I’m writing this at the end of a particularly intense period—two papers just submitted, multiple grant applications in various stages, and the final stretch of my BKO (Basic Teaching Qualification) at UHasselt. The pressure of postdoc life—securing funding, maintaining research output, developing as an educator—is very real.
But reflection has a way of clarifying what matters.
When I started teaching in June 2024 with the Bioinformatics course, I assumed my computational neuroscience expertise would naturally translate to the classroom. I was wrong. Teaching biomedical students required learning an entirely new skill: translating technical concepts across disciplinary boundaries.
The breakthrough came when I stopped thinking about teaching as knowledge transfer and started thinking about it as context building. Biomedical students don’t need to become computer scientists—they need to understand how computational tools can answer their questions. This shift transformed how I designed lectures, practicals, and especially the coaching sessions that students later praised in evaluations.
My teaching spans multiple worlds:
Each context demands different pedagogical approaches. What works for CS masters doesn’t work for nursing students. The common thread? Start from their world, not mine.
Not everything goes smoothly. During Bioinformatics project evaluations, we told a student group their report contained “ChatGPT hallucinations.” Their response—documented in course evaluations—was devastating. They felt accused of academic dishonesty when they’d only used AI for spell-checking, as UHasselt encourages.
This incident exposed my own uncertainty. Where exactly is the line between legitimate AI assistance and problematic use? How do we have constructive conversations about AI-generated content without making accusations?
I’m participating in the “Make Your Course AI-Proof” workshop on October 23, 2025, because I need these answers. The technology is evolving faster than pedagogical frameworks, and honestly, it stresses me out. But avoidance isn’t an option—our students are already using these tools, and they need guidance, not prohibition.
I supervise six master’s theses this year, co-supervise a doctoral student, and coordinate research across multiple institutions through the Flanders AI Research Program (Use Case Real World Evidence). The time investment is substantial, and balancing it with my own research and grant writing is challenging.
But when I see students present work that becomes publications—or better yet, when they develop the confidence to tackle complex problems independently—the investment feels worthwhile.
What makes this possible? Prof. Liesbet Peeters.
She’s been my mentor since day one at UHasselt, and her approach to mentorship has fundamentally shaped how I think about academic leadership. She doesn’t just offer advice—she creates conditions for growth. She encourages experimentation, normalizes failure as learning, and consistently demonstrates that supporting others’ development is not separate from research excellence but integral to it.
I try to pay this forward with the students I supervise. Not always successfully—I’m still learning to balance support with appropriate challenge, and to recognize when I’m overcommitting—but with intention.
Through the BKO process, I’ve identified patterns in my development:
From individual to collective: I used to think good teaching was about individual preparation. Now I understand it’s about team coherence. The positive evaluations in Bioinformatics came not from my lectures alone but from how our interdisciplinary team created a coherent learning experience.
From implicit to explicit: So many teaching problems stem from assumptions left unstated. Evaluation criteria, acceptable AI use, expected time investment—if it’s not explicit, students will fill gaps with their own assumptions, often incorrectly.
From reactive to reflective: The BKO structure forces systematic reflection. Instead of only reacting when problems arise, I’m building habits of regular evaluation and adjustment.
From expertise to empathy: My technical knowledge matters less than my ability to understand where students are and what they need to get where they’re going. This sounds obvious but required unlearning ingrained academic habits.
AI in healthcare, federated learning for sensitive data, brain-computer interfaces for motor restoration—my research sits at intersections where technical capability meets human impact. Teaching the next generation isn’t separate from this work; it’s central to it.
Students graduating today will face challenges we can’t fully anticipate. They need more than algorithms—they need judgment about when to use them, ethical frameworks for deployment, and the ability to communicate technical work to non-technical stakeholders.
This is what drives my teaching: preparing students not just to apply existing techniques but to navigate complexity, communicate across boundaries, and think critically about the implications of their work.
To my students—past, present, and future—thank you for your patience with my learning process. Your feedback, even when difficult to hear, has been essential.
To Prof. Liesbet Peeters: your mentorship has been transformative. You’ve shown me what academic leadership can look like when it prioritizes growth over gatekeeping.
To fellow early-career researchers navigating similar pressures: you’re not alone. The funding stress, the publication pressure, the impostor syndrome—it’s part of the terrain. Find your people. Ask for help. Celebrate small wins.
The postdoc phase is demanding, but it’s also formative. I’m learning to teach, to lead research teams, to mentor, and to balance competing demands. Not perfectly—far from it—but with intention and support.
And that, perhaps, is enough.
This post is part of my reflection on completing the BKO (Basic Teaching Qualification) at UHasselt. If you’re interested in federated learning, brain-computer interfaces, or the intersection of AI and healthcare, feel free to connect.