VibeFlow AI Hackathon
Research Report
Solving EdTech's Personalization Gap: Connecting students with compatible mentors through AI-powered matching
Where Human Intuition Meets AI Execution
Executive Summary
The Opportunity
The hackathon addressed two critical pain points: helping students find compatible, qualified mentors for niche subjects, and providing an efficient platform for expert mentors to share knowledge without administrative overhead.
The EduVibe Solution
An AI-powered EdTech platform with personalized student onboarding, AI-driven mentor recommendation with explainable AI (e.g., "90% match due to ML expertise"), interactive booking, and dual dashboards.
Core Finding
AI is indispensable for rapid prototyping and generating standard components. However, its use requires constant human validation, particularly for state management and complex decision logic.
Key Challenges Identified
State Loss
Data lost during form submissions, page refreshes, and navigation between steps
Logical Errors
Carousel active slide tracking failed; AI-generated logic insufficient for dynamic components
Over-Engineering
Unnecessary form steps and bloated state logic requiring manual cleanup
Outdated Library Suggestions
AI suggested deprecated libraries (moment.js) requiring replacement (day.js)
Complex Visual Interpretation
Animated graphics became simple placeholders; struggles with creative UI designs
Agent Mode Hardcoding
Custom logic via Agent Mode resulted in hardcoded values and inflexible implementations
Slower Response for Large Context
Claude 3 Opus showed slower response times when processing larger context windows
Domain-Availability Mismatches
GPT-4o occasionally mismatched domain expertise with mentor availability in recommendations
AI Models & Primary Tasks
Mentor Matching (Task 3)
High accuracy in generating explainable AI output
Homepage & Dashboard (Tasks 1, 5)
Natural UI structuring with verbose explanations
UI Generation & Preview
Rapid scaffolding with limited customization
Full Tool Stack Explored
Recommendations for Future Teams
- Refined Prompting: Use short, clean prompts to avoid bloated UI code, especially for complex state logic.
- Manual Oversight: Take control over core, non-standard logic and validate all suggested dependencies.
- Documentation: Document AI decisions, prompts that worked, and where AI broke down for future maintainability.
Background & Context
The VibeFlow Hackathon challenged developers to solve two critical pain points in modern education
Target Personas
Maya Chen
A student struggling with specific concepts like machine learning, seeking a truly compatible and qualified mentor who understands her learning style.
Pain Point:
Finding compatible, qualified mentors for niche subjects
Dr. Aisha Patel
A senior ML engineer seeking an efficient way to mentor—needs comprehensive session management and student analytics without administrative overhead.
Pain Point:
Lack of efficient, dynamic platform for expert mentors
The EduVibe Platform Solution
EduVibe is an AI-powered EdTech system designed to connect students and mentors through personalized learning paths and robust session management.
Personalized Student Onboarding
Multi-part form capturing academic background, skill levels, learning style, and goals
AI-Driven Mentor Recommendation
Algorithm combining skill, availability, and goals with explainable AI (e.g., '90% match due to ML expertise')
Interactive Booking System
Context-aware time slot booking with real-time availability management
Dual Dashboard System
Student dashboard for sessions and progress; Mentor dashboard for analytics and availability
The Opportunity
Solving EdTech's personalization gap with AI—addressing the difficulty for students to find compatible, qualified mentors for niche subjects, and providing an efficient platform for expert mentors to share their knowledge.
Research Focus Areas
- Where does AI truly accelerate development vs. create hidden friction?
- How does AI impact code quality, security, and long-term maintainability?
- What is the effectiveness of AI-driven state management in complex forms?
- How do different AI tools perform across UI, backend, and research tasks?
Hackathon Timeline
Hackathon Launch
Teams begin building the EduVibe platform with AI tools
Development Phase
Participants use Claude, GPT-4o, V0 for UI, backend, and research
Submission Deadline
Teams submit their completed EduVibe implementations
Mentor Evaluation
Expert mentors review projects (50% Report, 30% Dev, 20% Presentation)
Report Analysis
Comprehensive analysis of AI tool effectiveness and workflows
Methodology
Our systematic approach to analyzing competitor tool usage and effectiveness
Evaluation Process
Gather Reports
Collect competitor submissions and tool usage data
Analyze Tools
Evaluate performance across different categories
Extract Insights
Identify patterns and key findings
Compare Results
Benchmark against VibeFlow capabilities
Market Research
- Analyzed competitor submissions from the VibeFlow Competition
- Evaluated tools based on usability, customization, performance, and cost
- Collected quantitative metrics on completion rates and tool adoption
Competitor & Judge Feedback
- Gathered insights from detailed case studies
- Analyzed tool usage patterns, challenges, and outcomes
- Judge panel emphasized customization and performance optimization
Data Collection
- Extracted insights from AI tool reports (Claude, GPT, V0, etc.)
