CS3240 Workshop Case Study
This single-page portfolio documents my Workshop-focused contribution within PropBank. It shows the end-to-end design process from user research to insights, design principles, hi-fidelity prototype evidence, and reflection. The scope is Workshop-first, with ecosystem references only where they influence Workshop decisions.
Foundation
Workshop is a service within PropBank that allows users to find and save cosplay-related guides for practical execution.
PropBank is a cosplay and ACGN preparation platform that helps users discover events, source cosplay items with trust, access practical guides, and connect with the community in one integrated ecosystem.
It consists of five services: CoNews (event discovery), Gear Library and Marketplace (buy, sell, and rent cosplay items), Workshop (tutorials and guides), PropScan (AI-powered prop identification), and Creator Hub (community sharing).
Workshop is a structured instructional service where users can share and learn cosplay creation methods. It centralizes actionable resources for the ACGN community in the same app ecosystem.
Unlike Creator Hub, which supports casual sharing and event memories, Workshop focuses on step-by-step execution and feasibility.
Learning resources are important, but they are fragmented and used on-demand rather than consumed algorithmically. Interviewees did not describe guides as content they constantly browse. They search when they hit specific needs, such as makeup, prop construction, styling, or a character-specific challenge.
This research therefore examined what makes a guide collection useful and actionable for real decision-making, which is further detailed in the user research sections below.
Phase 1
Users in the cosplay and ACGN space often search for guides only when they hit a specific task (makeup, prop making, styling), but current resources are fragmented and hard to evaluate quickly. The main Workshop problem is commitment uncertainty: users cannot easily judge time, complexity, and budget before opening a tutorial.
How might we help users decide if a cosplay guide is feasible for their schedule and skill level, while keeping learning focused, trustworthy, and searchable?
Phase 2
Synthesis points used to map evidence into design action.
"I want guides tailoered to specific characters and skill levels
"I prefer guides that clearly state time, difficulty and resources upfront"
Users consistently valued a system capable of searching for guides that match their specific cosplay goals and constraints.
They expressed frustration with generic resources that may assume knowledge or materials they do not have, leading to wasted time and effort. This finding highlights the importance of upfront metadata and search filters in Workshop design to reduce commitment uncertainty and increase guide relevance.
"I want to know if a guide creator is credible"
"I need to know if I can do it or not"
Users often express their uncertainty about whether they can successfully follow a guide, especially for complex cosplays. They want to see trust cues such as creator credibility, user ratings, and hints of difficult steps before committing to a guide.
This finding suggests that Workshop should surface trust indicators and detailed previews to help users quickly assess feasibility and reduce the risk of wasted effort.
"Can do or not? Sometimes buying might be better"
"I care about practicality (difficulty, time) before committing"
Users expressed a common dilemma of whether to make a cosplay using a guide or buy it directly. They want to compare the effort, cost, and time of making versus buying before deciding.
This finding indicates that Workshop should facilitate a seamless comparison with the Marketplace for users to explore buy options alongside their make plans, enabling informed decision-making.
These questions translate the key findings into design opportunities for Workshop.
Design Direction: Create filters for search and allow guides to list the skills assumed and resource costs.
Design Direction: Create a review system and add a "Replicability" percentage to each guide.
Design Direction: Create a comparison tool that groups Marketplace and Workshop content into one aggregated view.
Age: 23
Charmaine is a relatively seasoned cosplayer who has completed multiple cosplays since age 18. She is not a professional cosplayer and still benefits from learning new techniques for the characters she likes. As a final-year student, she has heavy academic workload and must spend her time and resources carefully to continue cosplaying while completing her studies.
Workshop
Marketplace (secondary comparison)
Workflow Breakdown
The overall workflow is broken into three concrete user tasks so each stage can be designed, tested, and iterated independently.
Goal: The user searches for a workshop guide so they can follow it later after gathering required materials.
Steps 3 to 6 may be repeated in any order during search refinement.
User S and K says:
"Costumes these days are adding too many things. It gets very difficult."
"Can do or not?"
Goal: The user evaluates whether a shortlisted guide is worth the effort by checking summary details, author credibility, and community reviews.
User S says:
"Can do or not? buying can be better."
Goal: The user decides whether to create a cosplay using a guide or buy directly by comparing cost, effort, and expected outcome.
Phase 3
This section showcases the iterative approach from low-fidelity sketches through mid-fidelity flows to the final high-fidelity prototype. Each phase demonstrates design refinements based on research insights.
Design systems help us standardize elements and signifiers across the app, keeping consistent typography, color palette, components (CTA buttons, search bars, filter chips, snackbars), and navigation patterns. This ensures visual coherence across all five services and reduces user confusion when moving between Workshop, Marketplace, and other services.
The hi-fi prototype demonstrates Workshop-first flows where users can discover a guide, evaluate feasibility quickly, and decide whether to make or source items. The interaction model keeps content scannable and actionable rather than feed-heavy.
Phase 4
PropScan strengthens cross-service integration and fits the interview preference for non-intrusive, task-based AI scope.
Phase 5
Initial concept emphasized discovery browsing; users found it too passive and noisy.
Refocused on active search and guide-preview cards and filters.
Added cross service interactions such as make-vs-buy handoff to Marketplace for practical decision support.
Iterations are non-exhaustive and was an ongoing process. These are just examples.
Back to topPhase 6
Our design decisions were shaped directly by user evidence. I initially expected the Workshop feature to depend on targeted algorithms and signifiers to reduce confusion. Through semi-structured interviews, we learned that users prefer active search over algorithmic recommendations, and this helped us identify which signifiers truly matter to them.
The most significant challenge was deciding which features to explore deeply. Since this assignment requires three elaborated features, I focused on a consumer-centered approach rather than a creator-centered one. This best reflects what people looking to cosplay want based on our interview data, but it also means creator reward mechanisms were deprioritized. A future direction is to investigate how to incentivize high-quality guide creation by collecting input from users who create this content.
The final solutions primarily support users as consumers of Workshop content. The selected features reduce cognitive burden and commitment uncertainty when deciding whether to make or buy a cosplay, which is a key decision in the ACGN community.