Who is science for? How science communication shapes researchers

by Claudia Schrauwen, Faculty of Pharmaceutical, Biomedical and Veterinary Sciences, University of Antwerp

We’ve long thought about how participation in science communication activities can change the way researchers think about their science. Here, Claudia Schrauwen shares her experience as her scientific identity developed. –Krista Longtin, PhD, Editor

I used to think science communication was something that happens after the “real” work is done. You finish the experiments, analyze the data, write the paper, and then, almost as an extra step, you translate everything into simpler language for people outside your field. That’s how it’s often framed.

But during my PhD, I noticed something unexpected: explaining science to non-experts takes far more cognitive effort than we think. More importantly, it fundamentally changes how you think about your own research.

The audience problem we don’t talk about

Most of the time, scientists write for a very specific audience: other scientists in the same field. We share assumptions, jargon and entire frameworks of thinking that don’t need to be explained anymore. While that makes communication highly efficient, it also makes it narrow.

As communication research (Bullock et al., 2019) highlights, this shared language quickly becomes a barrier when we try to step outside expert communities. Outside that cozy academic circle are patients, families, clinicians from other disciplines, and policymakers. They are deeply affected by the outcomes of our work, yet they are rarely part of the conversation.

I wasn’t fully aware of this gap until I stepped out of the lab and spent time in a rehabilitation setting.

When numbers don’t stay just numbers

During that short internship, I found myself explaining my work in much more accessible terms than I usually would in an academic context. I was talking to patients about the exact same results I normally viewed in terms of outcomes, scores, and statistical differences.

I quickly realized that how you explain something doesn’t just help the audience, it changes how you understand it yourself.

–Claudia Schrauwen

I quickly realized that how you explain something doesn’t just help the audience, it changes how you understand it yourself. Cognitive psychology suggests this happens because generating an explanation requires active retrieval, which strengthens our own memory and comprehension far better than passive reading (Roediger & Karpicke, 2006).

What stood out the most was how much my framing influenced the conversation. It wasn’t a question of whether people could understand the science, but about how my explanation shaped what felt meaningful to them, and what questions naturally followed. In that sense, knowledge is not only shaped by data, but also by how it is communicated and interpreted in context. Knowledge, it turns out, is not just shaped by raw data, but by how it is interpreted in a real-world context. As health literacy studies emphasize, scientific info must match people’s lived realities, rather than just being transmitted as raw facts (Wittink & Oosterhaven, 2018).

Explaining forces you to step back

When you strip away the jargon, something shifts. Generative learning theory shows that understanding is not only formed through simply receiving information, but through actively restructuring and translating it into meaningful representations (Wittrock, 1992). Within this broader framework, self-explanation research shows that learning is strengthened when people actively generate explanations rather than passively receive information (Chi, 1994; Zhu, 2024).

Without your academic safety nets, you can’t assume any background knowledge. Even simple terms start to feel less simple. Words like “recovery” or “function” become harder to use casually because you suddenly realize how heavy they depend on context. In experimental work, those words are just operational definitions and necessary simplifications. But when trying to explain them to a patient, you realize how far those definitions can be from actual lived experience.

Image credit

Floating Staircases by Peyton Chung, CC BY 2.0

Communication as a thinking tool

I began to see that explaining science wasn’t just about clarity for others, it was also a tool for changing my own understanding.

When you try to put a concept into simpler language, you often hit a wall. You realize you’ve been relying on definitions that feel clear in a scientific context, but become less straightforward when trying to explain them in simple terms. It raises a simple but uncomfortable question: if I can’t explain my data clearly, do I really understand it, or am I mostly repeating how it is usually described in my field?

Atir & Risen (2025) recently highlighted a fascinating paradox: even though explaining improves our own learning, we often avoid doing it because it is cognitively demanding and exposes the gaps in our own understanding.

This paradox is highly visible when you sit down to rewrite scientific ideas for the public. You can’t rely on the fact that your peers already agree with you. You have to decide what is actually essential to the concept, and what is just academic window dressing. In that process, the cracks start to show. Gaps become visible, and definitions you never questioned suddenly feel unstable.

Who are we really doing science for?

There is a textbook answer: science is for knowledge, for progress, for society.

That sounds great on paper, but in daily practice, the specific audience we write for shapes what we consider important. It dictates what we measure, what we report, and what we value as a “successful” outcome. When that audience expands, even briefly, the weight of what matters shifts. It becomes much harder to ignore the distance between an experimental data point and its real-world relevance, and harder to treat concepts like “impact” or “recovery” as purely technical terms.

A quieter effect of science communication

We often view science communication as a one-way bridge from the lab to society. But I am convinced it works the other way around, too. Good communication brings society into the scientist’s thinking process, not as abstract end-users, but as real people whose lives are affected by how we define success, progress, and evidence.

Science communication is not only about transmitting knowledge, but also about shaping how knowledge itself is constructed and understood in context (Fischhoff, 2019).

Maybe science communication is not only about making science understandable. It might also be about making science slightly more grounded in what those concepts actually mean outside of our own technical language. And if that is true, then it is not something we add after the fact. It becomes part of how we decide what is worth paying attention to in the first place.

References

Atir, S., & Risen, J. L. (2025). The paradox of explaining: When feeling unknowledgeable prevents learners from engaging in effective learning strategies. Journal of Experimental Psychology: General, 154(1), 228–248. https://doi.org/10.1037/xge0001679

Bullock, O. M., Colón Amill, D., Shulman, H. C., & Dixon, G. N. (2019). Jargon as a barrier to effective science communication: Evidence from metacognition. Public Understanding of Science, 28(7), 845–853. https://doi.org/10.1177/0963662519865687

Chi, M. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18, 439–477. https://doi.org/10.1016/0364-0213(94)90016-7

Fischhoff, B. (2019). Evaluating science communication. Proceedings of the National Academy of Sciences, 116(16), 7670–7675. https://doi.org/10.1073/pnas.1805863115

Roediger, H. L., & Karpicke, J. D. (2006). Test-enhanced learning: Taking memory tests improves long-term retention. Psychological Science, 17(3), 249–255. https://doi.org/10.1111/j.1467-9280.2006.01693.x

Wittink, H., & Oosterhaven, J. (2018). Patient education and health literacy. Musculoskeletal Science and Practice, 38, 120–127. https://doi.org/10.1016/j.msksp.2018.06.004

Wittrock, M. C. (1992). Generative learning processes of the brain. Educational Psychologist, 27(4), 531–541. https://doi.org/10.1207/s15326985ep2704_8

Zhu, W. (2024). Effects of explaining a science lesson to others or to oneself: A cognitive neuroscience approach. Learning and Instruction. https://doi.org/10.1016/j.learninstruc.2024.101897

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