11/09/2026

Kitchen scales in the Integrity Kitchen

by Artem Artyukhov

Image credit: ChatGPT (GPT-5.6 Luna)

Think about a kitchen scales for a moment. It’s probably one of the most honest things in your kitchen. It doesn’t care how experienced you are, how confident you feel, or how much you want your cake to turn out perfectly. It simply shows you the numbers.

Imagine baking a cake and deciding to add flour or sugar “by eye” because you think you know what you’re doing. Maybe you’ll get lucky. But chances are, something will go wrong. The cake might collapse, taste too sweet, or turn out nothing like you expected.

Research works pretty much the same way. You can’t just guess your data, adjust charts to make your results look more impressive, take shortcuts with references, or copy AI-generated content without checking whether it’s actually true. Sooner or later, those shortcuts catch up with you.

Generative AI can be an incredibly useful assistant. It can help you brainstorm ideas, organize information, explore different perspectives, and speed up certain tasks. But it shouldn’t become the one making decisions for you. Think of it as a kitchen assistant who can suggest a recipe and help with preparation — but you still need to check the ingredients and measure everything yourself.

After all, no matter how advanced your kitchen equipment is, you can’t make a good cake using imaginary measurements. And you certainly can’t build reliable research on unverified information.

The “Tare” button: a small action with a big lesson

If you’ve ever used kitchen scales, you know the little button marked “Tare”. Before weighing your ingredients, you place a bowl on the scales and press that button. The display goes back to zero, so you measure only what you actually need.

Simple, right? But there’s a surprisingly powerful lesson here for anyone involved in research.

Before starting a project, especially when using AI tools, you need to do something similar with your own thinking. Put aside your personal expectations, assumptions, and desire to prove that you were right all along. Be willing to question your ideas, even when the results don’t match what you hoped to find.

AI tools need the same kind of critical attention. Their answers may sound confident and convincing, but that doesn’t automatically make them accurate. They can reproduce biases, misunderstand context, or even invent facts and references.

If you forget to press that mental “Tare” button, you risk measuring the weight of your own expectations or the AI’s assumptions instead of looking at the evidence itself.

Good research starts with an open mind, not with a predetermined answer.

A recipe others can actually follow

There’s another thing that connects cooking and academic research: both work best when other people can repeat what you’ve done.

Think about a good bread recipe. If it tells you exactly how much flour, water, salt, and yeast to use, someone on the other side of the world should be able to follow the instructions and get a similar result. That’s what makes a recipe useful and trustworthy.

Research should work in the same way.

References and citations are like the ingredient list of an academic paper. They show where your information came from, recognize the people whose work helped you, and allow readers to check the original sources for themselves.

The same principle applies to AI. If you’ve used generative AI to brainstorm, analyze information, process data, or improve your writing, be transparent about it. Explain how you used these tools whenever disclosure is required. There’s nothing wrong with using modern technology: the problem starts when you hide its contribution or present automated output as your own independent thinking.

Copying someone else’s ideas without giving credit, inventing references, or passing AI-generated content off as original research is a bit like publishing a recipe with missing ingredients and pretending you invented it yourself.

It doesn’t just raise questions about honesty. It makes your work impossible to properly verify, reproduce, or trust.

Trust is the most important ingredient

At the end of the day, kitchen scales and academic integrity have something important in common: both are about trust.

When you order food at a restaurant, you trust that the chef has used the ingredients listed on the menu. You don’t expect someone to replace real ingredients with something else and pretend nothing has changed.

The same goes for research. When people read a scientific study, they trust that the data is genuine, the sources are properly acknowledged, and the conclusions are based on real analysis rather than wishful thinking or automated guesswork.

That trust matters more than we sometimes realize. Reliable research helps us develop safer technologies, improve medical treatments, make better decisions, and understand the world around us. If the information behind it is unreliable, everything built on top of it becomes questionable.

AI is changing the way we study, work, and produce knowledge. There’s no reason to ignore its benefits. But the more powerful these tools become, the more important human judgment, responsibility, and transparency will be.

So, the next time you use a kitchen scales, remember that little “Tare” button. In research, it represents something much bigger: the ability to set aside your assumptions, question the information in front of you, and take responsibility for the results.

Because whether you’re baking a loaf of bread or writing a research paper, the same rule applies: you can use all the latest tools available, but you can never replace honesty with technology.

Dr. Artem Artyukhov is the Head of the Ukrainian Research Integrity Office and the Head of the Ethics Committee at the National Agency for Higher Education Quality Assurance. His research focuses on the intersection of academic integrity and artificial intelligence.

The authors' views are their own.

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