🎓 EduPath AI

Privacy-First Academic Companion & Longitudinal Career Navigator

EduPath AI transforms raw academic transcripts and university syllabi into grounded four-year academic roadmaps, personalized career guidance, measurable readiness insights, and verifiable progress reports.

🚀 Live Demo

📌 Overview

Students often have access to grades, course outlines, and career resources, but these pieces of information are rarely connected into one continuous academic and career journey.

EduPath AI addresses this gap by combining:

📄 Academic transcript analysis
📚 University syllabus and catalog grounding
🧠 Retrieval-Augmented Generation (RAG)
📊 Academic and career readiness scoring
🛠️ Personalized skill remediation
🗺️ Adaptive four-year roadmaps
🤖 Grade-aware AI advising
🎯 Verified career opportunities
📈 Longitudinal progress tracking
🏆 Achievement and milestone tracking
📑 Downloadable PDF progress audits and portfolios

Instead of providing generic AI advice, EduPath AI maintains a student's academic context and continuously updates recommendations as the student progresses.

🎯 Problem Statement

University students commonly face several connected problems:

1. Syllabus Disconnect

Students know which courses they are taking, but often do not know how individual courses map to:

Required technical skills
Projects
Career roles
Industry expectations
Future learning goals

2. Generic AI Advice

Traditional AI assistants can provide useful suggestions, but without grounding them in the student's actual academic record, advice can become generic or inaccurate.

3. No Longitudinal Tracking

Most academic tools focus on the current semester.

Students need a system that can answer:

"How am I progressing compared with where I started?"

4. Scattered Achievements

Projects, certifications, internships, competitions, leadership activities, and other achievements are often stored separately and are difficult to connect to career readiness.

5. Privacy Concerns

Academic transcripts and student profiles contain sensitive personal information. A student-focused system should minimize unnecessary exposure and isolate individual profiles.

💡 Solution

EduPath AI creates a continuous student-development cycle:

Academic Data
     ↓
Transcript & Syllabus Analysis
     ↓
Grounded Knowledge Retrieval
     ↓
Readiness Assessment
     ↓
Skill Gap Detection
     ↓
Personalized Recommendations
     ↓
Four-Year Roadmap
     ↓
Progress & Achievement Tracking
     ↓
Updated Readiness Assessment
     ↓
Progress Report / Portfolio

The key idea is simple:

Analyze → Recommend → Act → Track → Reassess → Improve

✨ Key Features

📄 1. Academic Transcript Analysis

EduPath AI extracts academic information from student transcripts and converts it into structured data.

The system can analyze:

Courses
Grades
Credit hours
Academic performance
Completed coursework
Areas requiring improvement

This creates the academic foundation for the rest of the system.

📚 2. University Syllabus Grounding

University course information can be stored and indexed using ChromaDB.

This enables the AI advisor to retrieve relevant academic context instead of relying only on general model knowledge.

RAG Pipeline

University Catalog / Syllabus
            ↓
      Document Processing
            ↓
        Chunking
            ↓
       ChromaDB Index
            ↓
    Semantic Retrieval
            ↓
       Gemini AI
            ↓
 Grounded Recommendation

📊 3. 100-Point Career Readiness Score

EduPath AI evaluates student readiness using four major pillars:

Pillar	Weight
🎓 Academic	30%
🧠 Skills	25%
🛠️ Projects	25%
💼 Experience	20%
Total	100%

Readiness Formula

Readiness Score =
    Academic × 0.30
  + Skills × 0.25
  + Projects × 0.25
  + Experience × 0.20

The score is designed to provide a high-level snapshot of career preparation rather than functioning as a formal academic evaluation.

🛠️ 4. Skill Gap & Remediation Engine

After analyzing academic and career readiness data, EduPath AI identifies areas where the student may need additional development.

Examples include:

Programming
Data Structures & Algorithms
Machine Learning
Deep Learning
Databases
Cloud
Communication
Project experience
Industry exposure

The system can then recommend targeted learning or practical activities.

