1.4. Generative AI and its potential applications

Generative AI has quickly become a key tool capable of performing creative, communicative and analytical tasks. However, there are also limitations.

Generative AI refers to a class of systems capable of generating new content – such as text, images, music, videos or programme code. These are usually based on so-called Large Language Models (LLMs) or multimodal models, which have been trained on vast amounts of data. They possess impressive capabilities for problem-solving and content generation.

  

Overview of various generative tools

Below, we present various generative tools. Most of these tools are generally based on machine learning methods and utilise neural networks, in particular the Transformer architecture. They analyse statistical patterns in large text, image or audio corpora and learn from them to generate appropriate outputs for new inputs. In doing so, they calculate probabilities – they do not ‘understand’ in the human sense, but instead select the most probable continuation of a context.

  

Generative AI text generators

Generative AI image generators

Generative AI video generators

  

How can the use of Artificial Intelligence in higher education be designed in a way that promotes learning, is responsible and fosters creativity?

The use of Artificial Intelligence (AI) in higher education opens up new possibilities for teaching and learning – provided it is didactically well-considered, critically reflected upon and creatively designed. This is not merely a matter of mastering AI tools such as ChatGPT or Copilot from a technical perspective, but above all of how these technologies can be meaningfully integrated into learning processes.

The use of AI is conducive to learning when it supports students in developing skills such as problem-solving, academic writing or critical thinking – for example, through the use of AI in writing workshops, for text analysis or for simulating arguments.

Responsible means taking ethical, legal and social issues into account: What data is being processed? How transparent are the systems? How can AI be used in a way that promotes educational equity and does not reproduce bias? Strategic guidelines or internal university recommendations can provide guidance here.

Creative use of AI occurs when it becomes the starting point for new teaching and learning formats – e.g. in playful scenarios, when creating your own AI applications, or as part of project work in which students actively explore the features and limitations of AI .

👉 Our resources provide practical suggestions and a wide range of ideas – whether you are just starting out or wish to further develop your existing teaching with AI.

  

Areas of application in higher education

Examples of how generative AI tools can be used include:

Teaching

• Creation of (interactive) teaching and learning materials and accessible content
• Creation of exercise and exam questions, including appropriate distractors and assessment criteria
• Tutorial support for students

Administration

• Automated text processing (e.g. proofreading, simplification, translation)
• Creation of social media content or video messages for public relations work

Research

• Visualisation and communication of research data
• Preparation of models or prototypes
• Support with literature reviews or methodological questions

Studying

• Brainstorming ideas for presentations, essays or exam questions
• Rephrasing your own texts or linguistic revision
• Help with structuring complex content

  

Tips for implementation in a university context

• Start small: Initiate pilot projects in individual departments and evaluate them systematically
• Promote competence: Schedule training and further education on usage scenarios
• Share knowledge: Utilise internal university networks, AI workshops or exchange formats
• Strengthen diversity: Integrate multimodal tools specifically into inclusion and diversity strategies

   

  

🎥 Videos (in-depth)

"AI in Higher Education" – YouTube playlist (dghd), in German

A multi-part video series on the integration and reflection of AI in teaching. Ideal for didactic inspiration and practical examples.

   

💡 Learning Summary Chapter 1.4: Generative AI and its potential applications

• Generative AI generates new content: It produces text, images, music or code based on large language or multimodal models and statistically calculates the most probable outputs – not consciously, but data-driven.
• A wide range of applications in higher education: Generative AI can support teaching, administration, research and student studies – for example, in the creation of learning materials, visualisations or automated translations.
• Responsible use is essential: Data protection, copyright and transparency must always be observed when using generative tools. Pilot projects, further training and the exchange of experiences promote a well-founded introduction.
• Using creative freedom responsibly: Good teaching with AI means not only using AI, but also engaging with the social, ethical and educational policy dimensions – in dialogue with learners.