AIX Design: Empathize

During the empathize stage, our goal was to gain a deeper understanding of our target audience and their needs for a journaling app like this. To do this effectively, we had to be very intentional about truly listening to and learning from them. My team and I conducted nine interviews with a diverse range of participants. Those that I personally interviewed were a 14-year-old girl, a 21-year-old female psychology major who journals frequently and understands its benefits, and a 22-year-old male who enjoys journaling but finds it difficult to stay consistent.

To analyze the data from each interview, we used ChatGPT to generate empathy maps and conduct a thematic analysis, helping us identify patterns across participants while grounding insights in direct quotes. For example, the prompt I gave ChatGPT for the Empathy maps was, “We conducted interviews to better understand the users’ views on journaling and self-reflection. Act as a user experience researcher and analyze the interview data. I will provide you with one interview at a time. Create an empathy map in 4 quadrants based on what the participant says, feels, thinks, and does. Each quadrant should contain participants’ quotes for each category (says, feels, thinks, and does). If paraphrased, include the original quote that it came from. Let me know when you are ready.”

Across all interviews, journaling was valued primarily as a tool for emotional regulation and personal growth. Participants described using it to clear their minds, process emotions, and reduce overwhelm. The biggest barrier wasn’t doubt about its value, but simply the struggle to get started. Many struggled with the blank page, low energy, or feeling like they needed something “important” to say. Low-pressure, flexible experiences were essential for our audience. Participants emphasized that journaling should feel supportive and forgiving rather than demanding. Privacy and psychological safety were non-negotiable. Users are ok using AI-generated prompts, as long as the AI doesn’t interact back with them. Ultimately, participants were motivated by long-term growth and pattern recognition, reinforcing that journaling is a deeply personal, evolving practice that requires trust and adaptable support.

While AI was incredibly helpful in organizing and analyzing our interview data, there were moments when the output felt overwhelming. At times, the insights were presented in large amounts of information that could have been simplified or broken down more clearly. I also wished it incorporated more direct quotes from the interviews into the empathy maps and thematic analysis. Without enough of the participants’ language, it became more difficult to accurately separate ideas and ensure the insights remained closely tied to the original data when transferring them to the charts.

Through this research, we gained a much deeper understanding of our target audience and what they truly expect from an app like this. It was valuable to see how AI could support us during the analysis phase, helping us organize and analyze our findings. At the same time, it was equally rewarding to recognize the strength of the research we conducted on our own, knowing that it’s not something AI would’ve been able to do. This stage of the process allowed us to collaborate with AI in an effective way while still relying on our own thinking, ultimately giving us clearer direction as we move forward with developing the app.