To the survey: https://docs.google.com/forms/d/e/1FAIpQLSfujaTdkiyrwagGO4Cv_JjGQ_IIWt9v4tO54aVBSiF7J6a_pw/viewform?usp=header
Over the past few weeks, I have been working on the survey for my master’s project. At first, I thought creating the questionnaire would be one of the easier parts of my research. I already knew what I wanted to investigate, so I assumed I only had to write a few questions and choose some images. However, I quickly realised that designing a good survey is much more complicated than it seems.
My research is about whether people can tell the difference between authentic photographs, AI-edited photographs and fully AI-generated images. Because of this, I wanted the survey to be easy to understand while still collecting useful data. Every participant should understand the questions in the same way, otherwise the results could become misleading.
I decided to create an online survey because it allows many different people to participate. Everyone sees the same images and answers the same questions, which makes it easier to compare the results later. I also wanted the survey to feel similar to how people normally look at images online. Usually, we don’t spend several minutes analysing a picture, we scroll through social media, see an image for a few seconds and immediately decide whether we think it looks real or not. That is why I ask participants to trust their first impression instead of thinking too long about every image.
One challenge appeared while I was writing the introduction. I had to explain the difference between authentic and AI-edited images. At first, I thought my explanation was clear enough, but before sharing the survey publicly, I asked three people to test it for me.
The purpose of this small pre-test was not to collect data. Instead, I wanted to find out if there were any questions that were confusing or difficult to understand. This turned out to be a very good decision.
The three participants gave me valuable feedback. One thing became clear very quickly: the term AI-edited was not as obvious as I had expected. Some people thought that every edited photograph should be classified as AI-edited, even if it had only been adjusted using brightness, contrast or colour correction.
Because of this feedback, I changed the introduction of my survey. I now explain more clearly that normal photo editing does not count as AI editing. Small adjustments like brightness, contrast, saturation, sharpness or colour grading are very common in photography and do not change the content of an image. In my study, an image is only considered AI-edited when artificial intelligence has been used to add, remove, replace or change objects or other elements within the picture.
This small change made me realise how important clear instructions are. Even if a survey is carefully designed, people may still understand questions differently than intended. Testing the questionnaire before starting the actual study helped me identify these problems early and improve the survey.
Looking back, creating the survey was much more than writing questions. It became an important part of my research process and taught me that small details can have a big impact on the quality of the results. In my next blog post, I will introduce the structure of the questionnaire and explain why I chose these specific questions.
