A personalized, dependency-aware learning roadmap generator that turns a career goal, existing skills, proficiency, and available study time into a structured learning plan.
🌐 Live Demo: https://learning-path-generator-snowy.vercel.app/
Learning Path Generator helps learners answer a simple question:
"Given where I am now and where I want to go, what should I learn next?"
Instead of giving every learner the same generic roadmap, the application generates a personalized learning path based on:
- Target role
- Existing skills
- Skill proficiency
- Weekly study availability
- Optional target timeline
The roadmap also respects skill dependencies, ensuring foundational concepts appear before the topics that depend on them.
Many learners struggle with:
- Not knowing what to learn next
- Relearning concepts they already understand
- Jumping randomly between tutorials
- Learning advanced topics before their prerequisites
- Having no practical projects to reinforce learning
- Creating unrealistic study schedules
Learning Path Generator addresses these problems by turning a target role into a structured, personalized progression.
Generate a learning path based on the learner's:
- Target role
- Existing skills
- Proficiency level
- Weekly availability
- Optional target timeline
The application accounts for skills the learner already knows and focuses the roadmap on actual learning gaps.
Learning topics are ordered according to their prerequisites.
The roadmap engine uses Kahn's topological sorting algorithm to ensure dependent skills appear after the skills they require.
Skills are grouped into logical phases to create a progression rather than a flat list of topics.
Weekly workloads are adjusted according to the learner's available study time.
Projects are associated with relevant skills so learners can apply what they have just learned.
Roadmap and learning progress persist locally, including across page refreshes.
The application is designed to remain usable across desktop and mobile screen sizes.
The application follows this general pipeline:
Target Role
↓
Required Skills
↓
Existing Skills + Proficiency
↓
Gap Analysis
↓
Dependency Graph
↓
Topological Ordering
↓
Learning Phases
↓
Weekly Scheduling
↓
Projects + Resources
↓
Personalized Roadmap
The engine combines role-specific skill mappings with dependency relationships and user-specific inputs to produce a tailored roadmap.
The application keeps roadmap generation logic separate from the presentation layer.
React UI
│
│
User Configuration
│
┌────────────┼────────────┐
│ │ │
Target Role Existing Weekly
Skills Hours
│ │ │
└────────────┼────────────┘
↓
Roadmap Engine
│
┌────────────┼───────────────┐
│ │ │
↓ ↓ ↓
Role/Skill Gap Analysis Dependencies
Mapping │
↓
Topological Sort
│
┌──────────────────┼──────────────────┐
│ │ │
↓ ↓ ↓
Phases Schedule Projects
│ │ │
└──────────────────┼──────────────────┘
↓
Personalized Roadmap
│
↓
Local Persistence
There is no backend or external AI API dependency in the current version. Roadmap generation happens locally in the application.
- React
- Vite
- JavaScript
- Tailwind CSS
- Local Storage
- Kahn's Algorithm / Topological Sorting
The project intentionally avoids unnecessary backend infrastructure for this use case.
Choose Target Role
↓
Select Existing Skills
↓
Set Proficiency
↓
Set Weekly Availability
↓
Generate Roadmap
↓
Review Learning Gaps
↓
Follow Learning Phases
↓
Complete Recommended Projects
↓
Track Progress
A learner who already knows a technology should not be forced to learn it again unnecessarily.
Advanced topics should appear only after the concepts they depend on.
Learning milestones should lead into practical implementation.
Available study time influences scheduling, but the application does not pretend that every learner can reach a role within an arbitrary deadline.
The application was validated through:
- Engine tests
- ESLint
- Production build
- Roadmap generation scenarios
- Skill gap behavior
- Dependency ordering
- Role-based roadmap differences
- Weekly-hour scheduling differences
- Persistence across refreshes
- Responsive/mobile behavior
git clone https://github.com/rigel08/learning-path-generator.git
cd learning-path-generatornpm installnpm run devnpm run buildhttps://learning-path-generator-snowy.vercel.app/
Completed
The current version delivers a complete client-side personalized roadmap experience with dependency-aware ordering, adaptive scheduling, project recommendations, and persistent progress tracking.
Learning technical skills is often less about finding more resources and more about knowing:
- What to learn
- In what order
- Why it matters
- What to build afterward
Learning Path Generator turns that problem into a structured system:
Choose a destination → understand the gaps → respect dependencies → learn progressively → build projects → track progress.
Srihith Rao
Computer Science student focused on:
Backend Engineering · AI/LLM Engineering · Full-Stack Development
GitHub: https://github.com/rigel08