Written by the 2023 99P Labs x CMU MHCI Capstone Team
Edited by 99P Labs
The 99P Labs x CMU MHCI Capstone Team is part of the Master of Human-Computer Interaction (MHCI) program at Carnegie Mellon University.
Catch up on Sprint 3 here!
Shifting Focus to Gen-Z Learning
At the end of Sprint 3, we created hot takes on possible in-car learning interactions based on our research insights. As we did this, we realized that we were focusing too heavily on how a learning experience can fit into a car instead of delving deeper into how our users learn in general. We needed to gain a deeper understanding of how our users learn to identify the problem we need to solve.
We interviewed Karen Holtzblatt and obtained valuable advice from her on how to conduct our research activities. She encouraged us to think more deeply about the defining moments of Gen-Z, including the impact of COVID-19 and the rise of downloadable services and entertainment. She also reminded us to think of how these factors fit into [99P Labs’] future business model. Most importantly, she urged us to actively listen and ask probing questions to our participants as we begin to plan our interviews with them.
Planning Interviews
Together, we decided that in-person interviews would be our next research activity. We each came up with a set of questions we wanted to ask Gen-Z. We wanted to know what they are interested in, what they like to learn and what they prefer to use on their devices to learn both formally and informally. We also wanted to know how this ties into what they value in life. When speaking to our faculty advisors, we realized that we also needed to understand how Gen-Z consumes information in general, and define what counts as learning in those moments. We continued to refine and build these questions as we gained more insights in our research synthesis.
In addition to in-person interviews, our team also set up Dscout, a research platform that was provided by our client in case we need to recruit Gen-Z participants for online interviews.
Synthesizing Research
We each came up with insights from our domain research and compiled these with the insights we obtained from intercept interviews and expert interviews. We started synthesizing these insights with our faculty advisors by reading off an insight and clustering related ones together.
During this process, we purposely avoided labeling these clusters so that we can easily move the post-its around and focus on figuring out the relationships between them. We had deep discussions amongst ourselves, deciding whether insights are going in the right place or whether we should form a new cluster entirely. We also noticed relationships between clusters and drew those out on the whiteboard.
After we drew insights from each research activity, we took these insights to draw 5 key insights that will inform our interviews in the coming week.
- People don’t want to learn as they would outside of cars, transit gives them reprieve.
- How freedom is defined is relative to the user, some may want a reprieve while others want to be productive.
- Cognitive load and mental limitations impact information absorption.
- Control is an important part of people’s learning experience.
- Semi-autonomy, AI, and car tech give us opportunities to turn cars into workspaces, but do people actually want that?
Creating a Visual Theme for Spring Presentation
As we are moving towards the latter half of the research phase, we have also begun researching current designs of human machine interfaces (HMI) in cars. We looked at interfaces in existing cars as well as game interfaces since car companies are starting to use game engines to design their interfaces and share a similar aesthetic. We are also coming up with a visual theme for our Client Presentation and research report.
Next Steps
- Continue conducting interviews with Gen-Z participants, both in-person and on dscout
- Synthesize the insights we obtain from these interviews
The work and knowledge gained from this project are only intended to be applicable to the company and context involved and there is no suggestion or indication that it may be useful or applicable to others. This project was conducted for educational purposes and is not intended to contribute to generalizable knowledge.
