AI UX Design Challenge

Business Opportunity

As mentioned before, UX Design courses and bootcamps do not have much AI work within UX as AI is still new. UX Designers regardless of experience level, would have a wonderful opportunity to gain a real-world experience on doing the AI UX Design challenge to have a case study on their portfolio. The plus side is the UX Designer would have a team, an industry to pick from, a Senior UX Designer mentor to provide strong guidance, and going through workshops on learning how AI applies throughout the UX Design process. This led to an increase of membership traffic up to 40%.

Purpose of the Project

During the AI UX Design challenge, I created the survey forms for each phase of the challenge to gather the feedback on each phase of the design process. Not many participants responded on the surveys, as they were busy and in focus of the design challenge. Even though they responded, the answers were vague in open questions. Therefore, I proposed to the team on conducting user interviews for participants to get better data, stories, and experiences on the design challenge.

Research Objectives

Primary Goals

• Understand participant experience: Uncover what participants found valuable, challenging, and memorable about the challenge

• Identify pain points: Discover obstacles, frustrations, and barriers participants faced during the program

• Explore learning outcomes: Understand what skills, knowledge, or perspectives participants gained

• Gather actionable feedback: Collect specific, detailed suggestions for improving future iterations of the challenge

Research Questions

Core Questions to Address

1.  Experience & Engagement: What was the overall experience like? What moments stood out?

2. Challenge & Learning: What were the most challenging aspect? What did you learn?

3. Value Proposition: How did this challenge meet (or not meet) your expectations?

4. Process & Structure: How did you feel about the program format, timeline, and support provided?

5. Barrier & Enablers: What helped you succeed? What made things difficult?

6. Outcomes: How will you apply what you learned? What's the tangible outcome for you?

7. Recommendations: What would you change about the challenge? What would you keep?

8. Community: How did you interact with other participants? Was that valuable? (Focus on team collaboration from each participant that was interviewed)

Participant Recruitment

Participant Profile

• Participants who completed AI/UX Design challenge is mandatory

• DIverse UX Designer roles: UI/UX Designers, product designers, UX researchers, students studying UX Design, and career-changers to UX Design  

• Varied Experience Levels: Entry-level, Juniors, Intermediate, Seniors

Strategy

• Target: 5 interviews (sufficient for qualitative saturation while manageable)

Participants Recruitment Approach

Using the retargeting marketing method to reach out to participants to join the user interview through the following below:

1. Email outreach: Start with a nice greeting. Describe the purpose behind the email, the type of study, and value of their feedback

2. Discord Messaging: Start with a nice greeting. Frame it as "helping us understand how to make future challenges better"

3. LinkedIn Messaging: Send a brief message to connect first. When the participant connects then message. In the message, start with a nice greeting and then ask for interest in participating for the user interview.

4. Scheduling: Offer multiple time slots across different time zones/times of day. Use my Google Calendar schedule link for participants to select the time slot

AI Involved throughout the User Interviews

The AI tool I used to help brainstorm user interview questions was through Claude AI and Gemini. The benefit with Claude AI, it assisted me through using the follow-up question ideas. I used Fathom AI to conduct the recordings and transcripts. The beauty with Fathom AI is the accuracy of the transcript it provides, a quick summary, and also the ability to ask AI in analyzing the transcript for answers to specific questions I request. I leveraged Claude AI for identifying the key deductive codes from UX Research questions to perform 1 round of data synthesis from the interview data. The next 2 rounds of data synthesis I used Claude AI to perform the task.

Data Synthesis Approach

1.   Leveraged the coding process for identifying the first round of themes. Conducted the deductive coding (predefined set of themes) based off of the research questions. Claude AI was used to come up with deductive codes on the list of themes. Organized the responses from each participant interview into the categories.

a. The key themes to organize information: overall_experience, challenge_type, learning_skill_gained, structure_timeline, structure_support, barrier, enabler, resource_gap, outcome_tangible, outcome_career_impact, recommendation, community_interaction, community_gap

2.  Used all of the participants interviews to categorize within each theme

3.  Based on the results from the second round, my marketing colleague and I worked together to provide the recommendations. I conducted the final round of synthesis to identify the key recommendations. In the Fig Jam file, you can see the final round of data synthesis and the recommendations from the data synthesis.

User Interview Results

The recommendations were divided into key insights from the third final round of data synthesis into the top 3 main categories. They are:

1. Keep on Doing It

2. Improvements for the next AI UX Design Challenge

3. Suggestions for the Future Design Challenges

Workshop & Content

• Prompt engineering should remain the core teaching focus instead of picking up in the mid-program

• Mentors provided their excellent expertise and knowledge sharing during the workshops

• Interactive workshops should continue on as they provide strong concrete examples

Improvements

• The timeline needs to be extended. Due to a large volume of deliverables occurring in the short time frame of 6-weeks, this led to an increase of burnout from the teams. It’s ideal to increase the timeframe to 8-weeks.

• Reduce the deliverable volume in the final week

• Improving the onboarding process for getting into the AI UX Design challenge. For instance, week 1 should focus on “orientation week” for learning about the team, mentor, deciding on the industry, and understanding the deliverables

• Enforce stronger mentor commitment. There was one team during the interviews that brought up the issue of the mentor’s inability to have time for reviewing the crucial deliverables and even showing up to the design challenge

Suggestions for future challenges

• Portfolio and career value proposition should be the core marketing message. The participants motivational driver is getting an AI UX project for their portfolio to help them out with job hunting

• Formalize mentor presence at final presentations as a requirement. In other words, a mentor should be required to show up for their team’s presentation. Mentor should review the final presentations before the team presents.

• Every workshop should end with an explicit list of deliverables to complete.

• Leadership opportunities should be encouraged

Conclusion

This research shifted the AI UX Design Challenge's feedback process from sparse, vague survey data to rich qualitative insight by conducting five participant interviews and running the data through a three-round AI-assisted synthesis process using Claude AI. The findings pointed to three priorities: 

• Preserve what's working (prompt engineering as a core focus, strong mentor-led workshops, and interactive learning formats

• Address structural pain points (extending the program from 6 to 8 weeks to reduce burnout, adding an onboarding/orientation week, and enforcing stronger mentor accountability)

• Reposition the program's marketing around portfolio and career impact, since that was participants' primary motivation for joining — ultimately showing how pairing human-led research with AI-assisted synthesis can turn thin data into decision-ready recommendations. 

Reflection

• I began with a survey to understand participants’ experiences with the IterateUX AI UX Design Challenge. I learned that open-ended questions produced vague responses, particularly from participants completing the survey on mobile devices. This taught me to consider participants’ context when designing research methods.

• Recruiting participants for interviews also taught me the value of strategic, multi-channel outreach across Discord, email, and LinkedIn. Interviews provided richer insights because participants were more comfortable sharing their experiences conversationally.

• For qualitative synthesis, I learned to use coding to identify patterns and themes efficiently. I also leveraged Claude AI to support the coding process, while applying my own research judgment to validate and interpret the findings.