Program Round 3

The Future of Teaming: Students and AI Agents part 1
Leon Abelmann, Annie Aggarwal, Greta van Bracht, Nayantara Thomas, TU Delft

What happens when AI becomes a teammate? Join this interactive workshop to explore how AI agents can foster richer student collaboration in projects and enable collaborative learning. We invite participants to experience AI-supported teamwork firsthand by co designing an educational activity with AI agents and sharing insights and findings from our educational intervention. Through guided reflection and discussion, we'll examine how AI can reshape collaboration, creativity, and learning.

*It is important for participants to bring laptops for this session.

Self-Related System Learning for a Healthy Future; a didactic tool to support students in multidisciplinary systems thinking for addressing complex societal challenges
Tjerk Jan Schuitmaker-Warnaar, Universiteit van Amsterdam

This session discusses an interdisciplinary didactic tool that supports students in developing systems thinking to address complex challenges in health care. The course this tool is developed in focuses on the transformation needed to create a sustainable and healthy healthcare system.

Addressing complex societal challenges requires students to learn to think from a systems perspective. They need to investigate how systemic mechanisms shape current practices. Complex societal challenges cannot be solved by a single discipline, as multiple technical, social, legal, and physiological causes and consequences are interconnected. Science and engineering students can be well equipped to analyse these problems. However, they may feel uncertain about how to assess and use knowledge from other disciplines. Students therefore need both the skills and motivation to find, evaluate, and connect knowledge from multidisciplinary sources. This requires self-regulated learning: the ability to take responsibility for one’s own learning by setting goals, monitoring progress, and reflecting on outcomes. It involves cognitive, meta-cognitive, and motivational or affective processes.
In the course, students first engage with an authentic, delineated problem. This supports motivation and provides a meaningful context in which they can develop relevant skills. From there, they move towards a broader systems question, examining the issue through a defined and manageable lens. They use causal loop diagrams (CLDs) to explore relationships, feedback loops, and possible leverage points within the system.

This form of education is socially relevant because it equips future professionals to analyse complexity and search for solutions. It is also future proof. Artificial intelligence can help students access relevant knowledge, but the connection to an authentic problem means that AI cannot simply complete the assignment for them. Students must always critically assess and relate their causal loop diagram to the specific problem context, which is an effective way to enhance learning.

Problem solved: how design education can equip students for uncertain futures
Marjolein Derks, Wageningen University & Research

In design education, students embrace their creativity to get to inspiring, novel solutions to wicked problems. They learn how to connect to and communicate with stakeholders and scientists from a multitude of disciplines. And they learn how to deal with uncertainty, as in design there is not just one correct answer, but an array of possibilities that can all be valid in different conditions and contexts. They therefore learn how to justify and build arguments for design choices they make. In short, designers develop unique skills that support them to deal with the world of uncertainty we face. Ddesign education allows for a highly interactive and attractive learning environment, and design is, by nature, very connected to the 'real' world.

Independent of the content subject, design education aims to support students (1) to become independent navigators of complex and uncertain situations and (2) to develop and effectively use their vision on what constitutes desirable change.

In this session we will highlight the unique properties of design education, but also discuss the struggles faced by both students and staff, and how to overcome them.

Scaling inquiry based learning: sustainable strategies for large cohorts
Liesbeth Bijlsma, Universiteit Utrecht

Inquiry-based learning equips students with the critical thinking skills needed to address complex societal challenges and prepares them for an increasingly dynamic professional landscape. By integrating authentic, ongoing research into curriculum design, inquiry-based learning also strengthens the connection between education and research, creating more sustainable and future-oriented learning environments.

In this interactive workshop, Liesbeth will discuss how inquiry-based learning can be designed for long-term impact. Together with participants she will explore different scalable inquiry-based learning approaches and how to sustainably embed them within curricula to cater large student cohorts. In addition, key considerations will be discussed to balance educational innovation with quality assurance in established programs.

Build Your Own Educational Card Game: From Experimentation to Scalable Game-Based Learning
Martijn Boussé, Maastricht University

How can a small educational experiment grow into something that can be shared, studied, and used across different courses and contexts? In this hands-on workshop, we use the Linear Algebra Card Game as a case study to explore this question.

The game was designed to help students move beyond isolated procedures and actively discuss connections between mathematical concepts. For example, understanding why “if a matrix is non-invertible, then it has a zero eigenvalue” requires connecting several ideas rather than applying a single procedure. During the game, students place cards representing concepts, examples, and theorems and explain their mathematical connections to their peers.

Participants will briefly experience the game themselves before turning to their own teaching. (No worries! No linear algebra knowledge is required.) In small groups, they will sketch a simple card game or game-inspired activity around a learning challenge from their own context. Along the way, we use our experiences developing, piloting, and researching the Linear Algebra Card Game to discuss what it takes to move beyond a promising experiment: iterative design, classroom implementation, evaluation, and making materials reusable. Our game is available as an open educational resource, allowing others to use and adapt it, while an ongoing Scholarship of Teaching and Learning study investigates how and under which conditions it supports student learning.

Participants leave with a first game concept for their own teaching and practical ideas for turning educational experimentation into something that can be shared, evaluated, and scaled.

Think Like a Chief Engineer: An MSc Course Where AI is Both a Partner and Black Box
Ali Ozbakir, Stefano Bromuri, Open Universiteit

This talk presents an MSc course, Software Engineering and AI. It rests on a simple premise: students should think like a chief engineer. They must own the full lifecycle, even when a task pushes them past their specialty.

Central to the course is a duality in how AI is treated. First, AI is a partner in two roles. As a simulated client, an LLM lets students elicit requirements from an open project idea. As a coding partner, it assists with code review and repetitive implementation work. Students are taught prompting as a discipline for both roles, covering common techniques and prompt security. These elicited requirements then drive two decisions: the architecture and the design patterns used to implement it. Patterns draw on the classic Gang-of-Four vocabulary, extended with AI-specific counterparts such as Retrieval-Augmented Generation and the Model Context Protocol. Second, AI is a black box. It must be integrated, tested, and held to compliance standards it cannot self-certify. Either way, students remain responsible for the outcome.

We report on the course's first run, piloted with two adult learners, practising professional software engineers. Their capstone is a multi-modal retrieval-augmented generation system combining OCR, vision captioning, and hybrid retrieval. Its AI components were evaluated and audited against the EU AI Act. We share what worked, what surprised us, and what we would change before scaling the course to a full cohort.

This session is part of the GenAIinCSEdu workshop series.