Role
UX researcher, interaction designer, and visual designer
INFO360 FINAL PROJECT | GAN LUO | TEAM 23
CardLogic
A UX case study for an AI-assisted sports card price checker that helps collectors and small sellers identify cards, compare recent sales, and make clearer buying or selling decisions.
Project Overview
Timeline
Semester-long UX process from research to prototype evaluation
Tools
Figma, user interviews, competitive research, testing notes, and feedback
Project Type
Academic UX case study for a sports card marketplace app
01 / Problem
Collectors need more support before they buy.
Sports trading card collectors often face uncertainty when deciding whether to buy a card. A listing may show the card image and price, but it may not clearly explain the card's value, recent market movement, or seller reliability.
This can be especially difficult for newer collectors. They may not know if a card is fairly priced, whether similar cards recently sold for less, or whether the seller has a trustworthy history. Many existing platforms focus on listing and selling cards, but they do not always help users understand the reasoning behind a purchase decision.
Design challenge: How might a marketplace app help collectors compare card
information, market trends, and seller credibility before making a purchase?
Who experiences it?
Newer collectors, casual buyers, and collectors entering a new sport or card set are most affected because they may not have enough experience to judge value quickly.
Why does it matter?
Trading cards can involve meaningful financial and emotional value. A poor purchase decision can lead to wasted money, frustration, and lower trust in online collecting communities.
Why do current platforms fall short?
Many marketplaces separate the listing from the evidence behind the listing. Users may need to leave the platform to check past sales, compare prices, or understand seller credibility.
Research basis
The problem direction was informed by interviews at the Front Row Card Show in San Diego, competitive analysis, literature research, and feedback from users who wanted clearer price signals.
Research evidence
In user research, collectors and sellers said they often check eBay sold listings, compare prices across multiple platforms, and worry about overpaying. A key user need was a faster and more reliable way to compare recent sales, card condition, grading, and market trends in one place.
Users need a faster way to compare card prices.
Users want clear price explanations, not just one number.
Recent sold prices are more useful than listed prices.
Card value depends on grade, player, year, rarity, and condition.
02 / Evaluation
Testing focused on confidence, clarity, and decision-making.
I evaluated the prototype through usability testing, a cognitive walkthrough, heuristic review, and informal user feedback. The goal was to learn whether users could understand the card details, compare market information, review seller credibility, and decide whether they felt comfortable buying.
Evaluation Question
Can users move from search to purchase decision while understanding price risk and seller trust?
Success Criteria
Users should complete core tasks without confusion, explain what influenced their decision, and identify whether a card seems fairly priced.
Participants
I tested with two students. One participant had previous experience purchasing trading cards online, while the other often shops online but had limited sports card experience.
Tasks
Users opened the app, found the price checking feature, scanned or searched for a card, reviewed card details and market price information, saved a card to a watchlist, and tried to create a sell listing.
What Worked Well
Users understood that the app could scan cards or let them manually enter card information. They also understood the overall flow from card recognition to price estimate.
Problems Users Experienced
Users were unsure whether market price meant current listing prices or recent sold prices. One user wanted clearer guidance during AI recognition, and another wanted a more visible sell listing button.
What I Learned
Evaluation showed that the app should separate recent sold prices from active listings, explain AI confidence, and make save/rescan/sell actions easier to notice.
Methods
The evaluation combined usability testing, cognitive walkthrough notes, heuristic evaluation, and feedback collected after task completion.
03 / Design Rationale
The interface is designed to reduce uncertainty.
CardLogic includes card information, market trend data, seller credibility, and a clear purchase decision flow because these are the areas where collectors often need support. Instead of forcing users to search across multiple sources, the design brings the most relevant decision-making information into one organized view.
Visual hierarchy was important because too much data can make the app feel intimidating. The design gives priority to the card name, condition, current price, recent trend, and seller credibility. More detailed information can still be available, but it should not overwhelm the first screen.
One trade-off was choosing simplicity over showing every possible market detail at once. A sports card market can include many variables, but the prototype focuses on the information most useful for a first purchase decision.
Design decision
Reasoning
Trade-off
Include AI image scanning and manual search
Research showed that users want a fast way to identify a card, but scanning may not always work.
The design supports both quick scanning and a backup manual path.
