Category: Communication Design

To the survey: https://docs.google.com/forms/d/e/1FAIpQLSfujaTdkiyrwagGO4Cv_JjGQ_IIWt9v4tO54aVBSiF7J6a_pw/viewform?usp=header

Once I had analysed the overall results, I became curious about the individual images. The average recognition rate already showed that identifying AI-generated images is difficult, but I wanted to understand what was behind those numbers. Which images confused people the most? Which ones were recognised more easily? Looking at each image one by one turned out to be one of the most interesting parts of the whole project.

As I mentioned in my previous blog post, authentic photographs were generally easier to recognise than AI-generated images. But once I started looking at the individual images, I realised that the averages only told part of the story.

Some authentic photographs were recognised by more than 70% of the participants, and one image was even recognised by almost 80%. At the same time, several fully AI-generated images were correctly identified by only around 20% of participants.

One result immediately caught my attention. Image 13 was an authentic photograph, but only 14.3% of participants identified it correctly. Image 20, which was fully AI-generated, reached exactly the same recognition rate. I found it fascinating that two completely different image types produced almost identical results. So, looking at each image individually revealed details that would otherwise have been easy to miss.

Another interesting part of the analysis was the confidence ratings (1 = not confident at all, 5 = very confident). Before analysing the data, I expected participants to feel much more confident when they answered correctly. Instead, the difference was surprisingly small.

On average, participants rated their confidence as 3.13 when they answered correctly and 2.99 when they answered incorrectly. Both values are very close to the middle of the scale, meaning that participants generally felt only moderately confident about their decisions. What surprised me most was how similar these two values were. Whether participants were right or wrong, their confidence hardly changed.

I also noticed a change in how participants viewed their own ability to recognise AI-generated images. Since participants rated their own ability before and after the survey, I was able to compare both answers. At the beginning of the survey, many participants rated themselves as being quite good at identifying AI-generated content. After completing the survey, several participants rated themselves as less confident than before. Although this pilot study is too small to draw firm conclusions, it seems that taking part in the survey made some people realise that distinguishing between authentic and AI-generated images is more challenging than they had expected.

The second part of my pilot study was a little different. This time, participants did not have to classify a single image. Instead, they saw two similar images and had to decide which one they thought was authentic. I was curious to find out if comparing two images directly would make the task easier.

The average recognition rate was 55.4%, which is slightly higher than in the first part of the survey. Even so, I was surprised that the results were still so close to chance. Some image pairs were much easier than others. Pair 3 was recognised correctly by 71.4% of the participants, while Pair 4 was the most difficult, with only 42.9% choosing the authentic image. Most of the other image pairs were recognised correctly by about half of the participants.

For me, this part of the study added another interesting perspective. It showed that even when participants could compare two images directly, deciding which one was authentic was often still difficult.

Analysing the results in this level of detail helped me understand much more than the overall recognition rate alone. It showed me which images were particularly successful for the study and which ones challenged participants the most. These observations will surely help me improve the image selection for my main study.

!!Attention!!

!!Please only look at the documents if you have already completed the study or if you do not intend to participate in it!!

To the survey: https://docs.google.com/forms/d/e/1FAIpQLSfujaTdkiyrwagGO4Cv_JjGQ_IIWt9v4tO54aVBSiF7J6a_pw/viewform?usp=header

Seeing the first responses come in was a really exciting moment. After spending so much time planning the survey, choosing the images and creating the questionnaire, I was finally able to look at the data. I was curious to find out whether people could actually tell the difference between authentic, AI-edited and fully AI-generated images. Even more importantly, I wanted to understand where people struggled and whether any interesting patterns would appear.

In total, 14 people completed my pilot study. They came from different age groups, although most participants were between 25 and 34 years old. Each participant looked at 24 images and decided whether each one was authentic, AI-edited or fully AI-generated. Altogether, this gave me 336 individual answers to analyse.

The first thing I looked at was the overall recognition rate. After comparing every answer with the correct solution, I found that participants classified only 42.3% of the images correctly. I honestly expected the result to be higher. At first, this seemed surprisingly low, but the more I thought about it, the more it made sense. AI-generated images have become incredibly realistic, and even manipulated images are often very difficult to recognise.

I also wanted to see whether some image categories were easier than others. Authentic photographs achieved the highest recognition rate, with 57.1% of participants identifying them correctly. AI-edited images were recognised correctly in 42.9% of the cases, while fully AI-generated images were the most difficult, with only 26.8% correct answers. This immediately caught my attention because it showed that participants struggled most with images that were created entirely by AI.

While these numbers already gave me a good overview, I quickly realised that there is more to it. Some images performed much better than others, so I decided to analyse every image individually. Looking at the results in more detail helped me understand which images confused participants and which visual characteristics might have influenced their decisions. This became one of my favourite parts of the analysis.

Besides the recognition rates, I also collected confidence ratings. After every image, participants indicated how confident they felt about their answer. I included this question because I wanted to know whether people were aware when they were uncertain. It is one thing to answer correctly, but it is another thing to know how reliable your own judgement actually is. I will analyse this in more detail in the next blog post.

One thing that became clear after analysing the pilot study is that recognising AI-generated images is more difficult than many people might expect. Before starting the study, I already assumed that people tend to overestimate their ability to recognise AI-generated images. Although this pilot study is too small to confirm that assumption, the results encourage me to explore this question further in the main study by comparing participants’ self-assessment with their actual performance.

!!Attention!!

!!Please only look at the documents if you have already completed the study or if you do not intend to participate in it!!

To the survey: https://docs.google.com/forms/d/e/1FAIpQLSfujaTdkiyrwagGO4Cv_JjGQ_IIWt9v4tO54aVBSiF7J6a_pw/viewform?usp=header

Conducting my first interview was a valuable experience. Not only because of the participant’s answers, but also because it taught me a lot about my own interviewing skills.

