Sprint 9: Prototypes to Presentation
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 8 here!
Hello dear reader. When we last left off, our intrepid team was gearing up for our final* round of user testing sessions.
We started out testing the concept in what can most charitably be described as a cardboard hut.
Fast forward to July, our users are now experiencing a high-fidelity version of the Mobile Mentor from my Rav4 while on the move in Pittsburgh (I know, I know, should have gotten a CR-V). It’s an upgrade that feels akin to a graduation. We used a wizard of oz setup to make the experience as immersive as possible.
This involved mounting an iPad on the headrest, which was connected to a Zoom meeting. Our team shared the Figma prototype on this meeting, allowing us to simulate the voice of the Mobile Mentor. This enabled interaction with our research participants and we were able to answer any queries they had about the learning content (image below on the right). We also used printed transparency sheets to mimic window overlays (image below on the left).
Identifying patterns in the data
As with each of the preceding 5 weeks, we synthesized the data collected from the sessions and noted interesting user comments and questions. By this point, we’ve gotten comfortable with interpreting user feedback beyond their explicit statements.
What we’ve found to be more important than what we hear is the underlying ‘why’. It’s common for users to tell us seemingly contradictory things. It’s important to understand patterns and sentiments amongst different users. These reactions help us ascertain whether we’re moving in the right direction.
As we discussed in our prior article, the weekly research synthesis informs our design principles. With these principles in place, we are set to give shape to the Mobile Mentor.
Who is the Mobile Mentor?
We centered our efforts on defining the Mobile Mentor’s behavior across various user scenarios, which includes defining what it would not do. This exercise enabled us to demarcate the boundaries of our concept. For example, imagine that Mobile Mentor notices the rider isn’t really engaged in the lesson. Maybe the rider is in a bad mood. Once Mobile Mentor detects a lack of interest, it waits until it reaches a critical threshold. It then gracefully ends the lesson with an encouraging phrase to let the user know it’s ok to take a break.
It will also offer to talk about why they don’t want to learn in case there’s something MM can do differently. Equally important though, is what the Mobile Mentor doesn’t do. In this case, Mobile Mentor doesn’t pressure the user to continue if they don’t want to. Our system is all carrot and no stick.
When in doubt, build a concept model
We then categorized the information above into a more approachable concept model. We used dot voting to identify which scenarios would be the most illustrative for our final presentation. All of these situations are important but we were interested in the ones that would give our audience the most complete picture of Mobile Mentor in the limited time of a presentation.
More importantly, these dot votes also allowed us to distill the essence of the mobile mentor experience (pictured below).
This is the essence of the mobile mentor experience, what we’re calling the ‘happy path’. From here we can imagine all of the auxiliary experiences a user could run into that we’ll need to also account for — a user getting motion sick, being tired, wanting to reroute to pick up groceries, etc.
What’s next?
To wrap things up and start preparing for our final deliverables, we’ve written out a script that tells the story of someone using the Mobile Mentor while on their commute. We’ve been sure to include both the core experience along with a few key moments where the user veers off the happy path.
*final round of user testing. Stay tuned for our final presentation where we showcase how our concept works while wearing an AR headset in a real self-driving car!
Thank you for learning along with the team on their Mobile Mentor journey! Follow 99P Labs here on Medium and on our Linkedin for more research projects and collaborations!
