04 · Design Solution

StudyHub

An AI-powered academic matching platform that makes collaborative learning accessible to every UW student, regardless of social confidence or existing network.

Value proposition: StudyHub exists because collaborative learning has never been equally accessible. Study groups have always formed through proximity, existing friendships, and social confidence, systematically excluding students who are introverted, socially anxious, or simply new to campus. Our platform closes that gap by combining AI-powered personality and academic matching with the trust infrastructure students need to actually show up.
StudyHub high-fidelity prototype
High-fidelity prototype. view in Figma

Core Concept

StudyHub addresses the accessibility gap in collaborative learning through a web-based platform that removes the social and technical barriers preventing students from forming effective study groups. The solution works in three phases:

  1. Build trust: personality and learning style onboarding survey, privacy controls, and verified profiles so students feel safe before committing.
  2. Surface matches: compatible matches by course with plain-language reasoning so students understand why they were paired.
  3. Support follow-through: shared goal setting, in-app scheduling, a collaborative group board, and streak tracking so groups don't just form but actually stick.

Unlike existing tools that rely on proximity, existing friendships, or unmoderated group chats, StudyHub focuses on trust-first matching, personality-aware pairing, introvert-centered design, transparent AI explanations, social proof adoption, and structured first-session facilitation.

How Users Interact

Students create an account with UW NetID credentials, complete a personality test and schedule setup, review AI-suggested matches with plain-language reasoning, accept or decline matches, schedule a first session, set shared group goals, and collaborate on a group board, all before their first in-person meeting.

StudyHub user flow diagram
User flow diagram

Personas & Stakeholders

Maya Chen · Direct user

Freshman · Computer Science · Age 18

Maya moved to Seattle from out of state and doesn't know anyone in her classes. She's introverted and finds it hard to approach strangers in large lecture halls. She defaults to studying alone even when she'd prefer a group.

Needs: Low-stakes entry, visible profiles before committing, transparent match reasoning, reputation signals.

Dr. David Pak · Indirect stakeholder

Lecturer · UW College of Engineering

David teaches a 200-person intro engineering course. He notices students struggle to collaborate, reflected in project quality and drop rates. He doesn't have bandwidth to facilitate study group formation himself.

Relationship: Benefits when students perform better and could be an institutional advocate if outcomes are demonstrable.

StudyHub stakeholder map
Stakeholder map

Key Features

Must have

Onboarding survey

Guided flow collecting class enrollment, learning type, personality (Big Five), availability, and club involvement, inspired by Co-Learn's detailed preference pattern.

Onboarding screen low-fidelity prototype

Personality + course matching

AI matching system using Big Five personality data alongside course enrollment for class-wise and general study groups, with plain-language reasoning for every recommendation.

Match preview low-fidelity prototype

Privacy controls & accessibility

Students control what profile information is visible. Accessibility accommodations are built into the core experience, not treated as an afterthought.

Privacy and settings low-fidelity prototype

Should have

  • Class roster integration
  • In-app scheduling linked to calendar apps
  • Group goal setting before first meetings
  • Verified badges for trust signals

Nice to have

  • Group streaks based on consistency
  • Infinite group board (FigJam-style) for shared notes and brainstorming
Feature ideation and prioritization map
Feature ideation and prioritization

Interface mockups

Home screen
Onboarding
Profile screen
Matching
Matches screen
Groups
High-fidelity prototype
Hi-fi

Design Process

From low-fidelity sketches to high-fidelity prototypes.

Low-fidelity prototype screen 1

Low-fi prototype 1

Low-fidelity prototype screen 2

Low-fi prototype 2

Low-fidelity prototype screen 3

Low-fi prototype 3

Annotated mockups

Key screens call out design decisions tied to research: prominent personality test entry (post-usability testing), match reasoning cards, privacy controls, and scheduler guidance prompts.

High-fidelity StudyHub interface
High-fidelity interface mockup

Storyboard

Six-panel storyboard of a student using StudyHub
Six-panel storyboard from onboarding to first group session

How It Addresses the Problem

StudyHub directly targets the students our research identified as most underserved: first-years without networks, introverted and socially anxious students, and commuters who can't rely on proximity. By matching on schedule, personality, and learning style before any awkward outreach, providing transparent reasoning to combat algorithm skepticism, and structuring the first session to reduce social risk, the platform addresses the behavioral gap that tools like Discord and GroupMe leave open. Every design decision is made with the most vulnerable student in mind, because students aren't failing to find study groups because they lack tools; they're failing because the social risk feels too high.

← Back to Problem Design Rationale Try Interactive Prototype → See Evaluation