Example

Current State
     ↓
Missing Skill: Machine Learning Deployment
     ↓
Recommended Learning
     ↓
Build a Deployment Project
     ↓
Add Achievement
     ↓
Recalculate Readiness

🗺️ 5. Adaptive Four-Year Roadmap

EduPath AI generates a personalized roadmap based on the student's:

Current semester
Academic performance
Existing skills
Skill gaps
Projects
Career interests
Experience
Achievements

The roadmap is designed to evolve rather than remain a static four-year plan.

Year 1
├── Academic Foundation
├── Programming
└── Basic Projects

Year 2
├── Core AI / CS Skills
├── Intermediate Projects
└── Technical Certifications

Year 3
├── Specialization
├── Advanced Projects
├── Research / Internship
└── Portfolio Development

Year 4
├── Capstone Project
├── Industry Preparation
├── Resume / Portfolio
└── Job / Graduate Study Preparation

🤖 6. Grade-Aware AI Advisor

The AI advisor uses the student's academic context to make recommendations.

Instead of asking:

"What should an AI student learn?"

a student can receive guidance based on their actual academic progress.

Examples:

Which skill should I learn next?
Which project fits my current level?
What should I improve before applying for internships?
Which courses are related to my target career?
What should I focus on this semester?

🎯 7. Verified Opportunities

EduPath AI can surface relevant opportunities such as:

Internships
Projects
Certifications
Competitions
Career opportunities
Learning opportunities

The objective is to connect readiness gaps with practical opportunities.

📈 8. Longitudinal Progress Tracking

A major part of EduPath AI is tracking how a student's profile changes over time.

The system supports a continuous cycle:

Initial Analysis
      ↓
Baseline Readiness
      ↓
Student Takes Action
      ↓
Student Updates Progress
      ↓
System Recalculates
      ↓
New Readiness Score
      ↓
Progress Comparison

Students can manually update their progress by adding:

New skills
Completed projects
Certifications
Internships
Competitions
Experience
Other academic/career milestones

The system can then compare the updated state against earlier progress.

Example

Initial Readiness      → 58/100
       ↓
Completed ML Project
       ↓
Earned Certification
       ↓
Added Internship
       ↓
Updated Readiness      → 74/100

This makes the platform longitudinal rather than just a one-time academic analyzer.

🏆 9. Achievements & Milestone Tracking

Students can maintain a structured record of meaningful achievements.

Examples:

🥇 Competition wins
📜 Certifications
💻 Projects
🧪 Research
💼 Internships
🎤 Presentations
🏅 Awards
👥 Leadership activities
📚 Completed learning milestones

Achievements can become part of the student's overall career-readiness picture.

Achievement Flow

Student Adds Achievement
          ↓
Achievement Stored
          ↓
Profile Updated
          ↓
Relevant Readiness Area Updated
          ↓
Progress Report Updated

📑 10. Downloadable Progress Audit & Portfolio

EduPath AI can generate a PDF containing a structured snapshot of the student's development.

A progress report can include:

Student profile
Academic summary
Readiness score
Readiness breakdown
Skills
Skill gaps
Projects
Achievements
Experience
Roadmap
Progress observations
Career recommendations

This can function as a personal academic/career audit and a structured portfolio artifact.

🧠 System Architecture

┌───────────────────────────────────────────┐
│              Student Input                │
│ Transcript + Profile + Syllabus + Updates │
└─────────────────────┬─────────────────────┘
                      ↓
┌───────────────────────────────────────────┐
│          Document & Data Processing        │
│ PDF Extraction + Pandas + Validation       │
└─────────────────────┬─────────────────────┘
                      ↓
┌───────────────────────────────────────────┐
│              Knowledge Layer               │
│         ChromaDB + Semantic RAG             │
└─────────────────────┬─────────────────────┘
                      ↓
┌───────────────────────────────────────────┐
│              Analysis Layer                │
│ Academic + Skills + Projects + Experience │
└─────────────────────┬─────────────────────┘
                      ↓
┌───────────────────────────────────────────┐
│            Readiness Engine                │
│              100-Point Score               │
└─────────────────────┬─────────────────────┘
                      ↓
┌───────────────────────────────────────────┐
│            Recommendation Layer            │
│ Gaps + Remediation + Roadmap + AI Advisor │
└─────────────────────┬─────────────────────┘
                      ↓
┌───────────────────────────────────────────┐
│         Longitudinal Tracking Layer        │
│ Progress + Achievements + Milestones       │
└─────────────────────┬─────────────────────┘
                      ↓
┌───────────────────────────────────────────┐
│              Output Layer                 │
│ Dashboard + Opportunities + PDF Reports   │
└───────────────────────────────────────────┘

🔄 Continuous Student Progress Cycle

EduPath AI is designed around repeated evaluation rather than a one-time analysis.

