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  7. Lecturers - AI labeling

For lecturers

AI in academic writing processes

On this page, you will find information and guidelines on how to disclose and document the use of AI technologies in academic writing and research.

 

Preliminary Notes

  • The information on this page serves as a suggestion and orientation. We would like to support you in deciding how AI use can be indicated and documented in your modules, especially in text production processes.
  • We recommend that you engage in dialogue with your students and, if possible, proactively indicate how students can or cannot use AI in your courses.
  • You can also use these text modules, e.g. for AULIS or your syllabus.

- As of March 17, 2025 -

General Information

Since generative AI technologies have automated text production and are changing common writing and reading practices in the academic sphere, the question of how to disclose and document AI usage in scientific writing and research processes has become a topic of discussion.

While certain trends are emerging in terms of how to disclose and document AI usage in academic writing, there is still no universal agreement across disciplines on the best approach. Instead, there are currently only guidelines specific to individual fields.

The following information is not intended to be a definitive or binding set of rules. Instead, it aims to provide a starting point for discussion between teachers and students, help reduce uncertainty, and offer a framework for implementing AI disclosure and documentation in academic writing.

    • Science relies on verifiability. However, outputs from GenAI do not have the status of a source that is readable and verifiable by third parties, as the output for a given prompt is only valid for that specific prompt in combination with the corresponding version of the tool, license, and potentially even the time, region of access, etc.

     

    To explain:

    • AI-generated texts use Large Language Models (LLM), which are trained to recognize patterns in natural language and reproduce them accordingly. These Large Language Models (e.g. GPT-3.5, GPT-4  for OpenAI ChatGPT and CoPilot, Claude from Anthropic, Gemini Nano from Google, etc.) generate text outputs based on complex neural networks and statistical probabilities.
    • They are trained on very large amounts of data. However, the dataset used for training a particular application is either a trade secret (e.g. Claude) or consists mainly of a Common Crawl, a specially compiled database of web texts, English Wikipedia, and freely available books (see for GPT-3 OpenAI July 2020: https://arxiv.org/pdf/2005.14165).
  • Texts created by GenAI have remarkable linguistic quality in most cases. However, high linguistic quality does not necessarily mean high content quality or accuracy. Even if texts are formulated in a complex way and sound credible, GenAI has a problem with factual accuracy.

    Because GenAI does not generate content based on specific sources (such as databases, books, etc.), it instead relies on statistical patterns to predict the most likely next element (character, word) in a response. This process creates linguistically coherent texts, but it does not guarantee factual accuracy.
    GenAI systems may sometimes "hallucinate" — that is, generate plausible-sounding answers that are not factually correct. Additionally, repeated queries may produce varying responses each time, as the output is influenced by probabilistic modeling.

    The verification of accuracy, i.e. factual accuracy and critical questioning of GenAI-generated texts, lies with us humans.

  • Being the author of a text—whether a scientific publication or an academic thesis—means taking full responsibility for its content and ensuring its accuracy. As an author, you are accountable for the substance, structure, and proper citation of all sources. It is essential to clearly distinguish your own original ideas from those of others and to acknowledge all sources and supporting evidence transparently.

    When using text-generating AI systems (GenAI) for more than basic proofreading or stylistic editing, you must: a) critically assess the accuracy and reliability of the content produced by the GenAI, and b) make clear how the use of GenAI should be disclosed in each specific context. Disclosure requirements may vary depending on the type of publication, so it is important to familiarize yourself with the relevant guidelines for your particular case.

    A responsible use of GenAI also includes complying with legal regulations. Details on what this entails can be found in this checklist provided by KI:connect:NRW. For example, copyright-protected materials may not simply be uploaded into a GenAI system for further use.

     

  • In the current AI Declaration of independence of Bremen University of Applied Sciences, two options are available: -

    • Option 1 – Use of AI technologies without a disclosure requirement 
    • Option 2 – Use of AI technologies with a disclosure requirement 

    In both cases, coordination between students and examiners is required.

