General Information on Using AI at the University of Greifswald
What is this about?
Artificial intelligence (AI) is becoming increasingly important in research, teaching and everyday life at universities. Particular focus is placed on the use of generative AI, which opens up new opportunities for research, studies and teaching, whilst at the same time raising questions regarding good academic practice and its use in the context of examinations, coursework, and theses. Issues such as data protection, copyright, and the responsible handling of sensitive data also play a significant role. This page provides a summary of key information on regulations, tools, support , and training programmes. It therefore serves as a guide for students, lecturers, and members of staff on the use of AI in different university settings.
What do I need to bear in mind?
Generative AI needs to be used in a thoughtful and responsible manner. It is vital that you comply with the study and teaching regulations and clearly indicate how you have used AI. The protection of personal and sensitive data takes top priority; entering such data into external AI applications is not permitted (see also the Guidelines on the Use of AI). Copyright issues must also be taken into account. Generative AI should therefore only be used as a support tool and not act as a substitute your your own independent work!
Where can I get help?
AI tools should be used with due care. A good place to start is the URZ’s basic training course, which is also a prerequisite for using the University of Greifswald’s AI tools.
The Centre for Academic and Digital Skills (ZADK) also provides comprehensive information on the various skills required for the use of AI tools, which are also referred to below.
![(Generative) AI [de]](/storages/uni-greifswald/fakultaet/mnf/biologie/zoologie/zool_museum/KI_UG/Generative_KI.png)
![AI @ UG Guidelines [de]](/storages/uni-greifswald/fakultaet/mnf/biologie/zoologie/zool_museum/KI_UG/Leitlinien_KI.png)
![AI & Exams [de]](/storages/uni-greifswald/fakultaet/mnf/biologie/zoologie/zool_museum/KI_UG/Pruefung_KI.png)
![Ethics & Bias [de]](/storages/uni-greifswald/fakultaet/mnf/biologie/zoologie/zool_museum/KI_UG/Ethik_KI.png)
![Prompt Competence [de]](/storages/uni-greifswald/fakultaet/mnf/biologie/zoologie/zool_museum/KI_UG/Prompt_KI.png)
![AI Tools/App Hub [de]](/storages/uni-greifswald/fakultaet/mnf/biologie/zoologie/zool_museum/KI_UG/AppHub_KI.png)
Coordination and contacts
A steering group comprising representatives from all member groups, faculties, the administration, and centrally run facilities coordinates and manages key AI-related issues. In addition, various focus groups work on different topics, such as courses on AI skills or guidelines on the use of AI in examinations.
AI Steering Group
- Patrizia Grenz (Student Pro-Rector)
- Dr. Juliane Huwe (Registrar, Head of Administration and Finance)
- Prof. Dr. Lars Kaderali (UMG)
- Dr. Jana Kiesendahl (ZADK)
- Dr. Wenke Liedtke (THF)
- Anja Mauritz (E-Administration)
- Prof. Dr. Peter Michalik (Pro-Rector)*
- Prof. Dr. Boris Schinkels (RSF)
- Prof. Dr. Ralf Schneider (URZ | CIO)
- Prof. Dr. Daniel Siegel (MNF)
- Prof. Dr. Anette Sosna (Pro-Rector)*
- Prof. Dr. Tina Terrahe (PHF)
- Stefan Wehlte (Legal Services Office)
- Dr. Christian Winterhalter (UB)
- Prof. Dr. Kristina Yordanova (Inst. f. Data Science)
- Doreen Hallex (ZQSL)
- Prof. Dr. Lars Kaderali (UMG)
- Jana Kiesendahl (ZADK)
- Dr. Wenke Liedtke (ThF)
- Prof. Dr. Peter Michalik (Pro-Rector)*
- Annika Peschel (ZPA)
- Prof. Dr. Boris Schinkels (RSF)
- Prof. Dr. Daniel Siegel (MNF)
- Prof. Dr. Anette Sosna (Pro-Rector)
- Patrizia Grenz (Student Pro-Rector)
- Prof. Dr. Micha Werner (PhF)
- Dr. Philipp Adämmer (Inst. for Data Science)
- Jette Boeck (student/e-tutor)
- Dr. Jasmin Hirschberg (Language Centre)
- Dr. Jana Kiesendahl (ZADK)*
- PD Dr Martha Kuhnhenn (Communication Studies)
- Kai Steffen (UB)
- Greet Stichel (Digital Changemaker & Economics)
- Prof. Dr. Ines Sura-Rosenstock (Media Education)
- Prof. Dr. Lars Kaderali (UMG)
- Stefan Kemnitz (URZ)
- Prof. Dr. Peter Michalik (Pro-Rector)
- Prof. Dr. Ralf Schneider (URZ | CIO)*
- Dr. Juliane Huwe (Registrar, Head of Administration and Finance)
- Anja Mauritz (E-Administration)
- Prof. Dr. Peter Michalik (Pro-Rector)
Glossary
Digital systems that tailor learning content to individual student’s level of knowledge, learning pace and needs.