┌──────────────────────┐
│ 1. Student Onboards  │
└──────────┬───────────┘
           ↓
┌──────────────────────┐
│ 2. Initial Analysis  │
└──────────┬───────────┘
           ↓
┌──────────────────────┐
│ 3. Readiness Score   │
└──────────┬───────────┘
           ↓
┌──────────────────────┐
│ 4. Recommendations   │
└──────────┬───────────┘
           ↓
┌──────────────────────┐
│ 5. Student Progress  │
│    & Achievements    │
└──────────┬───────────┘
           ↓
┌──────────────────────┐
│ 6. Reassessment      │
└──────────┬───────────┘
           ↓
┌──────────────────────┐
│ 7. Progress Report   │
└──────────┬───────────┘
           │
           └──────────────→ Repeat

📊 Progress Report Generation

The reporting system converts the student's current state into a downloadable document.

Student Profile
      ↓
Current Academic Data
      ↓
Skills + Projects + Experience
      ↓
Achievements
      ↓
Readiness Calculation
      ↓
Historical Progress
      ↓
AI-Generated Insights
      ↓
PDF Progress Audit

The report provides a point-in-time snapshot while preserving the idea of continuous progress.

🔐 Privacy & Security

EduPath AI is designed with student privacy in mind.

Current privacy-oriented mechanisms include:

🔒 Secure account/profile gate
🔑 SHA-256 hashed passcodes
👤 Isolated student JSON profiles
🚫 Secrets excluded through .gitignore
🚫 Student profiles excluded from version control
🚫 Local vector databases excluded from version control
🚫 Cache and generated runtime data excluded from version control

Sensitive Files

The project should not commit:

.env
student_profiles/
chroma_db/
cache/
secrets/

API keys and other secrets should be stored through environment variables or the deployment platform's secret-management system.

🧰 Technology Stack

Technology	Purpose
Python 3.10+	Core application
Streamlit	Web application and dashboard
Google Gemini API	AI reasoning and recommendations
ChromaDB	Vector database / RAG
Pandas	Data processing
NumPy	Numerical computation
PyPDF2 / pdfplumber	PDF extraction
ReportLab	PDF report generation
hashlib	Passcode hashing
Git / GitHub	Version control
Streamlit Cloud	Deployment

📂 Project Structure

EduPath-AI/
│
├── .streamlit/
│   └── config.toml
│
├── data/
│   └── university/
│
├── src/
│   ├── academic_analysis.py
│   ├── achievements.py
│   ├── career_analysis.py
│   ├── progress_tracker.py
│   ├── rag.py
│   ├── report_generator.py
│   ├── roadmap.py
│   ├── storage.py
│   └── utils.py
│
├── app.py
├── requirements.txt
├── .gitignore
├── LICENSE
└── README.md

🧩 Module Responsibilities

`app.py`

Main Streamlit application and user interface.

`academic_analysis.py`

Handles academic performance and transcript-related analysis.

`achievements.py`

Manages student achievements and milestones.

`career_analysis.py`

Evaluates career-readiness information and career-related gaps.

`progress_tracker.py`

Handles longitudinal progress updates and comparisons.

`rag.py`

Manages retrieval-augmented generation and ChromaDB interactions.

`report_generator.py`

Generates downloadable PDF reports.

`roadmap.py`

Generates and manages personalized academic/career roadmaps.

`storage.py`

Handles student profile and local data persistence.

`utils.py`

Contains shared helper functions and utilities.

