Generative AI Teacher Tutorial for the Graduate School of Life Sciences University Medical Centre Utrecht Netherlands
Creators: name: Fox, Christine Merie
name: Boelen, Fleur
Publisher: name: Zenodo
Year: 2024
Subjects: Generative AI
Graduate Education
E-Learning
Educators
Life Sciences
Higher Education Teachers

description: 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 Transformers: The Engine Behind LLMs 





             2.4 Tokenisation 




             2.5 Prediction, Not Common Sense 




             2.6 Tapestry of Data 




             2.7 Biases 




             2.8 The Black Box 




             2.9 Hallucinations 




             2.10 Data Sharing and Privacy 




             2.11 (E)nvironmental (S)ocial (G)overnance 




             2.12 Enhancing Education and Research 




             2.13 Equity in Education 




3.0 Crafting Effective Prompts 




            3.1 Introduction to Writing Effective Prompts 




            3.2 BRAVE(R) for Learning Assistance 




            3.3 BRAVE(R) for Image Creation 




            3.4 BRAVE(R) for Coding 




            3.5 Overloading Prompts and Factored Cognition 




            3.6 Simple Tasks 




            3.7 Reverse Prompting 




            3.8 Personalising your ChatGPT 




 4.0 Critical Evaluation 




            4.1 Evaluating and Critically Assessing GenAI Responses (FACTS) 




 5.0 GenAI Integrity Strategies 




            5.1 Vulnerable Assignments 




            5.2 Reducing the Overuse of GenAI in Courses 




            5.3 General Advice on Adapting Assessment Strategies and Criteria 




            5.4 Writing Assignments 




            5.5 Creative and Critical Thinking Exercise 




            5.6 Leveraging LLM in Coding Assignments 




            5.7 Example GenAI Disclosure Agreements 




            5.8 Example Rubrics 




 6.0 GenAI Academic Integration and Uses 




            6.1 Leveraging GenAI in Education: Practical Tips 




            6.2 Administrative tasks 




            6.3 Course Related Tasks 




            6.4 Research Related Tasks 




            6.5 Helpful Websites for Teachers 




            6.6 Free Tool Recommendations