- Analyzed architecture diagrams for system integration
- Reviewed "WOW" and "FACEPALM" moments
Findings & Observations
Key insights into AI tool usage patterns and effectiveness across different development tasks
AI Accelerates Frontend/UI Development
AI tools significantly speed up UI scaffolding and basic component creation. Teams rapidly scaffolded entire multi-step onboarding flows with AI assistance.
Struggles with Animations & State
Complex animations and state management require significant human intervention. AI-generated carousel logic failed to track active slides correctly.
Asking Mode Outperforms Agent Mode for Logic
Interactive, step-by-step prompting (Asking Mode) was more effective for custom logic like Mentor Matching, while Agent Mode excelled at initial UI generation.
Claude & GPT-4o Excel in Different Domains
Claude 3 Opus valued for natural UI structuring; GPT-4o selected for high accuracy in generating explainable mentor matching output.
State Management Challenges
A primary focus was on front-end state management—ensuring real-time, consistent state updates and smooth transitions
State Loss
Occurred during form submissions, page refreshes, and state mismatches during navigation
Logical Errors
AI-generated carousel logic failed to track active slide correctly, requiring manual intervention
Over-Engineering
AI sometimes generated unnecessary form steps or bloated state logic that had to be refined
Tool Categories & Usage
Backend & Logic
UI/Scaffolding
Research/Planning
IDE & Assistants
Tools vs. Tasks Performance Matrix
Comparative analysis of AI tools across different development tasks
| Tool | UI Scaffolding | State Management | Backend Logic | Mock Data | Research/Planning |
|---|---|---|---|---|---|
| Claude (Sonnet/Opus) | Medium | Low | High | High | High |
| GPT-4o/4.1 | Medium | Medium | High | High | High |
| V0 by Vercel | High | Low | N/A | Medium | Low |
| Rocket.new | High | Low | N/A | Medium | Low |
| Builder.io | High | Medium | Low | Medium | Low |
| Lovable | High | High | Medium | High | Medium |
| Perplexity AI | Low | N/A | Low | N/A | High |
| GitHub Copilot | Medium | Medium | Medium | Medium | Medium |
WOW Moments
Where AI truly accelerated development
- Rapid scaffolding of entire multi-step onboarding process
- AI-generated explainable mentor recommendations (e.g., '90% match due to ML expertise')
- Multi-part form field generation with basic validations
- Simple CSS hover effects and transitions
- Complex mock data generation for testing
FACEPALM Moments
Where AI created hidden friction
- State loss during form submissions and page refreshes
- Carousel active slide tracking failures
- Over-engineered state logic requiring manual cleanup
- Complex animated graphics replaced with simple placeholders
- Outdated library suggestions (moment.js instead of day.js)
Key Insights
- AI excels at structure and basic components but struggles with complex visual interpretation (animated graphics become simple placeholders)
- Asking Mode (step-by-step prompting) is more effective for custom logic; Agent Mode is better for initial UI generation speed
- Claude 3 Opus valued for natural UI structuring; GPT-4o selected for explainable AI output in mentor matching
- V0 provides rapid UI scaffolding but with limited customization options
- Dependency management is critical—AI may suggest outdated libraries (e.g., moment.js) that need manual replacement (e.g., day.js)
Analysis
Deep dive into tool performance patterns and competitive landscape insights
Competitor Tool Usage Analysis
Purpose & Use Case
Tools were primarily used for UI scaffolding, backend logic, and research tasks
Pros (Advantages)
Rapid prototyping, time savings, and automated code generation capabilities
Cons (Limitations)
Limited customization, state management issues, and manual intervention needs
Cost & Licensing
Varied pricing models from free tiers to enterprise subscriptions
Ease of Use
Generally accessible but with steep learning curves for advanced features
Impact & Outcomes
Mixed results with significant time savings offset by debugging overhead
Performance Analysis: Success vs. Failure Patterns
What Worked vs. What Failed
| Area | What Worked | What Failed | Reason |
|---|---|---|---|
| Mock Data | GPT-4o and Claude made accurate test data with correct formatting | - | Clear data structure requirements |
| Validation Schemas | AI tools generated clean Zod/Yup schemas from clear input | - | Well-defined validation rules |
