Sprint 5: Fruits of our Labor
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 4 here!
ROI
As a team, we came together last week to complete 10 hour-long interviews with people ages 18–33 (the age range of Gen Z in 2030). For more on why we’re focused on this demographic, please start here. These sessions helped us define ‘learning’ as it pertains to this project. It’s invigorating to listen to new voices and new ideas, but the return on interviews really starts when our digital interview notes come to life as little pieces of paper on our office wall.
Process -
- rewatch interview at 1.5x — 2x speed
- transcribe notable moments/thoughts into a spreadsheet
- copy the rows of notes
- paste into Miro as stickies (c’mon Figma, catch up)
- arrange stickies on A4 frame in Miro
- export as pdf
- carefully cut notes into squares using an ancient paper cutter
- distribute piles of notes to team members
- call out what sounds compelling, ‘yes and’, debate meaning
- tape to white board and affinity diagram
- existential dread, snack break, repeat
Pulling insights from our wall was an example of emergence; something concealed slowly coming into view. Here are some common themes on learning that have emerged.
Travel time can be a restorative reprieve from commitments and responsibilities.
From the beginning of the project, the focus has been on what partial autonomy will allow people to do. After all, freedom from the responsibilities of driving will give people the freedom to spend their time in other ways, like to learn.
Our interviews suggested something that made us reconsider the order of our two freedoms, however. People told us that sometimes travel is freedom from their daily routines and responsibilities. One of our participants who is a new mom said that she cherishes the chance to drive to the grocery store “because it’s my one chance to listen to audiobooks or podcasts”.
Learning constraints include trip duration, physical limitations, and mental state.
Our research participants listed many reasons why travel time isn’t currently used to learn or be productive. One participant details why trip time matters, stating that
“When I had a 5 minute commute, I just looked out the window or listened to music. It takes 10–15 minutes to get into a context for my brain so I don’t like context switching with an audiobook or podcast”.
Trip duration was further expounded upon by faculty advisor Hirokazu Shirado by describing long tail distributions.
In addition to time, the mental state of the learner must be factored in. College students we spoke with described having an intention to use travel time to complete homework, but this intent yielded mixed results.
One student described planning to do homework on the way to their spring break destination but was thwarted by feeling self-conscious by strangers watching her work. Another described wanting to do work on a plane but being too exhausted and opting to sleep instead.
Overstimulation
Why is the above situation funny? I’d argue it’s because we all instantly identify with this moment. When demands on our attention are high, we limit stimuli to focus better. This lines up well with a model that our faculty advisor and motivational speaker Megan Guidi helped our team envision.
This model is showing that travel and learning can be loosely categorized as ‘hard’ and ‘easy’. Hard travel might be white-out blizzard conditions or just a sunny day driving through Manhattan at rush hour. Easy learning includes activities like watching TikTok or listening to a podcast. This model is a reflection of things we heard our users say during interviews. When travel and learning are both easy, the environment is ripe for learning. We’ll call this yellow square above ‘square of learning environment’ for now.
Mismatch
If you’re a traveler and you find yourself in any of the other quadrants, you won’t have an optimal learning environment. The content and the context just aren’t aligned. For example, even if you’re listening to an easy-listening podcast that doesn’t require deep thought, chances are you’ll turn the volume down as you try to park (easy learning, hard travel). Likewise, just because you’re driving on flat, straight I-70 through eastern Kansas doesn’t mean you’re going to break out the differential equations and go to town (easy travel, hard learning).
Now Go Make Stuff
With our research insights in tow, it was time to start initial ideation for prototypes. We relied on a generative activity that had served us well in the past, Crazy 8’s, to bridge the gap between research and design. We then discussed as a team which ideas best captured our research insights.
Remember the punnet square from the last section? Well, we updated it to represent how our design should expand the ‘square of learning environment’.
Prototype ideas included experiences that ranged from immersive and restorative to enhancing user’s growth goals. We even discussed how in the future the car would become another device, fully integrated with a user’s set of personal data. This complete integration would fully realize human-machine teaming, a research area of interest we inherited from our project prompt. Basically, this concept looks at the car less like a tool and more like a teammate. As a teammate, how can the car provide context-appropriate content based on vast amounts of data you generate through wearables and your private information ecosystem? Put simply, the car will know you better than you know yourself.
Do you remember the historical era known as ‘pre-Spotify’? I do, faintly. People actually listened to the radio. Every now and then, the DJ picked the exact perfect song for your mood and circumstance. We’re looking to replicate that experience but cranked up to 11. In short,
Next Steps
We’ve begun discussions on how we can run a participatory design session with users to generate ideas of what contextually appropriate content could look like. One potential future scenario:
You’ve just left the gym. Your autonomous vehicle pulls up to the curb as you wipe sweat from your brow. Exhausted, you climb into the car. The car has been tracking your biomarkers from your Apple watch and Apple brain chip during your workout. The vehicle has adjusted the internal environment appropriately, giving you space to stretch your legs after all those heavy squats. It blows a nice breeze to lower your heart rate. The environment is not only comfortable, it’s now more conducive to learning. Which is good, because your car also knows you want to continue the Roman history podcast you were listening to in the gym.
To enhance your learning, the car’s screens and internal mixed reality system reflect events from the podcast. The walls of your car light up with images of the fire that engulfed the slums of the district south of Palatine Hill that Nero used to further his political agenda. Your adaptive sound system makes you feel like the podcast host is talking face to face with you. Ever the vigilant tutor, your car notes moments when your attention wanders and adds those facts to a list to circle back to later. You’re immersed in events from 64 AD through the future of learning.
“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.”
