Generative AI Teacher Tutorial for the Graduate School of Life Sciences University Medical Centre Utrecht Netherlands

We’ve made a substantial updates to our teacher tutorial to help you better understand the current landscape of generative AI and its implications. Whether you’ve worked through the tutorial before or are encountering it for the first time, now is the ideal time to explore the expanded content.

What’s New?

Updated and Expanded Section 2.0 (shared with the student tutorial):

• New explainer on Artificial General Intelligence (AGI) and how it’s being benchmarked
• A full chapter clarifying the distinction between Large Language Models (LLMs) and broader Generative AI
• Introduction to diffusion models, including how they work and what they’re used for
• Expanded content on Prediction… Not Common Sense.
• Expanded content on biases in training data and the social consequences of these biases
• A new chapter on AI sycophancy, covering how GenAI tools have been designed for agreeability and what that might mean when using these tools for feedback or assessment.
• More detail on the Black Box problem and how neural networks obscure transparency
• New insights into deepfakes, including their social, political, and scientific implications
• An extended discussion of Environmental, Social, and Governance (ESG) issues related to GenAI development and deployment
• A new chapter on geopolitics and the global AI race, examining national strategies, ethical frameworks, and power imbalances
• Additional information of how GenAI and its impact on higher education,
• A new chapter about Looking Ahead and how we can be more proactive than reactive. 

Additional updates:

• Minor language adjustments and updated terminology across Sections 3 and 4
• Revisions and new material in Section 5.0, especially focused on how to design thoughtful, responsible AI use in your teaching and assessment
• A new chapter in Section 6.0 on how to build your own chatbot for education and a new section recommending individuals and organisations to follow for insight into:
  • AI ethics
  • AI and environmental impact
  • AI in the life sciences
  • Policy and regulation
  • General GenAI literacy for educators

We encourage all teaching staff to checkout the new material!

 Introduction to the tutorial

As part of our commitment to innovation in research and education, the Graduate School of Life Sciences (GSLS) at University Medical Centre Utrecht has begun integrating the use of Large Language Models (LLMs) into our educational framework. The GSLS is renowned for its interdisciplinary approach, with 16 master’s programmes and a diverse community of 1,500 master’s students. We are dedicated to fostering a collaborative and dynamic learning environment that prepares our students for the challenges they may face in a modern life sciences career. 

In line with this mission, we recognise the growing influence of LLMs in academic research and education. To support our teaching staff in navigating and utilising these tools effectively, we have developed a series of tutorials specifically tailored for educators. These resources, launched in February 2024 alongside our Guidelines for Master’s Student and Teachers, are designed to provide both the technical knowledge and ethical framework necessary for the responsible use of LLMs in the classroom, ensuring that the highest standards of academic integrity and scientific rigour are maintained. 

A key element of these tutorials is our belief in the responsible, human-centred use of LLMs. At GSLS, we view LLMs as tools that can support human abilities rather than replace critical thinking or ethical judgment. For educators, these tutorials focus not only on the practical use of LLMs but also on fostering a value system that promotes transparency, mitigates biases, and protects data privacy. This balanced approach reflects our core values of responsible research and open science. 

To further support our teaching staff, these tutorials offer practical strategies for integrating LLMs into the curriculum while addressing challenges related to their use. Educators are equipped with methods for identifying vulnerable assignments, reducing misuse, and enhancing student learning through ethical LLM integration. Additionally, we provide tailored recommendations for leveraging LLMs in various academic tasks, aligning with GSLS's commitment to innovative and responsible teaching practices. Ultimately, our goal is to foster a culture where LLMs enhance human innovation and ethical decision-making, empowering staff to cultivate creativity, empathy, and critical reasoning in their students. 

Attribution: 
This tutorial is part of the GSLS GenAI resources developed by Christine Fox with the valuable contribution of Fleur Boelen and ChatGPT. We encourage educators and students to adapt and use this material in their learning and teaching processes, ensuring that any adaptations or shared versions are credited back to the original creators. We thank you in advance for respecting our efforts and contributions to the field of educational technology. 

We would like to thank Karin van Es, Marie-Louise Goudeau, and Davitze Könning for their constructive feedback. Additionally, we thank Laura Huiscamp, Shirrinka Goubitz, Marit de Kort, Zoë de Wit, and Harold van Rijen for their reviewing, minor editing, and feedback. Finally, we extend our gratitude to Ruud Dielen for his publishing assistance. 
 

Tutorial Table of Contents

1.0 Master’s Student Tutorial 

             1.1 Guidelines for Incorporating Generative AI in GSLS Master’s Education 

             1.2 Learning goals 

             1.3 Transparency & Responsible Usage: Setting expectations for students 
 

2.0 Introduction to Generative AI 

             2.1 Types of AI 

             2.2 Large Language Models (LLMs) 

             2.3 LLMs vs GenAI: Engines and vehicals 

             2.4 Diffusion Models: The Engine Behind Image, Video, Audio GenAI Tools 

             2.5 Transformers: The Engine Behind LLMs