GenAI resources

This e-learning is aimed at lecturers and teaching assistants who want to learn more about the responsible use of GenAI in higher education and the impact it can have on your teaching.

This e-learning is aimed at students who want to learn more about the responsible use of ChatGPT and GenAI in higher education.

This workshop for teaching staff demystifies how powerful GenAI tools work, examines how and why they are reshaping teaching and assessment, explores how students are using them, and considers the consequences of acknowledging – or ignoring – GenAI use in your educational context.

The Erasmus+ project ‘Teaching and Learning with Artificial Intelligence in Higher Education’ (TaLAI) aims to empower both educators and students in developing GenAI literacy and competence. Visit this page to read more about the project and visit the published articles.

UvA AI Chat is a chatbot developed to comply with the standards set by the University of Amsterdam for responsible AI use. Read about responsible AI use, the policy framework for GenAI in Education and how to use UvA AI Chat.

This article helps you thoughtfully integrate GenAI into your course by guiding you through key design choices, practical considerations, and ethical responsibilities, ensuring GenAI enhances learning without compromising educational quality.

This article shows practical examples of how GenAI can be integrated into your course. Each example highlights a different “role” GenAI can play.

This article shows examples of GenAI personas (or “system prompts” or “CustomGPT’s”) that are already created by UvA lecturers across different courses. Use them as inspiration to create your own, tailored to your teaching goals and context.

This article offers practical prompt engineering tips to help you and your students interact more effectively with GenAI tools. It highlights techniques to guide GenAI in generating relevant and high-quality responses.

The thesis assignment is one of the classical capstones of educational programmes, measuring a student’s ability to collect and synthesise information, as well as to articulate a critical perspective. The advent of Generative AI (GenAI) has put pressure on the validity of this assessment form, however. In this article, we will break down which changes you can consider for your thesis assignment, to be better equipped for the challenges offered by GenAI.

This article describes an in-class assignment where students and lecturers collaboratively create a GenAI Manifesto, outlining shared values, ethical guidelines, and practical agreements for GenAI use within the course. It encourages reflection, discussion, and joint responsibility in working with GenAI tools.

This article describes an in-class assignment where students engage in discussions and reflections on ethical GenAI use in academic writing, evaluating case studies to develop a consensus on responsible GenAI practices. It promotes critical thinking and ethical awareness in integrating GenAI tools in writing assignments.

GenAI changes the validity of assessment forms that require students to do work unsupervised. This article gives a brief introduction to how course design and programme-wide assessment strategy work together to strengthen the overall quality of assessment.

Generative AI can support student learning, but in some assessments you must be sure that students can reason, communicate and perform tasks independently of AI. This page explains when AI should be excluded from assessment and where you can get support. A balanced assessment strategy usually combines several formats to assess different learning outcomes, while supporting accessibility and fairness.

A well-structured assessment strategy contains both Lane 1 and Lane 2 assessment. This article provides assessment formats in Lane 1, to verify independent mastery.

If an existing assignment is vulnerable to being completed largely with AI tools, the goal is not necessarily to remove the assignment entirely. Instead, consider whether the assessment can be adapted so that students still need to demonstrate the intended learning outcomes.

Lane 2 assessments are unsupervised assessment tasks in which students may use generative AI, but are not required to do so. Unsupervised assessments include written assignments (e.g. essays, papers, reports), research projects and theses, portfolios, group projects but also AI-integrated assignments.

Lane 2 assessments are unsupervised assessment tasks in which students may use generative AI, but are not required to do so. In many cases, the learning outcome itself does not need to change. What changes are the assignment design and the assessment criteria: these determine whether, and how, AI may be used while still allowing students to demonstrate the intended outcomes.

AI can be integrated into assessment in different ways depending on the role AI plays in the learning task. In some assignments, AI may function primarily as a tool that supports students’ work, while in others it becomes the object of critical analysis or part of a collaborative workflow.