Legal aspects of generative artificial intelligence and large language models in examinations and theses

被引:0
|
作者
Maerz, Maren [1 ]
Himmelbauer, Monika [2 ]
Boldt, Kevin [3 ]
Oksche, Alexander [4 ,5 ]
机构
[1] Charite Univ Med Berlin, AG Progress Test Med, Teaching Div, Charitepl 1, D-10117 Berlin, Germany
[2] Med Univ Vienna, Teaching Ctr, Vienna, Austria
[3] State Commissioner Data Protect & Freedom Informat, Mainz, Germany
[4] Inst Med & Pharmazeut Prufungsfragen IMPP, Mainz, Germany
[5] Justus Liebig Univ Giessen, Rudolf Buchheim Inst Pharmacol, Giessen, Germany
来源
GMS JOURNAL FOR MEDICAL EDUCATION | 2024年 / 41卷 / 04期
关键词
assessment; AI; large language models; legal framework; PERFORMANCE; CHATGPT;
D O I
10.3205/zma001702
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
The high performance of generative artificial intelligence (AI) and large language models (LLM) in examination contexts has triggered an intense debate about their applications, effects and risks. What legal aspects need to be considered when using LLM in teaching and assessment? What possibilities do language models offer? Statutes and laws are used to assess the use of LLM: - University statutes, state higher education laws, licensing regulations for doctors - Copyright Act (UrhG) - General Data Protection Regulation (DGPR) - AI Regulation (EU AI Act) LLM and AI offer opportunities but require clear university frameworks. These should define legitimate uses and areas where use is prohibited. Cheating and plagiarism violate good scientific practice and copyright laws. Cheating is difficult to detect. Plagiarism by AI is possible. Users of the products are responsible. LLM are effective tools for generating exam questions. Nevertheless, careful review is necessary as even apparently high-quality products may contain errors. However, the risk of copyright infringement with AI- generated exam questions is low, as copyright law allows up to 15% of protected works to be used for teaching and exams. The grading of exam content is subject to higher education laws and regulations and the GDPR. Exclusively computer-based assessment without human review is not permitted. For high-risk applications in education, the EU's AI Regulation will apply in the future. When dealing with LLM in assessments, evaluation criteria for existing assessments can be adapted, as can assessment programmes, e.g. to reduce the motivation to cheat. LLM can also become the subject of the examination themselves. Teachers should undergo further training in AI and consider LLM as an addition.
引用
收藏
页数:14
相关论文
共 50 条
  • [1] Medical education empowered by generative artificial intelligence large language models
    Jowsey, Tanisha
    Stokes-Parish, Jessica
    Singleton, Rachelle
    Todorovic, Michael
    [J]. TRENDS IN MOLECULAR MEDICINE, 2023, 29 (12) : 971 - 973
  • [2] Clinical Science and Practice in the Age of Large Language Models and Generative Artificial Intelligence
    Schueller, Stephen M.
    Morris, Robert R.
    [J]. JOURNAL OF CONSULTING AND CLINICAL PSYCHOLOGY, 2023, 91 (10) : 559 - 561
  • [3] A Generative Artificial Intelligence Using Multilingual Large Language Models for ChatGPT Applications
    Tuan, Nguyen Trung
    Moore, Philip
    Thanh, Dat Ha Vu
    Pham, Hai Van
    [J]. APPLIED SCIENCES-BASEL, 2024, 14 (07):
  • [4] Integrating large language models and generative artificial intelligence tools into information literacy instruction
    Carroll, Alexander J.
    Borycz, Joshua
    [J]. JOURNAL OF ACADEMIC LIBRARIANSHIP, 2024, 50 (04):
  • [5] GenAI against humanity: nefarious applications of generative artificial intelligence and large language models
    Ferrara, Emilio
    [J]. JOURNAL OF COMPUTATIONAL SOCIAL SCIENCE, 2024, 7 (01): : 549 - 569
  • [6] The academic industry's response to generative artificial intelligence: An institutional analysis of large language models
    Kshetri, Nir
    [J]. TELECOMMUNICATIONS POLICY, 2024, 48 (05)
  • [7] Updated Primer on Generative Artificial Intelligence and Large Language Models in Medical Imaging for Medical Professionals
    Kim, Kiduk
    Cho, Kyungjin
    Jang, Ryoungwoo
    Kyung, Sunggu
    Lee, Soyoung
    Ham, Sungwon
    Choi, Edward
    Hong, Gil-Sun
    Kim, Namkug
    [J]. KOREAN JOURNAL OF RADIOLOGY, 2024, 25 (03) : 224 - 242
  • [8] Artificial intelligence, large language models, and you
    Marquardt, Charles
    [J]. JOURNAL OF VASCULAR SURGERY CASES INNOVATIONS AND TECHNIQUES, 2023, 9 (04):
  • [9] LEGAL IMPLICATIONS OF WEB SCRAPING IN THE TRAINING OF GENERATIVE ARTIFICIAL INTELLIGENCE MODELS
    Chaparro, Juan Manuel Pacheco
    Ramirez, Laura Barrero
    [J]. REVISTA LA PROPIEDAD INMATERIAL, 2024, (38): : 167 - 189
  • [10] GPT, large language models (LLMs) and generative artificial intelligence (GAI) models in geospatial science: a systematic review
    Wang, Siqin
    Hu, Tao
    Xiao, Huang
    Li, Yun
    Zhang, Ce
    Ning, Huan
    Zhu, Rui
    Li, Zhenlong
    Ye, Xinyue
    [J]. INTERNATIONAL JOURNAL OF DIGITAL EARTH, 2024, 17 (01)