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‎assets/css/custom.css‎

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@@ -814,6 +814,130 @@ p, .text, .prose p, .prose {
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color: #f9fafb !important;
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}
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/* Project cards styling */
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.project-card {
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display: flex;
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flex-direction: column;
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margin-bottom: 3rem;
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padding: 2rem;
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background: rgba(255, 255, 255, 0.9);
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border-radius: 12px;
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box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
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transition: transform 0.2s ease, box-shadow 0.2s ease;
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}
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.project-card:hover {
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transform: translateY(-2px);
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box-shadow: 0 8px 15px rgba(0, 0, 0, 0.15);
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}
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.project-image {
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width: 100%;
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height: 250px;
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overflow: hidden;
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border-radius: 8px;
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margin-bottom: 1.5rem;
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}
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.project-image img {
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width: 100%;
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height: 100%;
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object-fit: cover;
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object-position: top;
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transition: transform 0.3s ease;
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}
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.project-image img:hover {
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transform: scale(1.05);
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}
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.project-content {
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width: 100%;
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}
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.project-title {
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margin-top: 0;
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margin-bottom: 0.5rem;
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color: #2c3e50;
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font-size: 1.5rem;
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font-weight: bold;
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line-height: 1.3;
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}
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.project-subtitle {
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margin-top: 0;
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margin-bottom: 1rem;
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color: #3498db;
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font-size: 1rem;
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font-weight: 500;
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font-style: italic;
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}
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.project-content p {
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margin-bottom: 1rem;
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line-height: 1.6;
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color: #555;
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}
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.project-content ul {
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margin-bottom: 1rem;
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padding-left: 1.5rem;
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}
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.project-content li {
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margin-bottom: 0.5rem;
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line-height: 1.5;
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}
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.project-content a {
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color: #3498db;
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text-decoration: none;
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font-weight: 500;
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}
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.project-content a:hover {
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text-decoration: underline;
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}
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/* Dark mode styling */
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.dark .project-card {
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background: rgba(45, 55, 72, 0.9);
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color: #e2e8f0;
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}
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.dark .project-title {
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color: #f7fafc;
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}
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.dark .project-subtitle {
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color: #63b3ed;
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}
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.dark .project-content p {
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color: #cbd5e0;
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}
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.dark .project-content a {
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color: #63b3ed;
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}
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/* Responsive design */
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@media (max-width: 768px) {
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.project-card {
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padding: 1rem;
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margin-bottom: 2rem;
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}
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.project-image {
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height: 200px;
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margin-bottom: 1rem;
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}
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.project-title {
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font-size: 1.3rem;
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}
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}
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/* Responsive design for projects - mobile layout */
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@media (max-width: 768px) {
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.page-wrapper .group.bg-white\/90,

‎content/projects/_index.md‎

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# Page sections
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sections:
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- block: collection
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- block: markdown
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content:
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title: 'projects'
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text: ''
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count: 0
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filters:
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folders:
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- projects
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exclude_featured: false
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order: desc
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design:
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view: card
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show_date: true
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show_read_time: false
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show_read_more: true
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show_image: true
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show_links: true
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columns: '1'
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css_class: 'text-left'
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text: |-
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<div class="project-card">
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<div class="project-image">
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<img src="/projects/facet-project.png" alt="FACET Project" />
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</div>
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<div class="project-content">
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<h3 class="project-title">FACET: Teacher-Centred LLM-Based Multi-Agent Systems</h3>
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<p class="project-subtitle">AI for Education</p>
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<p>FACET is a teacher-centred LLM-based multi-agent system that supports teachers in developing personalized educational materials according to student characteristics, such as motivation, self-concept and performance.</p>
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<p><strong>key features:</strong></p>
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<ul>
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<li>Multi-agent architecture: Different agents handle content generation, student analysis, and quality assurance</li>
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<li>Personalization: Adapts materials based on individual student characteristics and learning preferences</li>
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<li>Teacher-centred design: Empowers teachers with AI assistance while maintaining pedagogical control</li>
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<li>Scalable framework: Can be adapted for different subjects and educational contexts</li>
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<p><strong>outcome:</strong></p>
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<ul>
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<li>Together with teachers, we develop a teacher-centred LLM-based multi-agent system to develop AI-based personalized teaching materials. Therefore, we do not only focus on performance, but also consider the motivational and affective aspects of students</li>
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</ul>
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<p><a href="https://arxiv.org/abs/2508.11401" target="_blank">Read the preprint →</a></p>
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</div>
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</div>
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- block: markdown
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content:
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text: |-
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<div class="project-card">
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<div class="project-image">
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<img src="/projects/cognitive-load-ar-study.jpg" alt="Cognitive Load AR Study" />
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</div>
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<div class="project-content">
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<h3 class="project-title">Cognitive Load-based Augmented Reality Design for Vocational Training</h3>
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<p class="project-subtitle">Augmented Reality design</p>
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<p>This research project investigates the design of human-centred Augmented Reality (AR) for learning using cognitive theories, such as cognitive load theory.</p>
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<p><strong>research questions:</strong></p>
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<ul>
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<li>How does visual guidance affect cognitive load in AR environments?</li>
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<li>What are the optimal design patterns for reducing cognitive load?</li>
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<li>How can eye-tracking data inform AR interface design?</li>
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</ul>
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<p><strong>key findings:</strong></p>
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<ul>
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<li>Visual guidance significantly reduces cognitive load in AR learning tasks</li>
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<li>Specific design patterns are more effective than others</li>
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<li>Individual differences in cognitive processing affect AR usability</li>
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</ul>
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<p><strong>outcome:</strong></p>
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<ul>
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<li>This research contributes to the theoretical understanding of cognitive load in AR environments and provides practical guidelines for designing more effective AR learning systems</li>
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</ul>
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<p><a href="https://aisel.aisnet.org/icis2024/" target="_blank">ICIS 2024 Paper →</a> | <a href="https://onlinelibrary.wiley.com/doi/10.1111/jcal.13095" target="_blank">JCAL Paper →</a></p>
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</div>
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</div>
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---