| Multi-step Forms | Logical form flows when given full form structure | State broke across steps | AI didn't track parent-child flow properly |
| Booking Logic | - | Couldn't stop double bookings | Didn't understand database-level locks |
| Animations | - | Buggy or clashed animations | AI missed timing or misused animation libraries |
| Backend Integration | - | Frontend and backend didn't match | Field names and APIs didn't align correctly |
What Worked Well
- Mock data generation with accurate formatting
- Validation schema creation from clear requirements
- Multi-step form flows with proper structure
- YAML to code translation for backend scaffolds
- Planning from PRDs and requirement documents
- Component reuse when provided with examples
What Failed
- State management across complex form flows
- Real-time booking logic and conflict resolution
- Animation timing and library integration
- Form field consistency and validation
- Modular code architecture and reusability
- Frontend-backend API alignment
Impact & Outcomes
Quantitative and qualitative assessment of AI tool effectiveness in the hackathon
AI Performance by Task Type (%)
Success vs failure rates across different development tasks
Key Insight: AI excels at UI scaffolding and mock data generation (85-90% success) but struggles significantly with state management and animations (25-35% success).
Development Outcome Distribution
Breakdown of AI-assisted development results
65%
Positive Outcomes
35%
Required Human Intervention
Positive Impacts
Time Savings
AI tools like V0 and Rocket.new reduced UI development time by 2-3 days, allowing teams to focus on core functionality and business logic.
Robust Algorithms
Fine-tuned mentor matching algorithms using Claude and OpenAI APIs significantly improved platform functionality and user experience.
Efficient Research
Perplexity streamlined requirements analysis and task planning, reducing research overhead by approximately 40%.
Negative Outcomes
Manual Interventions
Complex animations, state management, and backend logic required significant manual refactoring, often taking longer than building from scratch.
Bugs and Errors
Hydration errors in Next.js and incorrect project structures (e.g., Vite instead of Next.js) significantly increased debugging time.
Limited Customization
UI tools consistently failed to deliver brand-specific, responsive designs that matched project requirements and design systems.
Tools Used by Competitors
Comprehensive analysis of AI tools utilized during the hackathon, based on whitepaper findings
Tool Analysis Report
Detailed analysis of individual AI tools with strengths, weaknesses, and optimal use cases
AI Workflow: Agent vs Asking Mode
Agent Mode
Fast, autonomous generation of UI components and layouts
Asking Mode
Interactive, step-by-step prompting for complex features like Mentor Matching Algorithm
Claude 3 Opus
Primary Task
Homepage (Task 1) & Mentor Dashboard (Task 5)
Use Case
Backend logic, multi-step flows, onboarding, validation, API/schema design
Strengths
- •Natural UI structuring
- •Verbose explanations
- •Clean TypeScript code
- •Step-by-step logic
Weaknesses
- •Slower response time for larger context
- •May overcomplicate simple logic
- •Needs strong prompts
Best For
Multi-step forms, backend validation flows, readable modular backend logic
Key Takeaway
Valued for natural UI structuring but slower for large context windows
GPT-4o
Primary Task
Mentor Matching Algorithm (Task 3)
Use Case
Full-stack scaffolding, mock data, validation schemas, explainable AI output
Strengths
- •High accuracy in explainable output
- •Fast processing
- •Handles long inputs
- •Clean schema generation
Weaknesses
- •Occasionally mismatched domain expertise with availability
- •Can break multi-step logic
- •False confidence in outputs
Best For
Mentor matching with explainability (e.g., '90% match due to ML expertise')
Key Takeaway
Selected for high accuracy in generating explainable AI recommendations
V0 by Vercel
Primary Task
UI Generation & Dashboard Preview
Use Case
Text or Figma to responsive apps using React, Next.js, Tailwind, shadcn/UI
Strengths
- •Rapid UI scaffolding
- •Fast initial generation
- •Design-to-code
- •One-click Vercel deploy
Weaknesses
- •Limited customization options
- •Needs manual tweaks for complex logic
- •Struggles with complex animations
Best For
Rapid UI prototypes, dashboard previews, app layout drafts with real framework code
Key Takeaway
Used for rapid UI scaffolding but limited customization
