The Mobile Mentor
Honda x MHCI
See Final Design
Client Brief:
Reimagine the in-vehicle experience as a dynamic learning environment, integrating Gen Z's tech-centric lifestyle with their commute.
Timeline: Jan 2023 - Aug 2023
Client: Honda Research Institute/99P Labs
My Roles: UX Research Lead and Product Designer
Process and Project Timeline
Background
The automotive and tech industries are experiencing rapid evolution. By 2040, electric car sales will surpass traditional combustion engines, marking a pivotal shift in the industry. The rise of semi and fully autonomous vehicles will further redefine the automotive sector.
In this rapidly transforming landscape, Boston Consulting Group highlights a “narrow window of opportunity” for companies, like Honda, to anticipate and navigate disruptions. This evolving landscape presents significant growth potential in digital services in and around vehicles, opening new sources of revenue.
Challenge
We partnered with Honda’s 99P Labs to reimagine in-transit time for Gen Z, who will be a major share of the car-buying market in the 2030s. Our innovative project aimed to “reframe the vehicle as an intelligent mobile learning environment,” transforming travel time into educational opportunities. This initiative aimed to align with Gen Z’s tech-integrated lifestyle and help prepare Honda for the future. Our goal was to update the usual ideas of education and travel to fit the needs and interests of the next generation and incorporate emerging technologies like location awareness and AR/VR.
Research
Exploring The State of In-Transit Education
The project began with exploratory secondary research. We transitioned to primary research methods as we established a focus.
Research Questions
Gen Z’s Perspective
What are Gen Z’s motivations and values regarding learning and transit?
How does Gen Z’s attitude towards transit and car ownership differ from previous generations?
What prompts an individual to seek knowledge or entertainment while in transit?
Technological Advances
How will the rise of semi and autonomous vehicles impact the potential for in-car learning experiences?
What emerging technologies (e.g., AR, IoT, AI) can be leveraged to enhance the in-vehicle learning experience?
In-transit Learning
What forms of in-transit entertainment or education have been preferred in the past, and how have they evolved?
What environments and tools are most conducive to learning on the go?
Secondary Research
Literature review
Our literature review encompassed 15 diverse topics, including Gen Z’s values, experiential learning, automotive trends, educational gaming, human-machine collaboration, in-vehicle seating, and cognitive load.
Expert Interviews
We conducted interviews with 10+ education and technology subject matter experts to understand the history, feasibility, and challenges of in-transit learning. Major takeaways included the potential and limitations of AR/VR technologies in vehicles and tailoring learning experiences to trip context and duration. Experts emphasized balancing educational engagement with concerns like cognitive load and privacy.
Dealership Visits
We visited four local car dealerships to talk with salespeople and managers about their customer bases and what people look for when buying a car. We also learned about current and emerging technology offerings, like head-up displays.
Audi head-up display
Mercedes head-up display
Trip to Mercedes-Benz
Tesla Infotainment system
Exploring the purchasing process at Carvana
Primary Research
Intercept Interviews
We traveled alongside participants to understand what motivates Gen Z to seek knowledge while traveling. By walking, riding, and bussing together, we immersed ourselves in their worlds, observing firsthand how, what, and why they learn during transit. Our focus was to understand the triggers that capture their attention and the types of learning they actively pursue on the go.
In-depth Interviews
To gain a comprehensive understanding of learning behaviors, not just limited to in-transit experiences, I proposed conducting in-depth interviews. This approach aimed to uncover underlying learning motivations and broader learning dynamics essential for our project, especially in the context of non-traditional learning environments. We weren’t getting this information with previous methods.
Leading team through in-depth interview analysis.
Insights
Personalized learning works best for Gen Z, but on-the-go resources are lacking.
Gen Z appreciates tailored learning experiences but lacks suitable resources during travel. They engage in various activities, like listening to podcasts, reading, and chatting during transit. They have to go out of their way to find these resources. However, they don’t identify these activities as “learning” since they link learning with formal education structures.
People don’t see travel environments as ideal spaces for learning.
