AI in the writing process
The TUD is open to the use of (text-generating) AI systems as of May 2024. Specific guidelines can be found on the University Executive Board's website.
The term "artificial intelligence" covers a broad field of research. In scientific writing, the focus is on large language models (LLM) such as ChatGPT. In common parlance, "AI" has become established, which is why we also use this term to refer to LLMs.
The implementation of AI systems in the writing process offers numerous advantages, but also harbors the potential for unreflected dependency. In order for AI to support learning, reading and writing processes profitably, a competent and critical approach to this technology is a basic prerequisite.
The TUD Writing Center is the point of contact for all questions relating to the use of AI in academic writing. We are also happy to provide individual advice. This service is aimed at both students and teaching staff.
How generative AI can support academic reading and writing
AI tools can be used in every phase of the writing process. Although they do not completely replace human actions, they are suitable, for example ...
- for inspiration and brainstorming
- for the creation of outline drafts
- for help in selecting methods
- as an additional research option
- for the development of literature networks and summaries
- for summaries, translations and help with understanding texts
- for data evaluation and visualization
- for generating texts (and other media), e.g. from key points or as different versions of the same text section
- for text feedback on structure and writing style according to predefined criteria
- for correcting spelling and grammar
For more information, see the handout "Use of AI writing tools by students" from the Writing Center at Goethe University Frankfurt am Main.
AI tool tips
Which AI tools can usefully support the academic writing process? What can they do and what can't they do? How do you use them and what do you need to bear in mind with regard to terms of use, copyright and data protection?
Here is the most important information about our (current) favorites:
Brief description |
A pre-trained large language model (LLM) generates texts (and other media such as images or videos) after input (prompts) from users based on its training data. During generation, new, unique content is created by the models calculating the probabilities of the word sequence - they do not use wikis or databases in the training data to look up facts. As a result, the models sometimes hallucinate information and output false facts. This is not malicious or intentional, the models have no understanding of our world - their aim is to generate content and this content is factually correct with a certain probability. Depending on which model is used, different multimodal functions are available. The different models can, for example, also research their answers by accessing the Internet or generate even better texts using the newer reasoning models. A large language model can be used web-based (via a browser) or locally (‘installed’ on your own computer), and they are divided into proprietary models (such as ChatGPT) or open source (such as Llama) - this results in different functionalities and concerns in use. |
Strengths |
Generate texts |
Weaknesses |
Hallucinations Resources Biases |
Data protection |
The situation with regard to data protection and copyright is currently difficult - legal issues have not yet been conclusively clarified. Data protection An overview and further information on this can also be found on the page on the use of AI at the TUD. Copyright Large language models may use copyrighted content in their training data, but depending on the model, users have varying degrees of insight into the sources on the basis of which the new texts were generated. Copyright infringement cannot therefore be ruled out, and all content must be checked by the users. There are different approaches to how the legal situation in Germany can currently be viewed and interpreted, some ideas and insights can be found here:
|
Helpful for ... |
Inspiration and brainstorming, creating draft outlines, summaries, translations and help with understanding texts, generating texts (and other media), text feedback on structure and writing style according to predefined criteria, correcting spelling and grammar |
Access |
An account is most times required, chat histories are saved, depending on the provider. |
Further information |
There is a growing number of providers of large language models, e.g.
In addition, various access platforms have developed that bundle access to different LLMs, e.g.
|
Brief description |
Application with a focus on literature research and processing answers questions in natural language using scientific literature (AI Science Chat). Also provides support in understanding the literature (AI PDF Chat). |
Strengths |
Simple and clear Open Access |
Weaknesses |
Limited source base |
Data protection |
Neither the input nor the output is used to train the models further. The data is mainly processed on European servers. |
Helpful for ... |
Inspiration and idea generation, research processes, development of literature networks, summaries and help with understanding texts |
Access and website |
A free account is required, the chat histories are saved. |
Further information |
Brief description |
The AI-supported writing tool enables the optimization of texts in the areas of grammar, spelling, style and expression. |
Strengths |
User-friendliness Functions |
Weaknesses |
Limited language selection |
Data protection |
Only the DeepL Write Pro version guarantees, among other things, "maximum data security". This means that texts are deleted immediately after processing, are never shared with third parties and are not used to train the models. |
Helpful for ... |
Revision, correction |
Access and website |
https://www.deepl.com/de/write The number of possible text optimizations is limited in the free access. The paid Pro version for 10 euros per month (as of June 2024) offers unlimited text optimizations, use of writing styles and alternative suggestions in addition to the above-mentioned guaranteed data protection. |
Brief description |
Perplexity AI is a research tool that, like GPT, is operated via chatbot, but bases its answers on a list of linked sources. It can also be asked about uploaded files (texts, images, diagrams). |
Strengths |
Source information Up-to-dateness Search filter Attachment |
Weaknesses |
For academic research, this tool is only worthwhile in the Pro version. In free access mode, you get a colorful mix of sources that are available on the Internet. In the Pro version, however, many of the non-academic results can be filtered out using the Academic search filter, so that it is possible to find useful academic sources. In the compiled sources (usually 10-15) you can find what you are looking for. However, the results are far from a comprehensive overview of research - and here too, of course, you need to check whether each type of source is suitable for your own work (e.g. if you are quoting from a popular science blog). |