     

AI disclosure and documentation

Here you will find a compilation of materials relating to the labeling and documentation of AI use academic writing.

  • Recommendations for the HSB: three selected variants with templates
  • Summary documents on the advantages and disadvantages of various labeling approaches
  • Selected materials from scientific practice of professional associations, publishers, and scientific institutions

AI disclosure: Recommendations for the HSB

  • We have selected three of the most common examples from the various options for AI labeling and documentation and created corresponding templates. They can be used under the CC BY license and adapted to your own requirements or combined.
  • If you have any suggestions or important subject-specific additions to the examples and templates, please write to the writing lab.

 

    • Disclosure: Naming GenAI (tool name, version, provider/manufacturer, URL) and providing a transparent description of its use in a part of the work, e.g.: 
      • Introduction 
      • Methodology section 
      • Appendix 
      • or a separate section for AI usage 
    • Documentation: optional 
      • In most cases, a clear and detailed description is sufficient, see examples in scientific practice
      • Documentation of the prompt and output can be done additionally, see Example 2 
    • see the Templates for Example 1
  • Example 1 - Describing the use (WORD) (DOCX, 238 KB, Accessible file)

    • Disclosure: List the GenAI (tool name, version, provider/manufacturer, URL) in a table either in:
      • the appendix or
      • a separate section of the work
    • Documentation: Provide a table documenting the use of AI. The table can be structured, for example, by:
      • AI tool or technology used
      • Phases or steps in the writing process
      • Individual chapters of the work
    • See the templates for Example 2
  • Example 2 - Documentation table (WORD) (DOCX, 243 KB, Accessible file)

    • Preliminary note: Direct quotes, citation or paraphrasing of content assumes that we are dealing with sources that can be repeatedly accessed by third parties with the same content. However, AI-generated text, code, image material, etc. are dynamic and refer to specific prompts at a specific point in time. Without further action, their creation is not reproducible or verifiable by third parties. Even identical prompts can lead to varying outputs.
    • AI Output as a Source Using AI technologies as a tool differs from treating AI-generated output as an original source. Any information provided by AI should be carefully verified, and direct adoption is generally discouraged — unless the AI technology itself is the subject or a key component of the academic work. Such scenarios may include studies investigating bias in generative AI, hallucinations in large language models (LLMs), or comparisons between output from different GenAI tools. In these cases, the direct use of AI-generated content may be appropriate and meaningful, as the content forms part of the object of investigation.
    • Disclosure: The disclosure of AI usage is done according to the chosen reference style (e.g. APA, MLA, Chicago...) in the text as a short citation and in the bibliography with the whole reference.
    • Documentation: The documentation of the prompt and output of the GenAI must be done in different ways depending on the AI tool used:
      • Link to the corresponding chat (only possible for certain GenAI chatbots; the chat must not be deleted from the history) OR
      • Text copy of the prompt and output in the form of a documentation in the appendix of the work
    • See Template Example 3
    •  
  • Example 3 - Referencing (DOCX, 245 KB, File does not meet accessibility standards)

AI disclosure and dacumentation: Further information

The debate about disclosure, documentation and labeling options for AI use in academic writing is in full swing. We have selected interesting resources, which can help you and your students in dealing with the topic.

 

  • The University of Graz website presents various disclosure options (documentation tables, AI-Usage Cards, disclosure, citation, etc.) and provides a detailed overview of their advantages and disadvantages.

     

  • The universities in Lower Saxony have agreed on the following compilation:

    • Baresel, Kira; Eube, Cornelia; Knorr, Dagmar; Lutter, Ly; Nys, Jasmin de; Röben, Marieke (2024): KI-Gebrauch im Studienkontext dokumentieren (Version 1.0). Available online at https://doi.org/10.48548/pubdata-1476, last accessed on 18.03.2025.
    • Of particular interest are the various documentation tables:
      • holistic documentation
      • tool-oriented documentation
      • phase-oriented documentation
      • reflection-oriented documentation

     

    The Writing Center of Goethe University Frankfurt/Main has created an overview based on a framework by  Rowland (2023) that differentiates GenAI usage on a scale from 1 (Inspiration) to 4 (Content shaping).