The confined nature of vehicles, absence of learning setups (like desk space), and potential for motion sickness are barriers to learning on the go. Other limiting factors include time limitations and space constraints. Therefore, it’s uncommon to engage in deep concentration or intense work while traveling.
The best on-the-go learning for Gen-Z integrates low-effort learning and travel conditions.
The most effective and desirable on-the-go learning for Gen Z seamlessly integrates low-effort (easy) learning with low-effort (easy) travel conditions. Any other scenario overburdens one’s cognitive load and is inhospitable to learning.
Examples of easy learning and easy travel include watching TikTok on the bus or listening to a podcast while walking a familiar route.
Opportunities
With insights from our primary and secondary research, we identified an opportunity to reimagine the vehicle experience as a dynamic learning environment, harmonizing Gen Z's tech-centric lifestyle with their commute.
We established three design principles, each linked to an insight, to shape our design solution.
01
Personalized learning works best for Gen Z, but on-the-go resources are lacking.
Make it Personalized
Design engaging, memorable learning experiences tailored to people’s preferences, similar to how they consume podcasts, music, TV shows, and social media. The design should adapt to individual learning styles and interests, making transit time a personalized educational journey.
02
People don’t see travel environments as ideal spaces for learning.
Make it Supportive
Create a solution that supports productive learning even in confined or motion-inducing settings. The design should create supportive learning environments, optimizing available space and technology to provide comfortable learning setups.
03
The best on-the-go learning integrates low-effort learning and travel conditions.
Make it Easy
Focus on low-effort, integrated learning in line with people’s transit habits. The design should leverage emerging technology and integrate learning seamlessly with fluctuating travel conditions.
Our design must cater to the post-2030 era when advancements in AI, machine learning, and autonomous vehicles are expected to significantly reduce the mental burden of both travel and learning.
Ideation, Prototyping & Testing
Using our design principles and insights, we began ideating various solutions and narrowed down on these promising concepts.
Switching between Learning Modalities
In this future, cars are integrated into a vast information network, with access to new and diverse resources. We can enable passengers to interact with various learning platforms while on the move.
AI Content Curation
This concept shows an AI-driven content curation experience in the car, centered around a conversational agent. This agent facilitates the learning journey, enabling passengers to easily discover, save, and share information of interest. Key features include a conversational guide, interactive feedback, and flexible control modes.
Increased Learning Capacity
The car enhances learning by recommending new content and helping set educational goals. It detects when passengers lose interest and gently nudges them to explore experiences beyond their comfort zone. The car also uses AR and VR to build immersive and expansive educational experiences.
Participatory Design
After we narrowed down concepts from ideation, we ran a series of participatory design sessions where we co-created designs with people. Then, we asked for feedback on our concepts from ideation.
Throughout our prototype development process, participants were our co-designers. Their interactions with the prototypes contributed to shaping their own Mobile Mentor.
As our project progressed, our design sprints transitioned from generative to evaluative testing. Initially, we focused on broad exploration, discovering what users wanted from their Mobile Mentor. What do people want to learn in their Mobile Mentor? How do they want it delivered?
We used the following success metrics: ease, supportiveness, personalization, and desirability (compared to the current state).
Prototype Testing
We used our generative research findings to inform an initial prototype that we tested and iterated. We repeated this process of prototyping and iterating four more times. This brought us to our high-fidelity prototype, which we tested with users.
Prototype testing at various fidelity levels.
We simulated an in-car experience for participants using a ‘Wizard of Oz’ prototype setup for immersive augmented reality, using printed transparency sheets as window overlays. An iPad mounted on the headrest displayed our Mobile Mentor’s visuals and voice via Zoom, enabling real-time interaction. Our tests covered home screen interaction, pre-session mood and learning assessments, in-session engagement, and personalized recommendations, concluding with a learning summary.
Using transparency sheets to mimic AR window overlays.
The learning content shown in a user testing session from the team’s point of view (left side of screen) and the participant’s view (right side of screen).
