This graduation project focuses on improving the understanding and analysis of complex business processes that are difficult to manage due to inconsistencies, lack of standardisation, and poor interoperability between systems. The project is situated in the domain of business process modelling and simulation, …

This demo introduces prompt design as a research approach to studying visual generative AI, distinguishing it from prompt engineering practices that focus on producing aesthetically pleasing or technically polished outputs. Drawing on the query design framework from digital methods, we outline several …

Explainability of AI systems is branded as a solution to the black box problem. The EU AI Act introduces AI explainability as a requirement for high-risk AI systems, including a right to explanation. However, understanding the wording of the requirements and the related provider and deployer obligations …

In his article “Why AI Won’t Democratize Education”, Wieczorek (2025) challenges the techno-optimistic narrative surrounding intelligent tutoring systems. He argues that these systems, despite their promise of personalized feedback and educational efficiency, will not democratize education from a pragmatist …

Abstract from EHRCON25-openEHR International Conference 2025. The increasing demand for structured high-quality clinical data for AI and decision support systems highlights the shortcomings of current healthcare data infrastructure, particularly its misalignment with FAIR [1] data principles (Findable, …

Aletta Smits, professor in Human Centred Technology, zoomed in on human-AI interaction: how to give people autonomy and control. She introduced us to terms like recommender systems: any tool that helps you select one or more from a host of options; and decision support systems: any tool that helps you …

Efficient hydrogen (H2) storage remains a major challenge for clean energy applications. This study presents an AI-driven methodology to optimize H2 storage in porous carbon adsorbents. A comprehensive dataset of 917 literature-derived entries was used to develop two machine learning models: Random Forest …

How designers can contribute to responsible AI in the clinic

As artificial intelligence (AI) systems are increasingly deployed in high-risk financial decision-making contexts, the demand for transparency and interpretability becomes critical. Explainable AI (XAI) has emerged as a key research domain addressing these needs. While most existing XAI studies emphasize …

Industrial Cyber-Physical Systems (CPS) are sensitive infrastructure from both safety and economics perspectives, making their reliability critically important. Machine Learning (ML), specifically deep learning, is increasingly integrated in industrial CPS, but the inherent complexity of ML models results …