Case Study · Asurion
Joy AI
Would people trust an AI to help them trade in their device?
A concept evaluation study exploring how participants responded to AI voice guidance and AR scanning in a device trade-in experience—and what it would take for them to trust it.
Role
UX Researcher
Team
Product and Design
Platform
Mobile concept
Study
Dscout talk-aloud study
Study type
Concept evaluation
Methods
Dscout talk-aloud study
Participants
15 participants
Research outcome
Trust-centered design direction
The opportunity
Joy AI was a product concept, not yet built, that imagined a new kind of device trade-in experience. The concept combined AR scanning, conversational AI voice guidance, and a clean commerce UI to help customers assess and trade in their devices without needing to visit a store.
The product team needed to know whether this concept was worth building. Specifically: would customers trust it? Would the AI and AR features feel helpful, or would they create more questions than they answered? This study was designed to find out before any significant development investment was made.
What we needed to learn
The team had a concept but not yet evidence. Before investing in development, we needed to understand how real people would react, not just whether they could complete tasks, but whether they would actually trust this kind of experience.
Research questions
- 1How do customers react to AR device identification, and what concerns come up around camera access and accuracy?
- 2What does a trade-in process need to do to feel trustworthy? What information do people need, and when?
- 3How do people navigate switching between AR, voice, buttons, and chat input in a single flow?
- 4Where does the experience feel intuitive, and where does it create friction or confusion?
Hypotheses going in
- People would hesitate around the AR scanner, both the act of granting camera access and doubts about whether it would identify their device correctly.
- Existing expectations about device trade-in value would shape how participants judged the concept's fairness, regardless of the actual offer.
What we learned
The UI foundation held up
The clean structure, browse-by-category, and suggested upgrades all worked. The gaps were about explanation, guidance, and reassurance, not the layout.
AR scanning had real potential
Participants responded well to the camera scan, but they needed a manual fallback, clearer accuracy feedback, and some reassurance about privacy.
AI voice missed the mark
Voice narration felt more like a demo feature than a useful guide. It needed to be optional, and tied to something participants actually wanted help with.
How I approached the research
15 participants completed a remote talk-aloud study via Dscout, working through 6 prompts covering the full trade-in journey from home screen to checkout confirmation.
Research decision
This study focused on trust, expectations, and perceptions rather than task completion rates or usability metrics alone. The reason: a feature can be perfectly usable and still not be trusted. For an AI concept involving camera access and automated device valuation, we needed to understand the emotional and cognitive response, not just whether people could get through the flow.
Inclusive research consideration
Participants came in with very different levels of familiarity with AI tools. Some had used voice assistants and AR features before; others hadn't. That gap mattered when interpreting reactions to the AI voice and AR scanner, what felt intuitive to one person felt unfamiliar or uncertain to another. Findings were analyzed with that variation in mind rather than treating all hesitation as a usability failure.
Research findings
Findings organized by area. Each section includes what worked and what raised questions.
Some participant videos and interface screens have been omitted to protect participant privacy and confidential company information. The selected artifacts represent the key findings and decision points from the study.
Participants explored the trade-in options during the Dscout talk-aloud study. Categories and visible pricing helped them browse, while suggested upgrades raised questions about personalization. Participants also questioned trade-in values, device labels, and which devices were eligible.
Task: AI Overview — Apple TV 4K Page
Participants evaluated the product overview with AI voice guidance. Some responded positively to the voice and animation, but many found the narration unnecessary, repetitive, or disconnected from the actions available. The findings informed recommendations for optional narration that provides useful guidance at the point of a decision.
Participants evaluated how they would choose a device to trade in while reviewing an Apple TV 4K product page. Seeing multiple trade-in options and explanatory buttons helped clarify the step, but some participants were surprised that they could trade across device categories and were unsure where to begin.
The camera scan created excitement and reassurance, but participants still had questions about accuracy, privacy, and what the green check confirmed.
Participants evaluated the option to share a trade-in with other people. Sharing felt more reliable than sending screenshots and helped build confidence for family or shared-device decisions. However, participants still had questions about how invitations worked and what information would be shared.