Data protection |
Under Settings, you can determine whether or not uploaded data can be used for further training of the tool. You can exclude your own data from further use with just one click. |
Helpful for ... |
Research processes, development of literature networks, summaries and help in understanding texts, data evaluation |
Access and website |
https://www.perplexity.ai/ |
Further information |
Brief description |
SCISPACE is an AI tool for literature research and text comprehension. In addition to researching scientific sources, PDFs can be uploaded and questioned in a targeted manner. |
Strengths (selection) |
Data basis Copilot My library |
Weaknesses |
A search with Scispace does not replace a comprehensive database search, nor do the abstracts and insights replace the reading of some texts. As a rule, subject-specific databases are (even) better when it comes to completeness. However, the tool is very well suited for an initial literature overview on a specific topic due to the data basis, the diverse application possibilities for texts and the clarity. |
Data protection |
As PDFs are uploaded from your own hard disk, the issue of data protection is not insignificant here. Scispace itself therefore describes its high level of commitment to data protection: https://typeset.io/t/security-commitment/ |
Helpful for ... |
Inspiration and idea generation, help in selecting methods, research processes, development of literature networks, summaries and help in understanding texts, data evaluation |
Access and website |
https://typeset.io/ |
Further information |
Spelling correction |
Textshine corrects spelling, grammar, punctuation and typography of German texts. Texts are not rewritten, i.e. it should not be seen as a substitute for proofreading. |
Strengths |
In contrast to general purpose models such as ChatGPT, Textshine is optimized for spelling and grammar correction and the associated processes. |
Weaknesses |
Currently only available for German texts, and only for Word files (.docx format). Textshine is not (yet) optimized for academic texts. |
Data protection |
The company is based in Austria. |
Helpful for ... |
Revision, correction |
Access and website | Website Textshine User account required, the first 10,000 characters can be revised free of charge; 1,000 characters can be entered again and again free of charge on the homepage. Price packages in the paid version are charged according to the number of characters. |
Further information | Introduction by Alexander Seifert (founder) |
Offers for students
The AI workshops for students regularly take place once per semester. If you are interested in further dates and offers, please sign up for our list of interested parties on Opal.
- Workshop "Talk to the Machines: Prompt Engineering" (English)
12.12.2025 | 10:00-12:00 am | Fritz-Foerster-Bau 148, registration via OPAL
This workshop takes place as part of the ESF-funded STUDY SMART project. - Input session "HOW TO ... AI Tools for Academic Writing" (English)
22.01.2026 | 9:20-10:50 am | Fritz-Foerster-Bau 148, registration via OPAL
This input session takes place as part of the ESF-funded STUDY SMART project. - Workshop "AI Tools for Academic Writing: Basics and Challenges" (English)
27.01.2026 | 15:00-17:00 pm | tba, registration via OPAL
This workshop takes place as part of the ESF-funded STUDY SMART project. - Workshop "AI Tools for Academic Writing & Studying: Practical Approach" (English)
19.02.2026 | 15:00-18:00 pm | tba, registration via OPAL
This workshop takes place as part of the ESF-funded STUDY SMART project.
Offers for lecturers
Jump-Start your Writing Didactics
In the brochure "Starthilfe Schreibdidaktik" (Jump-Start your Writing Didactics) published by the SZD, you will find a chapter on the use of AI applications in teaching and the evaluation of their use by students.
You can download the brochure "Starthilfe Schreibdidaktik" (in German) here.
Information on hot to get started
On our information website "Academic Writing with AI Tools. Information and ressources for teaching" we report on how to successfully approach the complex topic of artificial intelligence in the university context and in scientific writing.
Workshops for lecturers
You can find all current continuing education courses offered by the Writing Center, including various workshops on “AI tools and academic writing”, in OPAL-Kurs "Weiterbildungen des SZD".
If you are interested in additional dates or further training requests, please send us an e-mail: .
Further offers
- ZILL: Information website on the didactic use of AI
- Studium Generale: Lecture series "Work in progress": AI - Opportunities and risks for employees and their interest groups
- ScaDS.AI Teaching and Training: courses and self-study materials
- Makerspace: workshops, experiments and exchange from May to July 2024
- Courses, workshops and events: SLUB event catalog
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Göttingen Society for Scientific Data Processing (GWDG): Portal with free access to various OpenSource LLMs with TUD login. academiccloud.de
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KI-Campus: Learning platform with courses, videos, podcasts and blog entries that can be used after free registration. KI-Campus.org
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VK:KIWA: Virtual Competence Center on AI and Research Associates with publications and an overview of AI applications at various stages of the writing process. VK:KIWA.de
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Hochschulforum Digitalisierung: Dossier "Generative AI", various contributions
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Writing Center of the Goethe University Frankfurt a. M.: Handout"Use of AI writing tools by students"
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University of Basel : Guide "Quoting from AI"
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Leipzig University Library: YouTube playlist "How to: Wissenschaftliches Arbeiten mit KI". Brief presentation of various AI tools and their potential applications in the academic writing process.
If you have any further questions, need support or advice, or would like to share examples of best practice, please do not hesitate to contact us.

Consultant for Writing Didactics
NamePaulina Hösl M.A.
Project sTUDy smart, expert for AI & writing
Send encrypted email via the SecureMail portal (for TUD external users only).
Writing Center of TUD
Writing Center of TUD
Visiting address:
Fritz-Foerster-Bau, Floor 5, Room 556 Mommsenstr. 6
01069 Dresden