Resources
A fundamental conceptual understanding is required in order to integrate the diverse application possibilities of artificial intelligence, and generative AI in particular, into teaching. The following compilation contains a range of different resources to lay the foundations for this and to deepen the subject matter.
Introduction to generative AI
Generative artificial intelligence (AI) is based on deep learning, in which machine learning is used to train artificial neural networks, known as Large Language Models (LLM). The models are trained with large amounts of data in order to generate new results based on the learnt patterns.
This technology, which has been researched for many years, achieved a breakthrough in 2017 when the Transformer architecture was published in the article external pageAttention is All You Needcall_made. A key concept here is the so-called principle of self-attention, in which, for example, words can be embedded in the correct context of a sentence or even an entire text. As an introduction to this, the external pagegenerative AI exists because of the transformercall_made illustrates these relationships in a very understandable way.
Generative AI is always based on a probability calculation. It can therefore lead to hallucinations and incorrect references. Biases can also arise due to incorrect data or its processing. This will improve with the further development of the models, but the output must still always be checked for correctness.
Basics
The following list offers the opportunity to learn the basics of generative AI, gain an insight into the technical implementation and try out the first possible applications.
- The short video series on external pageIntroduction to AI for Teachers and Studentscall_made shows the basics, introduces Large Language Models (LLM) and prompting and describes the benefits for teaching.
- Stanford University's external pageArtificial Intelligence Teaching Guidecall_made offers the opportunity to learn more about artificial intelligence in education and to positively influence the dialogue about it.
- The article external pageHow Large Language Models Workcall_made provides a step-by-step introduction to how an LLM operates.
- In the video on external pageGenerative AI in a Nutshellcall_made, in addition to the basics, possible uses are also shown, applications explained and the risks and limitations pointed out.
The external pageKI-英皇娱乐call_made is a learning platform for artificial intelligence and offers online courses on the basics of artificial intelligence, machine learning and the opportunities offered by language assistants in university teaching.
The external pageHochschulforum Digitalisierungcall_made is also addressing the topic and examining the effects of generative AI from various perspectives.
Prompting
Prompting refers to the provision of prompts to a language model. The quality and precision of a prompt largely determines the effectiveness of generative AI. With good prompting, the AI can be instructed to generate targeted output.
- external pagePrompt Laborcall_made course of the KI-英皇娱乐
- external pagePrompt-Katalogcall_made, KI-英皇娱乐 and Hoschulforum Digitalisierung
- external pageA Teachers's Prompt Guidecall_made
- external pagePrompt Engineering Guidecall_made (PromptingGuide)
- external pagePrompt Engineering Guidecall_made (LearnPrompting)
Information and discussion rounds
The Refresh Teaching event series regularly addresses the topic of AI and its impact on teaching. Lecturers provide insights into their teaching and report on their own experiences.
- Artificial Intelligence in Teaching and Learning: Large Language Models and insights into the Innovedum project "Assessing the Potential of AI for Scientific Writing Techniques".
- Teaching and Learning with AI: Practical ideas in the application of artificial intelligence in the field of assessment as well as in an ethical and historical context.
external pageLeLacall_made, the university didactics learning lab for digital skills, addressed the implications for the design of teaching at universities in the webinar series "AI or what the ChatGPT?". In the two series of events, various topics were addressed and examined in depth:
- external pageVolume 1call_made: Implications for language, writing and didactics, reflections on aesthetics and ethics.
- external pageVolume 2call_made: What do the data, performance records, AI in research, teaching and university operations say?
Data input for generally available applications in the field of generative AI should be treated with caution, as input data is usually not protected and can be reused by providers as training data.
Microsoft Copilot has been offering a protected environment for employees and students via the ETH account since February 2024. Further details can be found under Tools & Licences.