‎content/projects/cognitive-load-ar-study.jpg‎

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‎content/projects/cognitive-load-ar-study.md‎

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---
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title: "Cognitive load-based Augmented Reality design for vocational training"
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startdate: 2023-04-01
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enddate: 2025-09-14
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date: 2025-09-14
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featured: true
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# Project image
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image:
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filename: "cognitive-load-study.jpg"
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focal_point: "Smart"
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preview_only: false
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# Project links
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links:
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- name: "AR and visual guidance - Paper"
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url: "https://aisel.aisnet.org/icis2024/"
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icon_pack: "fas"
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icon: "file-alt"
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- name: "Design guidelines for AR - Paper"
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url: "https://onlinelibrary.wiley.com/doi/10.1111/jcal.13095"
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icon_pack: "fas"
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icon: "file-alt"
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# Project details
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# tags: ["Augmented Reality", "Cognitive Load", "Eye-Tracking", "Human-Computer Interaction"]
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tags: ["Augmented Reality", "Cognitive Load", "Eye-Tracking", "Human-Computer Interaction"]
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---
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This research project investigates the design of human-centred Augmented Rearlity (AR) for learning. Therefore it used cognitive theories, such as cognitive load theory. The study provides valuable insights into how AR interfaces can be optimized to reduce cognitive load and improve learning outcomes.
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## Research Questions
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## research questions
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- How does visual guidance affect cognitive load in AR environments?
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- What are the optimal design patterns for reducing cognitive load?
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- How can eye-tracking data inform AR interface design?
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## Methodology
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## methodology
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- **Eye-Tracking Analysis**: Comprehensive gaze pattern analysis
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- **Cognitive Load Measurement**: Multi-dimensional assessment of mental effort
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- **Controlled Experiments**: Rigorous experimental design with statistical analysis
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- **User Experience Evaluation**: Qualitative and quantitative UX assessment
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- Controlled experiments with quantitative and qualitative data assessment
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- Eye-tracking analysis
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## Key Findings
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## key findings
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- Visual guidance significantly reduces cognitive load in AR learning tasks
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- Specific design patterns are more effective than others
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- Individual differences in cognitive processing affect AR usability
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## Impact
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## read more
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- [AR and visual guidance - Paper](https://aisel.aisnet.org/icis2024/)
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- [Design guidelines for AR - Paper](https://onlinelibrary.wiley.com/doi/10.1111/jcal.13095)
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## impact
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This research contributes to the theoretical understanding of cognitive load in AR environments and provides practical guidelines for designing more effective AR learning systems.

‎content/projects/facet-project.md‎

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---
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title: "FACET: Teacher-Centred LLM-Based Multi-Agent Systems"
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date: 2025-08-01
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authors: ["admin"]
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featured: true
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summary: "A teacher-centred LLM-based multi-agent system that supports teachers in developing personalized educational materials according to student characteristics."
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# Project image
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image:
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filename: "facet-project.jpg"
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focal_point: "Smart"
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preview_only: false
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# Project links
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links:
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- name: "arXiv Paper"
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url: "https://arxiv.org/abs/2508.11401"
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icon_pack: "ai"
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icon: "arxiv"
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- name: "GitHub"
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url: "https://github.com/example/facet"
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icon_pack: "fab"
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icon: "github"
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# Project details
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tags: ["AI", "Education", "LLM", "Multi-Agent Systems"]
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---
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FACET is a teacher-centred LLM-based multi-agent system that supports teachers in developing personalized educational materials according to student characteristics. The system uses multiple AI agents to analyze student needs, generate appropriate content, and provide recommendations for educational worksheets.
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## Key Features
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FACET is a teacher-centred LLM-based multi-agent system that supports teachers in developing personalized educational materials according to student characteristics, such as motivation, self-concept and performance. The system uses multiple AI agents to analyze student needs, generate appropriate content, and provide recommendations for educational worksheets.
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- **Multi-Agent Architecture**: Different agents handle content generation, student analysis, and quality assurance
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- **Personalization**: Adapts materials based on individual student characteristics and learning preferences
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- **Teacher-Centred Design**: Empowers teachers with AI assistance while maintaining pedagogical control
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- **Scalable Framework**: Can be adapted for different subjects and educational contexts
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## key features
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## Technology Stack
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- Multi-agent architecture: Different agents handle content generation, student analysis, and quality assurance
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- Personalization: Adapts materials based on individual student characteristics and learning preferences
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- Teacher-centred design: Empowers teachers with AI assistance while maintaining pedagogical control
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- Scalable framework: Can be adapted for different subjects and educational contexts
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- Large Language Models (LLMs)
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- Multi-Agent Systems
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- Educational Technology
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- Python, React, and modern web technologies
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## read more:
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- [Facet - Preprint](https://arxiv.org/abs/2508.11401)
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## Impact
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## impact
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This project represents a significant advancement in AI-assisted education, providing teachers with powerful tools to create personalized learning experiences while maintaining the human element in education.
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Together with teachers, we develop a teacher-centred LLM-based multi-agent system to develop AI-based personalized teaching materials. Therefore, we do not only focus on performance, but also consider the motivational and affective aspects of students.

‎content/projects/facet-project.png‎

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‎content/projects/featured.jpg‎

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‎content/projects/featured.png‎

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‎static/images/projects/FACET.png‎

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‎static/images/projects/cognitive-load-study.jpg‎

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