Separate recent sales from estimated value
Testing showed users were confused when market price labels did not explain the data source.
The interface needs clearer labels, even if that uses more screen space.
Prioritize must-have features first
Upload image, manual search, estimated price, and recent sales comparison had the highest value.
More advanced ideas such as alerts and full platform comparison were left for later.
Use a guided flow from upload to decision
Users need to confirm card details before trusting an estimated market price.
The flow adds steps, but it reduces errors from incorrect AI recognition.
04 / Design Solution
Core features support a clearer buying journey.
The solution is a mobile marketplace experience that helps users move from search to comparison to purchase decision with more context and less guesswork.
Open app
Upload or search card
Confirm AI details
View price estimate
Save or create listing
AI Card Scanner and Manual Search
Users can upload a card image or manually search by player, year, brand, and grade.
AI Recognition Review
The system identifies player name, year, set, card number, grade, and confidence score for user confirmation.
Estimated Market Price
Users see an estimated value plus recent sale data such as last sold, average price, highest sale, and lowest sale.
Price Trend Chart
A simple chart helps users understand whether the card value is rising, falling, or staying stable.
Watchlist and Sell Listing Path
Users can save cards for later and move toward creating a sell listing after checking the estimate.
How the solution addresses the problem
CardLogic connects card identification, recent sales, price explanation, and action choices in one flow. Instead of asking users to check several platforms manually, it gives them a focused summary for deciding whether to buy, sell, save, or compare a sports card.
05 / Reflection & Learning
Design became a process of evidence and iteration.
This project helped me understand that UX design is not only about how an interface looks. The more important challenge is understanding what users need, where they feel uncertain, and how the design can support their decisions.
Moving from problem definition to prototype made me think carefully about each design choice. I learned that a feature should have a clear purpose, and that feedback can change the direction of a design in useful ways.
Evaluation was especially valuable because it showed gaps that were not obvious while I was designing. User feedback helped me improve labels, simplify information, and think more clearly about trust, scanning errors, and the need for clearer next steps.
Design thinking
I learned to move from a broad problem to a more specific user need, then test whether the design actually supported that need.
Challenges
The hardest part was deciding how to present price information without making the interface too crowded. Card pricing can include scanning, rescan, recent sales, estimated value, market trends, and listing actions.
How my idea changed
At first, I focused mostly on showing card prices. After feedback, I realized the whole user flow also needed to be clearer, especially the upload, rescan, and price explanation steps.
What I would improve
With more time, I would improve the recognition process, add clearer error messages when scanning fails, test with more collectors, and add stronger explanations for price ranges.
06 / Limitations & Future Plan
The concept needs more testing and real data to become stronger.
Limitations
- The project used a limited number of user tests.
- The prototype does not connect to real-time market data.
- The AI recognition concept assumes uploaded card images are clear enough to detect details accurately.
- The design mainly focuses on sports trading cards and may not fully apply to Pokemon or One Piece cards.
Assumptions and Trade-Offs
- The design assumes users are willing to review extra information before buying.
- The prototype prioritizes clarity over advanced tools for expert collectors.
- AI confidence and estimated value must be explained clearly so users do not overtrust the system.
- Adding more guidance may make the buying or selling flow feel slower for experienced users.
Future Plan
- Add real-time price and sale history data.
- Add clearer price ranges and separate recent sold prices from active listings.
- Improve AI recognition guidance, rescan options, and error messages.
- Test the design with more collectors and sellers.
- Add stronger comparison tools across platforms.
- Support collection tracking, watchlists, and more card categories.
07 / References
Reference placeholders
These references come from the research notes used in the project. They should be checked and finalized in the citation style required by the course before submission.
- Grand View Research. (n.d.). Sports trading cards market size, share, and trends analysis report.
- Chubb. (n.d.). The power behind the sports memorabilia market.
- Hilbert, T. (2024). Uncovering the potential for investment-grade returns in collectible sports and Pokemon cards. College of Wooster.
- OECD. (n.d.). Trust in peer platform markets: Consumer survey findings.
- Veltri, G. A. (2023). The impact of online platform transparency of information on consumers' choices.
- Algolia. (n.d.). How image search has changed online shopping.
- eBay, 130point, Card Ladder, and CardHobby were reviewed as competitive or market reference tools.