Cursor IDE
Primary Task
AI-Assisted Development
Use Case
AI-powered code editor for writing, refactoring, managing code with multiple model support
Strengths
- •Deep project understanding
- •Agent Mode for initial UI
- •Asking Mode for custom logic
- •Supports GPT, Claude, Gemini
Weaknesses
- •Needs good prompts for accurate output
- •Can misinterpret vague prompts
- •Learning curve for .cursorrules
Best For
Full-stack development, debugging, AI-assisted code generation with model flexibility
Key Takeaway
Asking Mode preferred for custom logic; Agent Mode for rapid UI generation
Perplexity AI
Primary Task
Requirements Analysis & Task Planning
Use Case
Planning tool, breaks down requirements, task sequencing, not a code generator
Strengths
- •Strong research and summarization
- •Great for structuring tasks
- •Endpoint planning
- •Real-time web search
Weaknesses
- •Cannot write backend logic
- •Outputs are high-level only
- •No code generation capability
Best For
Planning backend architecture, mapping tasks, researching system designs before coding
Key Takeaway
Excellent for pre-development planning and requirement analysis
Lovable / Builder.io
Primary Task
Full-Stack App Generation
Use Case
Generates full apps from text or Figma with backend integrations
Strengths
- •Clean code output
- •Rich integrations (Supabase, Stripe)
- •Mobile + web support
- •Full-stack generation
Weaknesses
- •Complex UI logic needs editing
- •May produce verbose code
- •Limited design control
Best For
MVPs, internal tools, dashboards when time is short
Key Takeaway
Good for rapid full-stack prototypes but requires refinement
SWOT Analysis
Strategic analysis of strengths, weaknesses, opportunities, and threats for each AI tool
SWOT Analysis Matrix
| Tool | Strengths | Weaknesses | Opportunities | Threats |
|---|---|---|---|---|
| Claude (Sonnet / Opus) | Strong logic, clean code, great for validation and audits | Slower, may over-engineer, needs clear prompts | Use in audits, backend flows, onboarding | High cost, slower vs GPT-4o |
| GPT-4o / GPT-4.1 | Fast, large context, great for mock data and APIs | Hallucinates logic, broken states, false confidence | Scaffolding, validation chains, dashboards | Misleading output, loss of trust |
| V0 | Text-to-code UI, Figma integration | Manual fixes for animations, complex UI | Speed up MVP UIs, collaborative workflows | AI outputs need heavy edits |
| Rocket.new | Natural language to full-stack app, high-quality code | Verbose code, needs customization | Non-devs ship MVPs faster, time-saving | Hard to debug complex flows |
| Perplexity AI | Excellent planning, summarization, task breakdown | No code generation, high-level only, no memory | Planning before AI code gen | Replaced by tools that also write code |
| Cursor IDE | Inline AI coding, .cursorrules, multi-model, Git integration | Misinterprets vague prompts, config learning curve | Raise code quality with inline rules | Errors if used blindly, refactor time adds up |
Recommendations & Improvements
Strategic guidelines for optimizing AI tool usage and development workflows
Core Recommendation
AI is indispensable for rapid prototyping and generating standard components. However, its use requires constant human validation, particularly for state management and complex decision logic.
Prompting Best Practices
Tool Selection Guidelines
Mentor Matching Logic
High accuracy in generating explainable output (e.g., '90% match due to ML expertise')
Alternatives:
UI Structure & Dashboards
Valued for verbose explanations and clean component organization
Alternatives:
Rapid UI Prototyping
Extremely fast initial generation, though limited customization
Alternatives:
Custom Logic Implementation
Interactive prompting prevents hardcoding issues in complex features
Alternatives:
Critical Human Review Areas
Developers must take manual control over these non-standard logic areas
State Management
Validate state persistence across form submissions, page refreshes, and navigation
Animation Logic
Review AI-generated animations—carousel tracking and transitions often fail
Dependency Validation
Check all AI-suggested libraries for deprecation (e.g., moment.js → day.js)
Complex Visual Designs
AI struggles with non-standard geometric patterns; manual implementation required
Documentation Requirements
Detailed documentation is crucial for engineering quality and future maintainability
VibeFlow Platform Advantages
The proposed solution addresses key pain points discovered during the hackathon