Posts tagged: AI

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

, by , Two student projects featured in SAI magazine HAII, student projects, HCI, AI, media

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

, by , Accessing LLMs from within slidev presentations teaching, slidev, AI, LLM, vue, presentations

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

, by , Launch of the Digital Future Lab at Hasselt University Digital Future Lab, AI, innovation, industry, research, digital, Flanders Make

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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Challenges and opportunities for delay-invariant telerobotic interactions (short paper)

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Effective operation in direct-control telerobotics relies heavily on real-time communication between the operator and the robot, as the operator retains full control over the robot's actions. However, in scenarios involving long distances, communication delays disrupt this feedback loop, creating significant challenges for precise control. To investigate these challenges, we conducted a user study where participants operated a TurtleBot3 Waffle Pi under varying delay conditions. Post-experiment brainstorming and analysis revealed recurring challenges, including over-correction, unpredictable robot behavior, and reduced situational awareness. Potential solutions identified include improving robot behavior predictability, integrating feedforward mechanisms, and enhancing visual feedback. These findings underscore the importance of designing intelligent interfaces to mitigate the impact of delays on telerobotic performance.

AI-spectra: A visual dashboard for model multiplicity to enhance informed and transparent decision-making

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We present an approach, AI-Spectra, to leverage model multiplicity for interactive systems. Model multiplicity means using slightly different AI models yielding equally valid outcomes or predictions for the same task, thus relying on many simultaneous "expert advisors" that can have different opinions. Dealing with multiple AI models that generate potentially divergent results for the same task is challenging for users to deal with. It helps users understand and identify AI models are not always correct and might differ, but it can also result in an information overload when being confronted with multiple results instead of one. AI-Spectra leverages model multiplicity by using a visual dashboard designed for conveying what AI models generate which results while minimizing the cognitive effort to detect consensus among models and what type of models might have different opinions. We use a custom adaptation of Chernoff faces for AI-Spectra; Chernoff Bots. This visualization technique lets users quickly interpret complex, multivariate model configurations and compare predictions across multiple models. Our design is informed by building on established Human-AI Interaction guidelines and well know practices in information visualization. We validated our approach through a series of experiments training a wide variation of models with the MNIST dataset to perform number recognition. Our work contributes to the growing discourse on making AI systems more transparent, trustworthy, and effective through the strategic use of multiple models.

Paper on A Visual Dashboard for Model Multiplicity

, by , AI-Spectra: A Visual Dashboard for Model Multiplicity to Enhance Informed and Transparent Decision-Making paper, FAIR, modelmultiplicity, AUI, infovis, Flanders Make

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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Paper on Anthropomorphic User Interfaces

, by , Paper on Anthropomorphic User Interfaces paper, FAIR, AI, HCI, AUI, Flanders Make

Anthropomorphic User Interfaces

Together with Eva Geurts, we explored Anthropomorphic User Interfaces (AUIs) and created a taxonomy that helps us to analyze, identify, and design appropriate AUIs. The paper is available here, and our interactive tool that helps you to find related resources for specific aspects from our technology is available at this URL: https://anthropomorphic-ui.onrender.com.

Citation

@inproceedings{geurtsantropomorphic2024,
title = {Anthropomorphic User Interfaces: Past, Present and Future of
Anthropomorphic Aspects for Sustainable Digital Interface Design},
author = {Eva Geurts and Kris Luyten},
booktitle = {Proceedings of the European Conference on Cognitive Ergonomics 2024},
articleno = {31},
numpages = {7},
keywords = {Anthropomorphism, Human-like interfaces, Taxonomy, User interface design},
location = {Paris, France},
series = {ECCE '24},
year = {2024},
publisher = {Association for Computing Machinery},
url={https://anthropomorphic-ui.onrender.com},
doi = {10.1145/3673805.3673831},
isbn = {9798400718243}
}

Abstract

Interactions with computing systems and conversational services such as ChatGPT have become an inherent part of our daily lives. It is surprising that user interfaces, the gateways through which we communicate with an interactive intelligent system, are still predominantly devoid of hedonic aspects. There is little attempt to make communication through user interfaces intentionally more like communication with humans. Anthropomorphic user interfaces can transform interactions with intelligent software into more pleasant experiences by integrating human-like attributes. Anthropomorphic user interfaces expose human-like attributes that enable people to perceive, connect, and interact with the interfaces as social actors. This integration of human-like aspects not only enhances user experience but also holds the potential to make interfaces more sustainable, as they rely on familiar human interaction patterns, thus potentially reducing the learning curve and increasing user adoption rates. However, there is little consensus on how to build these anthropomorphic user interfaces. We conducted an extensive literature review on existing anthropomorphic user interfaces for software systems (past), in order to map and connect existing definitions and interpretations in an overarching taxonomy (present). The taxonomy is used to organize and structure examples of anthropomorphic user interfaces into an accessible collection. The taxonomy and an accompanying web tool provide designers with a reference framework for analyzing and dissecting existing anthropomorphic user interfaces, and for designing new anthropomorphic user interfaces (future).

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Papers accepted on Anthropomorphic UIs and Model Multiplicity

, by , Multiple Flemish AI Research Program (FAIR) papers accepted paper, FAIR, modelmultiplicity, AUI, Flanders Make

Model Multiplicity in Interactive Software Systems

We got a workshop paper accepted, presenting the initial work of Gilles Eerlings et al. We explore how model multiplicity can be a potential answer to reduce overtrust in AI, as well as avoid undertrust. Still a lot of work that lies ahead, but this seems like a promising direction.

Citation

@inproceedings{luyteneerlings-modelmultiplicity2024,
  author = {Kris Luyten and Gilles Eerlings and Jori Liesenborgs and Gustavo {Rovelo Ruiz} and Sebe Vanbrabant and Davy Vanacken},
  title = {Opportunities and Challenges of Model Multiplicity in Interactive Software Systems},
  booktitle = {The Second Workshop on Engineering Interactive Systems Embedding AI Technologies},
  year = {2024}
}

Abstract

The proliferation of artificial intelligence (AI) in interactive systems has led to significant challenges in model integration, but also end-user-related aspects such as over- and undertrust. This paper explores how multiple AI models with the same performance and behavior but different internal workings—a phenomenon called model multiplicity—affect system integration and user interaction. We discuss the implications of model multiplicity for transparency, trust, and operational effectiveness in interactive software systems.

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