Two student projects from the UHasselt Human-AI Interaction course featured in SAI Update

The SAI Update magazine (Nov 2025 , sia.be) selected two projects from our Human–AI Interaction (HAII) course for its Next Technology Generation special. Proud of our students Linsey Helsen and Xander Vervaecke who turned their Human-AI Interaction project ideas into concrete, useful systems.

1) A Multi-Agent Approach to Fact-Checking ( , ) — Xander Vervaecke (UHasselt) Xander’s LieSpy.ai coordinates multiple LLMs (e.g., GPT, Gemini, Mistral) to verify claims, compare reasoning, and aggregate evidence into a transparent verdict. The interface exposes sources, trust scores, and model rationales, moving fact-checking beyond a single-model answer. Key ideas: multi-agent collaboration, cross-validation, explainability.

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LLMQuery for Slidev: Integration of on-the-fly LLM Queries during your Presentation

I wanted to show my students appropriate ways of using LLMs for and during coding, so I started building (with some LLM help) a Slidev component, LLMQuery.vue, that adds LLM interactions to slides. It feels important to actively show students how these tools can amplify human knowledge and skill building rather than replace it altogether, even if I’m far from an expert. So with a bit of LLM help , I put together a sli.dev component in Vue that integrates LLMQuery right into my Slidev presentation. Maybe it’s useful for others too, so I’m sharing it here for download and further tinkering—people who are much better at web dev (there are many!) can probably turn it into something truly polished.

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Launch of the Digital Future Lab: Toward Intelligible, Trustworthy and Human-Centered Digital Systems

The official launch of the Digital Future Lab (DFL) marks an exciting step forward for Hasselt University and for the ecosystem of digital innovation in Flanders. With over 80 researchers across various interdisciplinary groups, DFL focuses on creating well-designed, human-centered, trustworthy, and useful digital systems that address both industrial and societal challenges. We did an interview (in Dutch, with Ann T’Syen) that can be found here.

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Master thesis Maties Claesen nominated for the EOS thesis award!

Very proud of my thesis student Maties Claesen, who has been nominated for the EOS thesis award! His work, “ZeroTraining: Extending Zero-Gravity Objects Simulation in Virtual Reality Using Robotics,” combines virtual reality and robotics to simulate weightless objects more realistically – crucial for astronaut training and space exploration. Maties demonstrated some impressive creative problem-solving skills, especially in combining diverse fields to tackle complex challenges with limited hardware resources.

Special thanks to Andreas Treuer, Martial Costantini, and Lionel Ferra at ESA for their support, valuable insights and feedback on this work. Andreas was particularly instrumental for this work by sharing his experiences and providing feedback throughout the project which was crucial in refining both the scope as well as the implementation of this work.

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Throwback 20 years to 2004: XML-based User Interface Description Languages

This year marks 20 years since I co-organized the Workshop on User Interface Description Languages (UIDLs) during the Working Conference on Advanced Visual Interfaces, held in Gallipoli, Italy (May 25–28, 2004). Together with Marc Abrams, Jean Vanderdonckt, and Quentin Limbourg, we created an event that surpassed all expectations in terms of attendance, engagement, and the quality of contributions.

The early 2000s were a transformative period for the field of Human-Computer Interaction (HCI). Researchers and practitioners alike were grappling with the challenge of building flexible, reusable, and context-aware user interfaces (UIs) that could adapt to the growing variety of devices and use cases. XML, with its ability to structure and abstract information, became the language of choice for UIDLs.

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Paper on A Visual Dashboard for Model Multiplicity

In AI research, model multiplicity can help users better understand the diversity of AI predictions. Our new system “AI-Spectra” provides a visual dashboard to harness this concept effectively. Instead of relying on a single AI model, AI-Spectra uses multiple models—each seen as an expert—to produce predictions for the same task. This helps users see not only what different models agree or disagree on, but also why these differences occur. Gilles Eerlings (a FAIR PhD student ) and Sebe Vanbrabant where the main contributors for this work and combined machine learning, model multiplicity and visualisations that focus on the characteristics of an AI model, instead of explaining the behaviour.

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