Key Findings
Preference for Informal Learning
Users consistently preferred formal learning content to be delivered in informal ways, aligning with our research favoring a less rigid and more engaging learning approach.
Catering to Various Mindsets
Users’ mindsets in a car vary between being ready and eager for the day, winding down, or somewhere in between. Learning is most effective during active, higher-arousal times, like before work.
Need for Content Transparency
To maintain trust in the system, it’s important that people know why the Mobile Mentor suggests specific actions or content.
Trust in AI Learning
Participants were comfortable relying on the Mobile Mentor’s capabilities to manage their learning journey. After all, they trust AI to manage other areas of their life, like entertainment (Netflix recommendations), search (ChatGPT), assistance (Siri, Alexa), and navigation (Google Maps).
Our findings provided valuable insights into user behaviors, preferences, and needs within the project context, helping inform design decisions and recommendations for the Mobile Mentor.
Interaction Flow
After we tested our high-fidelity prototype with users, I then created the Mobile Mentor’s interaction flow detailing its core path and functionality.
The Mobile Mentor “Happy Path” is the essence of the mobile mentor experience. From here, we can imagine all of the offshoot experiences a user could run into — like motion sickness, being tired, or wanting to reroute to pick up groceries.
After our final prototype testing and after creating the interaction flow, we created a final conceptual prototype video to show our client.
Final Design
The Mobile Mentor is an in-vehicle, context-aware learning companion designed to enhance Gen Z’s transit experience by merging personalized, adaptive learning with their everyday commutes.
Core Design Features
Conversation-centered learning
The experience is based on conversations between the passenger and the Mobile Mentor. Conversations enable hands-free, convenient communication. The inclusion of screens, AR, and gestures enhances the experience with intuitive visual cues and supplementary information. Riders ask their Mobile Mentor questions and have enriching discussions, as they would with a smart friend.
Using geolocation to create an immersive learning experience
The Mobile Mentor enriches learning by integrating the vehicle’s geolocation with educational content, creating an experience akin to a personalized tour guide. It dynamically links lessons to the passenger’s immediate surroundings, highlighting points of interest that align with their learning goals.
Integrating with the rest of a user’s life
The Mobile Mentor integrates data from passengers’ personal devices and apps, enriching the learning experience with tailored content. This enables the system to fine-tune its recommendations based on the passenger’s interests and current learning progress. It also monitors health conditions and reminds passengers of essential self-care practices.
Delivering content that matches the user’s mental state
The Mobile Mentor customizes its interactions and content to match the user’s mental state and daily rhythm. It assesses the user’s openness to information, adapting its tone and teaching approach to suit various times of the day, ensuring their cognitive load is never overwhelmed.
Efficient learning wrap-up
The Mobile Mentor dedicates time at the end of a trip for a recap, summarizing the topics discussed. This feature prepares passengers for their destination. It alleviates the stress of planning post-transit activities, as passengers can rely on Mobile Mentor for seamless transitions.
Outcome
The project directly influenced 99P Labs’ future initiatives. For example, it inspired a new project on human-AI teaming field testing methods, drawing from our prototype’s chatbot integration and real-time virtual avatar.
Project findings were distributed to key groups within Honda, including a business innovation team at American Honda Motor, Honda Research Institute USA executives, and the Global Human Factors Research Network covering US, EU, and JP entities.
“We anticipate this capstone will continue to have a ripple effect on our research methods at 99P and with our partners.” — Client
Reflection
This project was a journey outside my comfort zone, challenging me to use new skills and technologies.
Integrating Complex Systems to Innovate. This project was interesting and challenging because it navigated the intersection of many different systems working together. This involved synthesizing disparate elements into cohesive and creative bespoke models to communicate ideas.
Using AI to Improve Workflow. Our project's forward-looking nature inspired us to integrate emerging technology into our workflow, including ChatGPT, AI Voice Generators, and Unreal Engine.
Stay Strategic and Human-Centered. My experience with the research phase underscored the importance of avoiding premature solution-focused thinking.
Want to read more about our process? Check out our Medium Blog