What My Grad Thesis Taught Me.
My thesis was ambiguous, but it made me better at research.
I recently graduated from my Master’s program in Interaction Technology at the University of Twente. Like many students, that meant completing a thesis.
What I didn’t expect was how ambiguous, uncertain, and challenging that process would be. At several points, I wasn’t sure if what I was doing even made sense. The scope kept shifting. The results weren’t always clear. And I constantly felt like I was balancing between doing “good research” and just trying to finish something that worked. But looking back, that messiness is exactly what made the experience valuable.
This is not a summary of my thesis. This is what it actually felt like, and what it taught me about research, design, and working in uncertainty.
A Bit of Context
My thesis explored how nature-inspired soundscapes can be used in robots within agricultural settings, and how these sounds are perceived by farmers and animals.
What looked and sounded like a very straightforward idea at the time started to look fuzzy the more I got into it.
Besides trying to figure out what to design, I also had to figure out:
- What exactly is the problem?
- Who am I really designing for → farmers, animals, or both?
- What counts as “good” communication through sound?
- What nature-inspired sounds are suitable for the farming environment?
- What research method was appropriate?
As you can imagine, that was a lot to figure out. So I kept redefining the scope. And over time and many, many iterations, I figured out how to narrow down what started as a broad idea into something I could actually study.
In the end, I got more clarity as I reviewed existing research, identified gaps and refined my main research question through prior work and real-world input.
The process felt a lot less like following a set plan and more like actual product discovery: messy, nonlinear and full of reframing.
Shifting From Designing to Thinking Like a Researcher
One of the biggest adjustments I had to make was letting go of my instinct to “design something.” I always thought that my thesis would involve building a prototype or creating a tangible interaction.
But in reality, I worked with an existing robotic platform in the lab, which meant my focus shifted entirely. My thesis required depth in methodology: interviews, qualitative analysis, and a carefully structured experimental setup.
Instead of building, I found myself doing something very different. I had to design the research setup, define the stimuli, map those stimuli to specific intentions (idle state, alert, and task completion), and carefully structure how participants would experience and respond to them.
In other words, my focus shifted to designing how to generate insights that could better inform product decisions for sound design in agricultural robots.
This was the first time I truly worked purely as a researcher. There were moments I felt like I wasn’t doing “enough,” especially because I wasn’t creating anything tangible.
Still, this experience reshaped how I think about design and research. It reinforced that it’s not always about creating solutions. But about asking the right questions, structuring meaningful evaluations, and recognising that generating insights is often the first and most important step before any solution.
Research is Messy
My study involved interviews with farmers and observing animal behaviour in response to different robotic sounds.
Nothing about this was clean or predictable.
- Recruiting participants wasn’t straightforward
- External constraints (time, location, and access) constantly influenced decisions.
- Responses were subjective and sometimes contradictory
- Participants’ behaviour didn’t always align with expectations
- Not everything could be controlled
There were no perfectly structured datasets or clear-cut answers. Real-world data is messy, and that is not a flaw; it is just how it is. Instead of chasing perfect signals, I had to interpret patterns, embrace ambiguity, and lean on qualitative depth rather than “neat” results.
I Couldn’t Have Done This Alone
One of the most important things my thesis taught me was to collaborate more and ask for help. If I had tried to do everything on my own, I don’t think I would have finished. At some point, I would have been forced to change my topic entirely.
This became especially clear during participant recruitment. Finding participants wasn’t straightforward. I’m not from the Netherlands, I don’t speak Dutch, and most farmers I needed to reach were Dutch-speaking. So I had to get creative.
I started with people around me, friends, and friends of friends. I visited local farmers’ markets and spoke to vendors directly. I used Google Maps to identify farms in my region and began calling them one by one.
That process alone pushed me out of my comfort zone. But language was still a barrier.
At some point, a friend of a friend (Thomas) stepped in and became my “default Dutch collaborator.” He helped me make calls and joined interviews when participants only spoke Dutch. Without him, many of those conversations simply wouldn’t have been possible.
As time pressure increased, I expanded my search beyond the Netherlands:
- A friend in Germany participated
- My uncle connected me with a farmer back home
- Friends reached out through their own networks to find additional participants
Recruitment became a network effect…And it didn’t stop there.
When it came to analysing qualitative data, I faced the same challenge again. Some interviews were in Dutch, so I needed help with translation and transcription. My Dutch collaborator supported part of this process, and three other friends (two of them were Dutch) helped verify audio, translations, and transcripts.
At that point, my thesis stopped feeling like an individual project. It became a collective effort. Collaboration wasn’t optional; it was essential for me to make progress. More importantly, I learned that asking for help is not a weakness in research or design. It’s a skill.
In real-world product work, you rarely have all the answers or all the capabilities on your own. You rely on others for context, for access, and for expertise you don’t have.
Not Getting “Perfect” Results
I went into my thesis subconsciously expecting a clear outcome:
“This sound works best.” “This design is optimal.”
That’s not what happened. 😅
The results were nuanced (may be explored in more detail in a future post). Context mattered, and perceptions varied from person to person. At first, it felt like failure, because nothing pointed to a single, definitive conclusion or an obvious “right” answer. I kept waiting for the data to line up neatly, and it just did not.
Over time, though, I realised something important: good research does not always hand you clean answers. Its real value often lies in sharpening the questions you ask in the first place. When you understand why people respond differently, where assumptions break down, and which factors continue to influence outcomes, you end up with better insights, clearer priorities, and a more honest view of the problem. Those insights are usually more valuable than a tidy result, because they shape what you choose to build next and how you decide what “good” looks like.
So What Did My Thesis Actually Teach Me?
Not just about sound design. Not just about research methods. It taught me how to:
- navigate unclear problem spaces
- make decisions with incomplete information
- balance ambition with feasibility
- collaborate effectively
- extract meaning from messy data
In other words, it taught me the same skills required in real-world product and design work.
A reminder for anyone out there working on their thesis right now
If your thesis feels unclear, overwhelming, or imperfect, you’re probably not doing it wrong. You’re probably just in the middle of it, and that’s where the real learning happens.
If you’re working on a thesis or navigating complex, ambiguous projects, I’d love to hear how your experience has been.
And if you’re building products in spaces that involve ambiguity, research, or real-world constraints, I’m always open to connecting and learning from others.
I’m also currently open to UX/Product Design opportunities, where I can contribute to research-driven and thoughtful product experiences.
