Case Study · Master's Thesis
MentorMap
Designing a clearer path to mentorship for architecture students
Overview
An architecture student, late at night in the studio, stuck on a rendering, a model, or a tool they were expected to "already know."
The core insight that shaped MentorMap: uncertainty about whom to approach, what to ask, and whether a question even merits asking actively discourages help-seeking behavior. Students weren't failing to seek mentorship because of lack of effort; they lacked visibility into the support that already existed around them.
MentorMap is a hybrid mentorship platform designed to make peer expertise visible within architecture studios, combining physical touchpoints with digital depth, context, and actionability.
My Role
End-to-End UX · Master's Thesis
End-to-end UX design: research, synthesis, ideation, interaction design, prototyping, usability testing, and validation across a 12-month thesis project.
December 2024 – December 2025. Thomas Jefferson University, Master's Degree in UX & Interaction Design.
How Big Is the Problem
The research speaks.
12 architecture students across academic years at Thomas Jefferson University, plus 1 faculty stakeholder interview, revealing the full arc of mentorship friction from first year to thesis.
Across every year of study, the same pattern emerged: students weren't asking for help not because help wasn't available, but because the path to it was invisible. The question wasn't "is there support?" but rather "who, exactly, and how do I ask without looking incompetent?"
Problem Statement
"How Might We reduce poor peer mentor matches by making it easier for architecture students to see who can support them with specific skills, tools, or guidance they need at each stage of their education?"
Derived from 13 qualitative interviews · Thomas Jefferson University, 2024
Research & Discovery
Listening before designing.
Research was structured to capture both the emotional texture and practical friction of mentorship within architecture programs. Interviews were semi-structured, allowing students to navigate their own experiences, revealing not just what they struggled with, but what they had silently normalized.
Key themes: visibility gaps in peer expertise, hierarchical perception of asking for help, and the absence of a shared language for "what I need" versus "what I can offer."
Ideation
Ideation: divergent concept explorations across physical and digital touchpoints, narrowed through feasibility and impact framing.
Designing the Flow
Mapping the path through.
With research insights synthesized, we mapped the complete user journey, from a student discovering they need help, to finding the right peer mentor, initiating contact, and completing the support loop. The flow had to account for first-time users, returning users, and the hybrid nature of the platform.
Mentorship didn't need more complexity; it needed visibility in the places students already were.
Onboarding Flow
Onboarding: designed to establish skill profile, set mentorship preferences, and connect with the studio community in under 3 minutes.
Wireframes
Structure before polish.
Wireframes were used as a communication tool as much as a design artifact, presenting flows to faculty stakeholders and testing early structural decisions before investing in high-fidelity work. Each screen was annotated to document rationale, edge cases, and open questions.
Annotated Mockups
Every decision documented.
High-fidelity mockups were annotated to capture the thinking behind each design choice, including interaction states, accessibility considerations, and edge cases, creating a handoff artifact that communicated intent as clearly as execution.
Usability Testing
Testing with the people it's for.
Five rounds of usability testing were conducted across design iterations, each round targeted at a specific hypothesis. Sessions were moderated, task-based, and followed by a brief debrief to capture post-task sentiment and surface moments of hesitation that metrics alone couldn't explain.
Key issues surfaced and resolved across testing rounds:
Let's Talk Business
Designed to sustain itself.
A great product with no viable model is a prototype. MentorMap was designed with sustainability in mind, identifying revenue streams, defining success metrics, and pressure-testing assumptions before committing to a direction.
Premium AI Features
Intelligent mentor matching, skill-gap analysis, and personalized learning path recommendations for power users.
Community Workshops & Events
Curated skill-sharing sessions, critique workshops, and portfolio reviews hosted within the platform ecosystem.
Partnerships with Software
Co-branded skill modules and integrations with Rhino, AutoCAD, Revit, and other architecture-focused tools students actually use.
Reduced time searching for the right help, by surfacing mentor availability and expertise contextually.
Overall skill improvement timeline: students reach proficiency faster with structured peer guidance.
Student adoption & engagement target, driven by visibility, ease of entry, and social proof within the studio.
Business Model Canvas: mapping value propositions, customer segments, revenue streams, and key partnerships.
Validating the Product
Testing the riskiest assumptions.
Before building at scale, three pretotyping experiments were designed to test the riskiest assumptions: not whether the product was usable, but whether the core behaviors it depended on would actually occur in the real world.
One-Night-Stand Test
✓ Passed
The physical Skill Wall consistently sparked real conversations in studio spaces, confirming that visibility and proximity lower hesitation and drive mentorship interactions. Students engaged without prompting.
Fake Front Door Test
✓ Passed
High engagement rates validated genuine interest in public mentorship sessions and skill-based discovery. Students clicked through and expressed intent to use the platform before it existed.
Mechanical Turk (AI Guidance)
✗ Failed
Students were hesitant to rely on AI-generated guidance without context or control. Key learning: AI-supported mentorship is only valuable when it augments human support, offers customization, and preserves user agency.
Key Learnings
Five things this project taught me.
A year of research, design, testing, and iteration, distilled into the moments that shaped how I think.
Visibility, not effort, is the barrier.
Students struggle not because they don't try, but because the support around them is invisible. Design that surfaces existing resources is often more impactful than design that creates new ones.
Hierarchy creates hesitation.
When mentorship feels hierarchical, unclear roles make students reluctant to ask. Framing peers as collaborators rather than authorities, even subtly in copy and UI, meaningfully reduced perceived risk.
Expertise is invisible in physical spaces.
Peer knowledge exists in abundance within studios, but without a shared layer to surface it, students reinvent the wheel daily. Physical-digital hybrids can bridge this without requiring behavior change.
AI must augment, never replace, human connection.
AI-supported guidance is valuable only when it augments human support, offers customization, and preserves user control. When AI felt opaque or uncontrollable, students rejected it, even when technically useful.
Mentorship that works changes how people feel.
The most meaningful outcome wasn't skill transfer; it was reduced anxiety, increased confidence, and a sense of direction. Designing for emotional outcomes, not just functional ones, is what makes the difference.