Before the interview, I was mainly focused on asking the right questions. I prepared an interview guide and thought about the order of the questions, but I quickly realised that asking questions is only one part of the process. Listening carefully is just as important.

One challenge I faced during the interview was taking notes while also paying full attention to the participant. Sometimes I found myself looking down to write something instead of maintaining eye contact or thinking about a good follow-up question. Although I still managed to collect all the important information, I noticed that my attention was divided.

Despite this challenge, I felt that the atmosphere during the interview was very relaxed. The participant seemed comfortable and answered all questions openly. I think creating a friendly and comfortable environment made it easier for them to share their thoughts honestly. This was also reflected in the observation sheet completed by another student, who described the interview as friendly, respectful and professional. The observer also noted that the participant appeared comfortable throughout the conversation.

Another thing I learned was how valuable follow-up questions can be. Some of the most interesting answers were not the direct responses to my prepared questions but came from asking participants to explain their thoughts in more detail. This helped me understand not only what they think but also why they think that way.

Looking back, there are also a few things I would improve next time. Instead of writing detailed notes during the interview, I would like to record the conversation first, with the participant’s permission, and transcribe it afterwards. This would allow me to focus completely on the conversation, maintain better eye contact and react more naturally to what the participant is saying. It would also reduce the risk of missing interesting details while writing.

Overall, I learned that conducting an interview is a skill that improves with practice. Preparing good questions is important, but creating a comfortable atmosphere and actively listening are just as essential. This interview was a great learning experience for me and gave me more confidence for future qualitative research. I am looking forward to using these experiences in the next interviews I conduct.

To the survey: https://docs.google.com/forms/d/e/1FAIpQLSfujaTdkiyrwagGO4Cv_JjGQ_IIWt9v4tO54aVBSiF7J6a_pw/viewform?usp=header

After spending a lot of time designing my survey, I wanted to include another research method in my project: an interview. While the survey focuses on collecting data from many people, the interview gave me the opportunity to explore one person’s thoughts in much more detail.

Before the interview, I prepared a list of open questions. I wanted to understand how the participant thinks about AI-generated images, whether they trust images online and which clues they use to decide if an image is authentic.

The participant told me that they use AI quite often and spend a lot of time online. Because of this, they felt quite confident in recognising AI-generated images. At the same time, they also said that it is becoming more and more difficult to tell real and AI-generated images apart. I found this interesting because even someone with a lot of experience with AI feels uncertain sometimes.

Another topic we discussed was what people actually look for when judging an image. The participant explained that they usually pay attention to unrealistic content, strange details or common AI mistakes such as unusual hands or facial features. They also mentioned that images with very “perfect” skies or unrealistic scenes immediately make them suspicious. These answers were similar to what I had already read in some of the literature, which made me feel that my research is moving in the right direction.

One answer surprised me the most. When I asked how much they trust images online, the participant explained that they are generally careful. Not only because of AI, but also because authentic images are often taken out of context or edited to support a certain story. This reminded me that image authenticity is about much more than AI. The context in which an image is shared also plays an important role.

We also talked about the importance of the source. The participant said that trustworthy sources can increase confidence, but they still prefer to verify information instead of believing it immediately. I think this is an interesting point because it shows that people do not only judge the image itself but also where it comes from.

Overall, I really enjoyed conducting this interview. It helped me look beyond simple survey answers and better understand the thoughts behind people’s decisions. Even though it was only one interview, it gave me useful ideas that I can keep in mind when analysing my survey results later. I am curious to see whether other participants think in a similar way or whether completely different patterns will appear.

To the survey: https://docs.google.com/forms/d/e/1FAIpQLSfujaTdkiyrwagGO4Cv_JjGQ_IIWt9v4tO54aVBSiF7J6a_pw/viewform?usp=header

After finishing the questionnaire, I thought the hardest part was behind me. I was wrong. The next step, choosing the images for the survey, turned out to be just as challenging.

At first, I simply started collecting images that I thought would fit my study. But after a while, I realised that this approach would not work. Every image I included could influence the results, so I needed to think much more carefully about my selection.

I also wanted to avoid focusing on only one type of subject. If all images showed people, the results might only tell me how well participants recognise AI-generated faces. To get a broader picture, I chose images from different categories, including people, animals, landscapes and everyday scenes. This will hopefully help me see whether some types of images are easier to judge than others.

One category was especially important to me: AI-edited photographs.

Many studies and online discussions focus on images that have been created completely by AI. However, I think AI-edited images are just as interesting, if not even more interesting. In many cases, these images look almost identical to a real photograph because only a small part of the image has been changed. An object might have been added, removed or replaced, while everything else remains authentic. These small changes are often much harder to notice than a completely AI-generated image.

Besides the individual images, I also added a second part to my survey. In this section, participants compare two very similar images. One image is authentic, while the other has been edited using AI. Instead of deciding between four answer options, they simply have to choose which image they believe is the real one.

I included this second task because I am curious whether people perform differently when they can directly compare two images. Maybe small AI edits become easier to recognise when there is an authentic version right next to them, or maybe they are still difficult to spot. I am looking forward to finding out.

One final challenge was the survey platform itself. Google Forms reduces the image quality slightly, so very small details are not always visible. Because of this, I avoided choosing images where the answer depends on tiny artefacts or imperfections. Instead, I selected images that can still be judged even at a lower resolution.

Looking back, choosing the images took much longer than I expected, but it also made me think more deeply about my research question. Every image is now part of my experiment and plays an important role in helping me understand how people perceive authenticity in a world where AI-generated content is becoming more and more common.