人类监督;教育中的高风险人工智能应用将属于欧盟人工智能法案.
结论:
- 清晰的大学框架对于LLMs在教育中的伦理和法律使用至关重要.
- 调整评估标准和程序可以减轻作弊风险,LLM可以成为考试对象.
- 建议对人工智能进行教师培训,以有效地将LLMs作为教育中的补充工具进行整合.
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