Welcome to SisterShield

Key Terms

• TF-VAWG — Harmful behaviors carried out through technology, like cyberbullying, online stalking, or sharing private images without consent
• Next.js — The framework used to build this app; handles both the website you see and the server behind it
• Twine — A free tool for building choose-your-own-adventure stories with clickable choices
• i18n — Short for “internationalization”; making the app work in multiple languages (English + Korean)
• LLM — An AI that reads and writes text (like Claude or ChatGPT), used here to generate stories and translate content

SisterShield is an interactive educational platform designed to help women and girls recognize, respond to, and recover from Technology-Facilitated Violence Against Women and Girls (TF-VAWG). Built as a solo project for the Technovation Girls Senior Division, it combines trauma-informed design, AI-powered course generation, and choose-your-own-adventure storytelling to create a safe, empowering learning experience.

Development Methodology & Project Governance

This documentation is organized to demonstrate rigorous development methodology and transparent project governance. Every design decision, technical choice, and user experience refinement is mapped directly to measurable outcomes so stakeholders can quickly find the evidence they need.

Quick Navigation

For a comprehensive mapping of every project goal to specific evidence and documentation pages, start with the Impact Accountability Framework.

About the Project

Detail	Value
Platform	Web application (responsive, mobile-first)
Stack	Next.js 14, TypeScript, Prisma, PostgreSQL, NextAuth
Languages	English (en-US) and Korean (ko-KR)
AI Integration	LLM course generation (Claude/GPT-4o), DALL-E 3 imagery, auto-translation
Safety Features	Quick Exit (Escape key), Get Help dialog, crisis resources
Team Size	1 (solo project)
Division	Technovation Girls — Senior Division

Core Capabilities

1. Interactive Visual Novel Courses — Twine-based choose-your-own-adventure stories that teach TF-VAWG recognition and response through safe, simulated decision-making.
2. AI-Powered Course Generation — Teachers use LLMs to generate culturally relevant scenarios, with DALL-E 3 producing safe, appropriate cover imagery.
3. Trauma-Informed Design — A calm color system, Quick Exit button, and supportive language throughout — designed to avoid re-traumatization.
4. Full i18n Support — Every string, every label, every piece of content available in both English and Korean.
5. Role-Based Experience — Students self-serve courses, teachers create and review content, admins manage the platform.

Quick Navigation

Use the table below to jump directly to any Introduction sub-page.

Page	What It Covers	Primary Rubric Area
Motivation & Community Need	The problem, evidence, community personas	Ideation
Global Framework & SDG Alignment	SDG mapping, Global Safety Footer, UN framework	Potential Impact, Avoid Harm
User Feedback Loops	3+ iterative testing cycles with documented changes	User Experience
Design for Target Audience	Trauma-informed design, accessibility, Avoid Harm checklist	User Experience, Avoid Harm
The Learning Journey & Resilience Narrative	Challenges, learning narrative, Before/After evidence	Growth & Perseverance
Impact Accountability Framework	Full rubric mapping with evidence and scoring strategy	All Categories
Media & Outreach Toolkit	Pitch video, demo video, technical video storyboards	Pitch, Technical
Interactive Narrative Architecture	Why interactive narrative, course structure, choice architecture	Technical, Potential Impact
Design Decisions for Rubric Mastery	All major design decisions mapped to rubric evidence	All Categories
Marketing & Feedback Integration	Marketing experiments, acquisition funnel, pilot feedback	Entrepreneurship
Financial Sustainability	Revenue model, cost structure, growth projections	Entrepreneurship

How to Read This Documentation

• Start broad: This Introduction section gives context, motivation, and strategy.
• Go deep: The Technical Docs section covers architecture, AI integration, the design system, and feature implementation.
• Check the evidence: Every claim links to a specific page or includes a TODO placeholder where evidence will be added.
• Follow the framework: The Impact Accountability Framework is the master index for evaluation.

Evidence Placeholders

Throughout this documentation, you will see TODO markers where empirical data, user quotes, or external references need to be filled in. These placeholders ensure intellectual honesty — no metrics or claims are invented.

A Note on Safety

SisterShield deals with sensitive topics. The platform and this documentation follow these principles:

• No medical or legal advice is provided. The platform connects users to professional resources.
• Language is supportive and empowering, never diagnostic or victim-blaming.
• Crisis resources are always accessible, both in the app and referenced in this documentation.
• Quick Exit is available throughout the application for users who need to leave immediately.

If you or someone you know is experiencing technology-facilitated violence, please reach out to a trusted resource. In the US, text HOME to 741741 (Crisis Text Line). In Korea, call 1366 (Women’s Emergency Hotline).