Our company specialises in digital products, so it’s crucial for us to get a better understanding of people’s thoughts and attitudes towards AI and existing AI tools. One of our main interests was to see how much people trust them. Thus we decided to do research on the topic.
We designed quantitative research to get an overview. In this post, we will introduce our research process, and give a sneak peek at our top 3 findings. You can also check out the detailed results with all the graphs and findings.
Why we ran this study
UX studio is delving into the research on the topic of AI. We collected the
best AI tools for design and research, and we also reviewed the current obstacles of different AI research tools and collected insights and recommendations about them. Moreover, we started to think about how to handle prompting challenges with AI writing tools.
As researchers, we’re fascinated by human behaviour, especially when it comes to new technologies like AI. By studying how people interact with it and how much they trust it, we can learn a lot about their thinking and behaviour. Understanding current usage patterns also helps us predict how AI tools should be designed to align with users’ expectations in the future.
Our specific interest regarding trust and AI
We opted for a quantitative research approach and chose a survey, since we wanted to examine general attitudes, and needed a large sample size for that. Utilizing a survey, we hoped to identify patterns and trends that would provide valuable insights into the broader landscape of attitudes toward AI tools.
Sneak peek at our findings
In the end, 169 participants filled out our survey, and we could reach a wide age range from 9 different European countries.
Our top 3 interesting findings were:
People in general trust the concept of AI, but different factors can affect the level of trust.
The most popular tool to try is ChatGPT.
People’s biggest expectation of AI tools is to save time.
To see the full, detailed results, click here!
How did we conducted our research?
1. First we defined the general research questions
When we started the project, our baseline questions were:
- Do people trust AI and AI tools?
- What concerns do people still have related to the concept of AI and AI tools?
- Are people open to trying AI tools?
- What tools do people use?
- For what reasons do they use these tools?
- What are their expectations for using them?
- What tool do they use most frequently, and why?
Additionally, we were interested in how gender, location, industry, and previous experiences can affect the level of trust.
2. Then we selected the appropriate tools
Based on our questions and our decision that we would go with an exploratory quantitative approach, we decided to run a survey. We aimed to have participants from western, southern, eastern, and northern countries within Europe.
We aimed to design a maximum 10-minute-long survey to get a better understanding of our questions.
We chose Alchemer to craft the questionnaire due to its suitability for our needs (eg. Likert scale, slider, logic skip). Subsequently, we distributed the survey using MakeOpinion, which enabled us to swiftly collect sample data from our chosen countries.
3. Third step was to create the timeline
During our research project, we adhered to a structured timeline to ensure thoroughness and efficiency.
In Week 1, we conducted an extensive review of existing literature on AI to gain a comprehensive understanding of the topic. During the reviewing process we dedicated time to also discuss what we read and ideate about the research goals.
Following these discussions, in the mid of the second week we defined our research objective and questions.
After having our clear research questions, in the second half of Week 2, we created the survey questions and set up the survey.
The third week was spent on data collection and analysis. In the first half of the week, we launched the survey, and within a few days, we received enough responses. Thus, in the second half of the third week, we focused on data analysis.
Finally, in Week 4, we collaborated with a designer to create a visually engaging and clear output from the results, making our findings clear and visually appealing.
Why do we do internal projects?
Although client work is always our top priority, we strive to invest time and energy in internal projects as well because we believe these investments will pay off.
We engage in internal projects to gain experience with topics, methods, and areas that personally interest us. This approach not only fosters a culture of continuous learning and innovation but also allows us to stay ahead of industry trends and improve our skills. By exploring our passions through these projects, we can experiment with new techniques and methodologies, which in turn enhances our professional growth. We believe that this hands-on experience benefits our clients too, as it equips us with a broader knowledge base and the ability to offer more creative and effective solutions tailored to their needs.
With our current research, we delved into the topic of AI to broaden our understanding of the user experience with AI tools. This project also provided us the opportunity to utilize and refine our quantitative research skills, experiment with new statistical tools like JASP, and create visually engaging outputs from our results with Webflow.
Searching for the right UX agency?
At UX Studio, we are professionals in conducting quantitative research and have successfully completed numerous research projects. We believe in the importance of integrating both qualitative and quantitative research, as this combination allows us to obtain more detailed and comprehensive insights.
Is there anything we can do for you at this moment? Get in touch with us, and let’s discuss your current challenges. Our experts would be happy to assist with both quantitative and qualitative UX research, strategy, or UX/UI design.
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