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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

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

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

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 2024

Critical AI Literacy e-learning course

Update May 12, 2025: The Critical AI module has been faithfully remade in Wikiwijs to be openly available to those who cannot access the University of Groningen's Brightspace. The form is identical to the Brightspace version, and as such can be used as an example for those looking to remake the module in their own online learning environment/LMS. The Wikiwijs module can be found by following the link. This is a collection of relevant files for the first version of the University of Groningen's Critical Ai Literacy e-learning course. This e-learning course was implemented in the university's LMS Brightspace, and is aimed at both students and teachers who want learn more about the fundamentals of Text-based Generative AI models such as ChatGPT. The course tackles five topics: What is AI? How do you Generative AI models work? What are the limitations of Generative AI models? What ethical implications are associated with Generative AI models? What are some applications and considerations for using Generative AI models. Each section tries to explain its content in relatively simple terms, so that anyone regardless of educational background can gain a working understanding of Generative AI Models. Consequently, some of the information may be heavily simplified or does not fully extent to the finer details of AI technologies. As a simple introduction to Generative AI models, this e-learning can be followed on its own, or it can be implemented in other courses focusing on topics such as Academic Skills or Information Literacy. There are three kinds of files available: Word documents that have all the texts and images for each section (5 sections, each tackling one of the topics, an introduction section, and a references and future readings section), video files for introductory videos as well as supplemental animations (the word documents refer to these videos), and powerpoints that are the basis for the animations. For reuse, we recommend using these files to build the e-learning in your institution's own e-learning platform or LMS.

Document

Lilian Tabois, Alicia Streppel, Rob Nijenkamp, Martijn Blikmans-Middel, Yvonne de Jong

17 februari 2025

Libguide Artificial Intelligence

AI (Artificial Intelligence) is technology that enables computers and machines to simulate human intelligence. As a result, AI can execute certain tasks that are usually done by people. Based on data, algorithms and statistical models, computers and machines can recognise patterns and make predictions. This kind of AI is called predictive AI. This libguide focuses on the use of AI by students. The libguides covers four topics: What is AI?; Responsible Use; AI-Tools and Prompting.

Website

Universiteitsbibliotheek Utrecht

27 september 2022

Drang naar Samenhang | #39 Waarom heeft de ene taal meer woorden voor kleuren dan de andere?

Het Nederlands kent aardig wat woorden om verschillende kleuren mee aan te duiden. Dat geldt ook voor de landen om ons heen. Maar lang niet elke taal heeft zo’n uitgebreide collectie aan woorden voor kleur. Hoe komt dat? En is er een bepaald patroon te herkennen in de kleuren die wel worden benoemd? Aan de hand van recent psychologisch onderzoek proberen we deze vragen te beantwoorden. Bronnen Gibson, E., Futrell, R., Jara-Ettinger, J., Mahowald, K., Bergen, L., Ratnasingam, S., … Conway, B.R. (2017). Color naming across languages reflects color use. Proceedings of the National Academy of Sciences, 114(40), 10785-10790. Wnuk, E., Verkerk, A., Levinson, S.C., & Majid, A. (2022). Color technology is not necessary for rich and efficient color language. Cognition. https://doi.org/10.1016/j.cognition.2022.105223

Audio

Anita Eerland, Rolf Zwaan

23 augustus 2023

Lecture: Generalizability and Artificial General Intelligence (AGI)

Limitations of currrent AI approaches Definition of Artificial General Intelligence (AGI) Capabilities & core requirements of AGI How can we test for AGI? How far away is AGI and what are the benefits and risks? Update test

Website

HCAIM Consortium