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Abstract
Interested in using Generative AI in your scientific work processes in a responsible and ethical manner?This block course for PhD students allows for experimenting with generative AI to generate texts, images, and audio that can be used in various scientific contexts, from presentations to publications. PhD students are invited to engage in a problem-based learning setting, tackling concerns and
Objective
• Hands-on training on using generative AI in a responsible, efficient and professional way in daily scientific work. • Competencies to deal with limitations, potential pitfalls and benefits of AI-based tools in relation to research integrity and ethics. • Reflect on manipulation techniques of present and past scientific visualizations techniques. • Get introduced into synthetic audiovisual data such as deepfakes. • Use visualization techniques in co-setup with AI-tools to create visual abstracts, thumbnails or graphical abstracts for peer-reviewed publication. • Practice collaborative workflows with genAI. • Get familiar with technologies that should help to protect personal or copyrighted data when using AI and apply them to own data.
Content
Day 1: Ethical Prompting Lab (9:00 – 17:00, Nov 12) Outputs generated with AI often amplify social biases, stereotypes, or inaccuracies through their filtering of data: for example, favoring certain groups or ideas, perpetuating stereotypes, or making incorrect assumptions based on learned patterns. These unwanted AI pitfalls can be minimized through ethical prompting coupled with system -based measures. The day is structured in a way to provide a short theoretical input on prompt engineering with a special focus on following the guidance of the PROMPT AI Framework for Informed AI Use followed by workshops and case studies to discuss the different outputs of genAI. The goal is to develop prompts that are precise, responsible, personalized, culturally sensitive, explainable, less conversational and gain troubleshooting skills. Day 2: Responsible image creation / scientific visualization (9:00 – 17:00, Nov 13) Participants will be introduced into the potential and fallacies of image-based AI tools respecting originality, transparency, and copyright. The applications of synthetic audiovisual media such as “deepfakes” in the realm of academic work will also be critically discussed. How to create reproducible and accurate scientific visualizations from scientific data using e.g. stable diffusion models? What are the potentials and limitations of using diffusion models for scientific visualizations? How to prompt for original ideas and how to change the images through human expertise and own style by using creative but controlled prompting? Students will practice sketch-to-image, collage-to-image, inpainting methods to explore potential use of these tools for their own projects. Day 3: AI-supported scientific workflows (9:00 – 17:00, Nov 18) Participants will explore workflows that can simplify or automate certain steps in scientific work. Especially we will look into individual chat bots and customized GPTs. We will understand how RAG workflows (PDF to text, Image to vector) are working as they are the underlying principle of individual and controlled chat bots. We are aiming also to test GPTs for their correctness or biases. Day 4: Protecting your privacy (9:00 – 17:00, Nov 19) Here we aim at increasing the awareness of what you give away when you share (your) data in AI-based environments. What is the private, sensitive or sensible information that in different AI-based tools and instances users are leaking? Expertise in the field will point out the techniques and processes to avoid leakage of the private, non-copyrighted, original, and personally owned material. This day will focus on measures of protecting privacy in institutional environments, techniques that can protect images and information to be used by gen AI. An example of the new forms of protecting data is watermarking, used to make the origin and the copyrights transparent.
General Information
- Language
- English
- Levels
- DR
- Frequency
- Semesterly recurring
Examination
- Type
- ungraded semester performance
Registration & Places
- Max Places
- 20
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise |
PhD-Student Experimenting Lab: Explore the responsible use of AI in academic work
**together with Life Science Zurich Graduate School and University of Basel**
Dates: Wednesday, 23.09.2026; Thursday, 24.09.2026 and Wednesday 30.09.2026, time 09:00-17:00; group work and self study in between.
For non-ETH PhD students enrolled in PhD programs of Life Science Zurich Graduate School and University of Basel, please register at:
Choose ► Plant Sciences ► "Explore the Responsible Use of Generative AI for Scientifc Purposes"
For the full program of the course, see:
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No time listed | 32 h semesterly |
Offered In
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Doctorate Environmental Systems Sciences (More Information at: )
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