    • Writing Center of Goethe University Frankfurt/Main: Framework for developing rules for AI-supported writing processes. Available online at https://www.starkerstart.uni-frankfurt.de/149427334.pdf, last accessed on 26.03.2025.
  • A consideration of AI guidelines and typical problems that arise for students:

    • Radau, Jakob; Maibaum, Miriam; Weßels, Doris (2025): Multiperspektivische Betrachtung problematischer KI-Handreichungen an deutschen Hochschulen – die Sichtweise der Studierenden. In: Hochschulforum Digitalisierung, 27.02.2025. Online verfügbar unter https://hochschulforumdigitalisierung.de/multiperspektivische-betrachtung-problematischer-ki-handreichungen/, last accessed on March 10, 2025.
    • Buck, Isabella (2025): Wissenschaftliches Schreiben mit KI. 1. Auflage. Stuttgart: UTB; UVK.
    • The first German-language book on the topic. 
    • A central premise: Thought processes, which form the foundation of writing, are not and cannot be replaced by AI tools.

Academic pactice - options for disclosure, labeling and documentation

Here you will find various examples of how major professional associations, publishers, and research funding institutions formulate their guidelines for disclosure and documentation of AI use, and thereby define what is considered scientific integrity within their framework and what is not accepted.

 

  • IEEE Guidelines for Artificial Intelligence (AI)-Generated Text

AI Guidelines from IEEE

IEEE is a worldwide professional association for professions and scientists in the field of electrical engineering and information technology.

  • APA policy on the use of generative artificial intelligence (AI) in scholarly materials

AI Guidelines from APA

APA is the American Psychological Association. The APA reference style is widely used across many disciplines. Current information and changes can be found in the APA Style Blog.

 

  • Springer Editorial Policy for Artificial Intelligence (AI)

AI Guidelines Editorial Policy - Springer

Springer - a large publishing house with 2,900 magazines and 300,000 books.

 

  • Statement of the German Research Foundation

DFG – German Research Foundation

Statement by the Presidency of the German Research Foundation (DFG) on the impact of generative models for text and image generation on the sciences and the DFG's funding activities, as of September 2023.

 

  • The DFG generally permits the use of large language models:

    • Transparent handling of generated text and image content is essential for ensuring scientific quality (p. 1)
    • Content and formal responsibility for texts remains with the researchers (p. 1)
    • It must be ensured that the use of generative models does not infringe on third-party intellectual property and does not lead to scientific misconduct, such as plagiarism (p. 2)

    The use of generative AI is excluded in peer review processes. In this case, the principle of confidentiality applies.

FAQ - Lecturers

  • As of February 2025, there is no uniform regulation regarding the use of AI technologies. However, the following recommendations and frameworks apply:

    • Use the materials provided on these pages as well as professional development opportunities to reflect on and define your own position regarding the use of AI.
    • Reach a clear agreement with your students about the role AI technology may or may not play in your module, particularly in relation to assessment. (You may use the suggested wording provided for AULIS.)
    • Inform students about the AI Declaration of Academic Integrity (as of November 2024).

     

  • The following is considered unauthorized:

    • Examination regulations: The general section of the examination regulations states:
      "The use of artificial intelligence without explicit permission constitutes the use of an unauthorized aid (§16 (2), AT-BPO, AT-MPO, revised version from 10/2023)."
      If unauthorized or non-transparent use is discovered, it is considered a violation of the principle of originality.

    • Violation of explicit instructions: If students disregard your explicit, written instructions within the module.

      Prerequisite: You must have clearly communicated your guidelines. Therefore, inform students at the beginning of the course about how AI technologies may be used in your module. You may also refer to the available usage options and the disclosure examples (for students) provided on this page. 

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