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HBO/WO

MBO

17 december 2024

Generative AI Tutorials for Students and Teachers

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. We recognise the growing influence of LLMs in academic research and education. To ensure that our graduates and staff can effectively navigate and utilise these tools, we have developed a tutorial designed to provide both the technical knowledge and ethical framework necessary for responsible use.

Document

Christine Fox, Fleur Boelen

2024

Artificial Intelligence and Librarianship

Courses on Artificial Intelligence (AI) and Librarianship in ALA-accredited Masters of Library and Information (MLIS) degrees are rare. We have all been surprised by ChatGPT and similar Large Language Models. Generative AI is an important new area for librarianship. It is also developing so rapidly that no one can really keep up. Those trying to produce AI courses for the MLIS degree need all the help they can get. This book is a gesture of support. It consists of about 100,000 words on the topic, with a 4-500 item bibliography. The third edition has changes and additions. These include: a new Chapter 6 on evaluation and the future new materials in Chapter 5 on current large language and multimodal models scattered revisions, corrections, and updates. Chapter 1: Intellectual Background Chapter 2: Chatbots Chapter 3: Language Models Chapter 4: Large Language Models Chapter 5: Large Multimodal Models Chapter 6: Evaluation and the Future Chapter 7: Bias and Unfairness Chapter 8: Bias in Machine Learning and Librarianship Chapter 9: What Might Natural Language Processing (NLP) Bring to Librarianship? Chapter 10: What are the Opportunities for Librarians? Chapter 11: Librarians as Synergists Chapter 12: Librarians as Sentries Chapter 13: Librarians as Educators Chapter 14: Librarians as Managers Chapter 15: Librarians as Astronauts Appendix A: Some Theoretical Background to Librarianship Appendix B: Working with LLMS Appendix C: Two Important Methodological Points Appendix D: Causal Diagrams Appendix E: Knowledge Graphs Glossary Bibliography ISBN 13: 9780473722944

Open (Access) Tekst Boek

Martin Frické

23 augustus 2023

Lecture: Natural Language Processing

Students understand the basic concepts of Natural Language Processing Students learn NLP use cases Students get familiar with various NLP tools and concepts

Website

HCAIM Consortium

19 juli 2021

Organizing for Sustainability - A Guide to Developing New Business Models

This upper-level Open Access textbook aims to educate students and professionals on how to develop business models that have a positive impact on people, society, and the social and ecological environment. It explores a different view of how to organize value creation, from a focus on an almost exclusively monetary value creation to one that creates positive impact through multiple values. The book offers students and entrepreneurs a structured approach based through the Business Model Template (BMT). It consists of three stages and ten building blocks to facilitate the development of a business model. Users, be they students or practitioners, need to choose from one of the three offered business model archetypes, namely the platform, community, or circular business models. Each archetype offers a dedicated logic for vale creation. The book can be used to develop a business model from scratch (turning an idea into a working prototype) or to transform an existing business model into one of the three archetypes. Throughout the book extra sources, links to relevant online video clips, assignments and literature are offered to facilitate the development process. This book will be of interest to students studying the development of business models, sustainable management, innovation, and value creation. It will also be of interest executives, and professionals such as consultants or social entrepreneurs seeking further education. Is supported by a self-paced MOOC, also Open Access, holding seven chapters and over 50 teaching units ready to use: https://bit.ly/3oFZ3jj

Open (Access) Tekst Boek

Jan Jonker, Niels Faber

23 augustus 2023

Practical: Federated Learning - Train deep models

Understand how to train models using the Federated Learning framework Understand how local data distribution affects the federated learning Becoming familiar with a high-level framework like TensorFlow

Website

HCAIM Consortium

30 september 2024

A student’s guide to Generative AI

Welcome to this e-module on using Generative AI (GenAI) during your time as a BA or MA student at the Erasmus University Rotterdam!

Website

Erasmus University Library

23 augustus 2023

Practical: Natural Language Processing

Student will understand the basics of the core NLP techniques Student gets familiar with the use of a Naive Bayes Classifier

Website

HCAIM Consortium

27 mei 2025

Gen AI Impact Scan

Dit materiaal is gepubliceerd met het doel een voorbeeld te geven, een inspiratiebron te zijn, of als bouwsteen te dienen binnen een andere onderwijscontext. Voel je vrij om (gedeelten) over te nemen en naar eigen inzicht aan te passen. De methode is ontwikkeld om de impact van Generatieve AI (GenAI), zoals ChatGPT, te beoordelen op de validiteit van toetsing en de relevantie van leerdoelen in een curriculum. De scan heeft een signalerend en activerende functie. Het doel is om een systematische en coherente aanpak te bieden voor het aanpassen van een curriculum als reactie op het (ongeoorloofd) gebruik van GenAI door studenten. De methode bestaat uit vijf stappen: De onderstaande documenten zijn aanwezig in dit pakket: Doelgroep: Opleidingsteams, coördinatoren, docenten en andere onderwijs professionals die de validiteit van de toetsing en relevantie van de leerdoelen willen borgen, gezien de impact van generatieve AI. Basisniveau: Basis GenAI literacy is nodig bij de professionals die de impact scan gaan uitvoeren. Indien die nog niet aanwezig is, kan deze e-learning gebruikt worden om dat te bereiken. (https://tlc.uva.nl/en/article/e-learning-for-lecturers-about-genai/?faculty=53) Benodigdheden: -Lokaal met beamer voor de workshops -Online werkomgeving om bestanden te delen zoals Excel bestanden -Toegang voor docenten tot een geavanceerd taalmodel om de toetsing te maken. Idealiter in een veilige omgeving waar niet getraind wordt op de data zodat er geen problemen zijn met copyright van het leermateriaal. Contactpersoon: Remco Jongkind (r.c.jongkind@amsterdamumc.nl), Erik Elings (e.w.elings@amsterdamumc.nl), Birgit van Berlo (b.m.a.vanberlo@amsterdamumc.nl), Jennita Meinema (j.g.meinema@amsterdamumc.nl), Floor van der Steijle (f.vandersteijle@amsterdamumc.nl)

Document

Erik Elings, Remco Jongkind, Birgit van Berlo, Jennita Meinema, Floor van der Steijle

30 september 2024

Tips and tricks for using Generative AI as a PhD Candidate

Welcome to this e-module on how to use Generative AI (GenAI) during your time as a PhD candidate at the Erasmus University Rotterdam!

Website

Erasmus University Library

1 september 2025

Algorithmic assessment systems

This class pursues a twofold goal of making students both think about the use of AI in recidivism prediction/social benefits allocation and reflect on the possible use of AI for making evaluative, moral judgments. To do so, students are first presented with contextual information about recidivism prediction and social benefits allocation controversies before the class through mandatory reading. During the class, students are divided into four groups that take contrasting positions on these two issues (recidivism prediction and social benefits allocation) and are asked to suggest (counter-)arguments for the position that they have been assigned in a debate-style manner, following academic argumentative norms. Later, students use ChatGPT/GPT3 or an alternative LLM with a chat interface to prompt it about arguments and counter-arguments pertaining to their position. As a result, students compare and discuss the answers they have come up with and those offered by ChatGPT, both within the groups and with the whole class, exploring different dimensions of using AI for evaluative moral judgments

Document

Olya Kudina, Dmitry Muravyov