The cart felt familiar and easy to trust, with visible totals and Apple Pay standing out positively. Participants still needed clearer trade-in completion, taxes, shipping, confirmation, and next-step guidance.
Design opportunities
These are the areas where the research pointed most clearly toward a change, and why.
UI & Home Screen
Keep the clean structure, browse by category, suggested upgrades, but clarify labels and device tags to reduce confusion about what's personalized versus generic.
AR Scanner
Add a manual fallback if the scan doesn't work or gets the wrong device. Ask for camera permission upfront and be explicit that photos are not stored.
AI Voice
Make narration optional with a clear toggle. Reframe the voice from a flashy intro to a contextual guide, something that explains compatibility, answers common questions, or walks through trade-in value.
Trade-In Clarity
Explicitly tell people that cross-category trades are allowed. Move 'Trade in a different device' higher in the flow. AI could surface eligible devices based on account history.
Sharing Feature
Let people share via link, SMS, or email. Give them control over what's included, value only versus full device details. Consider surfacing this contextually rather than as a fixed step.
Trade-In Value & Confirmation
Show taxes and shipping before payment, not after. Provide a clear confirmation screen and a receipt. If AI is explaining the trade-in value, make that explanation visible before the person commits.
Business Impact
What changed because of this research
Helped the team distinguish which AI features increased trust and which created hesitation, before any significant development investment was made.
Shifted design priorities from adding more AI functionality to improving transparency, user control, and confidence throughout the trade-in experience.
Provided clear direction for future iterations, including optional AI guidance, stronger camera permissions messaging, and clearer trade-in explanations.
What surprised us
Not everything landed the way we expected. A few findings shifted how we understood the concept.
01
The sharing feature resonated more than expected
We thought sharing might feel niche. Instead, several participants immediately connected it to trust, sharing the trade-in with someone else meant they couldn't be taken advantage of. That was a use case we hadn't fully anticipated.
02
The green check created new confusion
The AR scanner's success state, a green checkmark, was supposed to signal that the scan worked. But participants read it differently. Some thought it meant the trade-in itself had been approved. A small visual element was carrying more weight than intended.
03
AI voice divided the room clearly
The split wasn't random. Participants who already used voice assistants regularly were more open to it. Those who didn't found it intrusive. That pattern made it clear the feature needed to be optional, not removed, but not assumed.
Looking back
This project reinforced something I think about a lot in AI concept research: novelty and trust are in tension. People can find a feature impressive and still not believe it. The "wow factor" of AR scanning got participants talking, but their real questions were about accuracy, privacy, and fairness, none of which the UI was answering clearly at that point. The study helped the team see that the work wasn't just adding polish. It was building the conditions for trust from the ground up.
Synthesis
Overall Takeaways & Opportunities
Across the concept evaluation, participants were open to AI and AR support in the trade-in experience, but adoption depended on clarity, transparency, and control. The strongest opportunities focused on explaining value, guiding decisions, protecting privacy, and making next steps easier to understand.
Takeaways
- Participants were open to AI and AR support, but needed clearer explanations and more confidence that trade-in values were accurate and fair.
- The underlying browsing and checkout structure felt familiar. The biggest opportunities were explanation, guidance, and reassurance.
- New features such as the AR scanner, AI voice, and sharing should remain optional enhancements rather than required steps.
Opportunities
- UI and home screen: Keep the clean category, brand, and suggested-upgrade structure, while clarifying device labels and recommendations.
- AR scanner: Preserve the “wow factor,” but provide manual fallback entry, accuracy checks, and clear privacy assurances.
- AI voice: Shift from a flashy introduction to optional, useful guidance that is detailed and connected to the next action.
- Sharing trade-in details: Support family and shared-device decisions with simple SMS or email invitations and clear control over what information is shared.
- Trade-in value and confirmation: Explain how value is calculated and provide clear confirmation and next-step guidance, including shipping and receipt information.
Overall participant perspective
Participants generally viewed the concept as modern and convenient, but willingness to use the experience depended on fair trade-in values, a transparent process, and clear explanations of what the AI was doing at each step.
Many participants said they would consider using the experience in real life because of its convenience. Adoption still depended on confidence in the valuation, transparency about the process, and clear next-step guidance.