An AI system that pursues a goal, processes information, makes decisions, and can carry out individual steps independently, such as conducting research, planning or autonomously completing tasks using digital tools.
The ability to use AI systems competently and to evaluate them critically. This includes understanding, applying, reflecting and helping to design such systems.
A defined sequence of rules or computational steps for solving a problem or processing data. In AI systems, algorithms are used to recognise patterns in data and calculate outputs.
An interface that enables different software systems to communicate with one another, e.g. between a learning platform and an AI tool.
A service provided by Greifswald’s University Computer Centre, featuring AI tools that run on the university’s own servers. According to the website, user input does not leave this secure environment.
A branch of computer science that develops systems capable of performing human-like tasks such as learning, problem-solving, or language processing. It works with algorithms and data patterns.
Systematic errors or biases in AI systems that may arise from unbalanced training data or flawed models.
Very large and complex datasets that are difficult to process using traditional methods and often form the basis for AI applications.
A dialogue-based AI system that answers questions or automates tasks, such as providing study advice or support with e-learning.
A licensing model that allows creators to specify the conditions under which their works may be used.
Protection of personal and sensitive data against unauthorised access or misuse. In a higher education context, this is particularly relevant in relation to the GDPR (please also refer to the Guidelines) and when data is transmitted to external AI systems.
Deep learning is a machine-learning method based on artificial neural networks. In this process, the computer learns from a very large number of examples (training data). The system gradually identifies patterns in data, e.g. in texts or images. For example, it can recognise faces or formulate replies. The computer does not ‘understand’ in the same way as a human being, but rather calculates which result is most likely to be correct.
Digital competency encompasses the safe, critical and responsible use of digital technologies. It goes far beyond mere computer skills and encompasses the ability to use digital media confidently for information, communication and problem-solving, as well as for creating content.
Learning offers on digital platforms and using digital tools.
Deals with moral and social issues related to the use of AI, such as transparency, fairness and responsibility.
AI systems capable of generating new content, such as text, images, videos or programme code.
A specialised processor that accelerates complex calculations for AI applications.
Incorrect or fabricated content generated by generative AI systems.
In-person teaching and synchronous online teaching offered at the same time (synchronously). In this context, hybrid teaching does not mean all forms of combined online and in-person teaching. Instead, it refers exclusively to synchronous forms of online and in-person teaching (dual synchronicity).
An AI model for processing and generating natural language.
A language model that has been trained on a vast amount of text data and is capable of understanding and generating natural language.
Analysis of learning data to improve teaching and learning processes.
A process in which computer systems learn from data and recognise patterns without being explicitly programmed to do so.
The tendency of AI systems to reproduce dominant or frequently voiced opinions.
A detailed prompt (input) specifying the role, task, steps, context, objective, and desired output format.
AI systems capable of processing different types of data simultaneously, such as text, images and audio.
Computer-based natural language processing, e.g. translation, text generation, or voice agents.
A computational model based on how the human brain works, which is used in many AI tools.
Freely accessible academic content. In the context of AI, this is relevant for the legally permissible use of texts.
Software whose source code is available to the general public and may be modified.
The result generated by an AI system, such as text, an image, code, a summary, or an analysis.
Input or task for a generative AI. The quality of the prompt affects the quality of the output.
Proficiency in prompt engineering, i.e. the ability to effectively guide AI systems using clear, precise, and context-rich prompts.
Techniques for formulating queries in a targeted manner in order to obtain better results from AI syste
Pre-formatted input text that can be adapted for your own purposes.
Data that accurately represents the groups or phenomena under investigation. They help to reduce distortion.
Consumption of electricity, water, or computing power during the operation of AI systems.
Clarification of who is responsible for AI-supported decisions, errors or damage.
A concept promoting the responsible use of AI, taking into account ethical considerations, data protection, and impact on society.
Correlations in data on the basis of which AI systems generate predictions or outputs.
A simplistic or distorted portrayal of individuals or groups.
The dataset used to train an AI system. The quality, scope, and distortions in this data influence the results.
Transparency regarding whether and how AI was used.
Digital tools that automate tasks or provide information, such as AI tutors.