AI in Academic Writing Processes

• 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.

• This depends on the agreement with the respective instructor, the assignment, and the module. There is neither a general permission nor a general prohibition.
• Please consult with the instructor and choose the corresponding passage of the AI-declaration of independence in agreement with them.

• Ask your instructors directly about the use of AI in specific modules and exam performances. 
• Refer your instructors to the materials on this page and make use of them yourself as well.
• Instructors will also find additional information on AI usage and building blocks for AULIS on this page.
• Please refer to the AI Declaration of Independence and the available options for responsible AI use.

The question may be brief, but the answer is lengthy, as it needs to consider several levels that are relevant to you:

Disclosure variants/disclosure requirements

• There is no uniform, universally applicable rule. The use and disclosure of AI in your assignments can be interpreted and handled differently depending on the subject area. On this page, you will find three proposals and templates for AI disclosure and documentation that you can use as a guide.

Instructor/examiner requirements

• It may also depend on the specific content of the assignment whether AI use is allowed or not.
• In any case, ask your instructors/examiners if and in what form you can use AI technologies. You can also refer to these variants and the above-mentioned labeling examples (here for instructors).

Examination regulations

• In the general part of the examination regulations, it states: "The use of artificial intelligence without explicit permission constitutes the use of a non-permitted aid (§16 (2), AT-BPO, AT-MPO, new version of 10/2023). If unauthorized or opaque use is discovered, it is considered a violation of the principle of independence.

Transparency and authorship

• Note that AI-generated text is not equivalent to scientific sources.
• The responsibility for the content lies with the author of the text. Uncritical adoption of AI-generated text passages does not correspond to the practice of good scientific work.

Refer to the answer to the question ‘What information do I need to provide?' and discuss with your instructors/examiners. Additionally, use the materials on this page and the AI declaration of independence to clarify the question.

Refer to the answer to the question ‘What information do I need to provide?' and discuss with your instructors/examiners. Additionally, use the materials on this page and the AI declaration of independence to clarify the question.