🚀 Installation

1. Clone the Repository

git clone https://github.com/mshakeelrasheed/EduPath-AI.git
cd EduPath-AI

2. Create a Virtual Environment

Windows

python -m venv .venv
.venv\Scripts\activate

Linux / macOS

python3 -m venv .venv
source .venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

4. Configure Environment Variables

Create a .env file or configure secrets through your deployment platform.

Example:

GOOGLE_API_KEY=your_google_gemini_api_key

Never commit API keys or other secrets to GitHub.

5. Run the Application

streamlit run app.py

The application will become available through the local Streamlit URL shown in the terminal.

🔑 Google Gemini Configuration

EduPath AI uses Google's Gemini API for AI-powered reasoning and recommendations.

You need a valid Gemini API key.

Store the key securely rather than hard-coding it inside the source code.

For Streamlit deployment, configure the key through Streamlit Secrets.

🧭 User Workflow

1. Sign In
      ↓
2. Create / Load Student Profile
      ↓
3. Upload Academic Transcript
      ↓
4. Provide University Catalog / Syllabus
      ↓
5. Analyze Academic Progress
      ↓
6. Generate Readiness Score
      ↓
7. Identify Skill Gaps
      ↓
8. Receive Remediation Recommendations
      ↓
9. Generate Four-Year Roadmap
      ↓
10. Consult AI Advisor
      ↓
11. Explore Opportunities
      ↓
12. Add Skills / Projects / Achievements
      ↓
13. Reevaluate Progress
      ↓
14. Compare Progress
      ↓
15. Download Progress Audit / Portfolio

📈 Example Readiness Dashboard

A student's readiness profile can be represented as:

Career Readiness
━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Overall Score        74 / 100

Academic             82 / 100
Skills               70 / 100
Projects             68 / 100
Experience           55 / 100

The important point is not only the score itself, but why the score is at that level and what the student can do next.

🔁 From Static Profile to Longitudinal Profile

Traditional academic systems often look like:

Student Data → Analysis → Result

EduPath AI extends this into:

Student Data
     ↓
Analysis
     ↓
Recommendations
     ↓
Student Action
     ↓
New Skills / Projects / Achievements
     ↓
Updated Profile
     ↓
Reassessment
     ↓
Progress Report
     ↓
Next Recommendations

This creates a living academic and career profile rather than a static report.

🏗️ Development Philosophy

EduPath AI follows several design principles:

Grounded AI

AI recommendations should be connected to available academic context whenever possible.

Student-Centered

The system is designed around the student's current state rather than generic career advice.

Longitudinal

Progress is tracked across time instead of being evaluated only once.

Explainable

Readiness is divided into understandable components so students can identify improvement areas.

Privacy-Aware

Student data and application secrets should be isolated from public source control.

Action-Oriented

Recommendations should lead toward concrete actions such as learning, building, applying, or achieving.

🧪 Example Student Journey

Consider a student beginning with:

Academic Readiness: 75
Skills:             52
Projects:           40
Experience:         20

The system may identify:

Primary Gaps:
- Practical ML projects
- Industry experience
- Portfolio development

The roadmap can then prioritize:

1. Complete an ML project
2. Deploy the project
3. Document the project
4. Add the project to the profile
5. Apply for relevant internships

After the student updates their profile:

New Project
      +
Deployment
      +
Internship
      +
Certification
      ↓
Updated Readiness

The system can reassess the student's current position and provide the next recommendations.

🌱 Future Development

Potential future improvements include:

📊 Advanced progress analytics
📅 Semester-by-semester planning
📈 Historical readiness charts
🎯 More granular career-role matching
🧠 Advanced multi-agent career advising
🔍 Improved opportunity verification
🧾 Enhanced portfolio generation
🔔 Progress reminders
📱 Mobile-friendly experience
🏫 Support for additional universities
📚 Larger academic knowledge bases
🔐 More advanced privacy and authentication controls

🌐 Live Demo

Try the deployed application:

🚀 EduPath AI — Live Demo

👥 Team & Contributions

Team Member	Role & Contributions
Muhammad Shakeel Rasheed	Co-Lead Developer & AI Engineer — Project ideation, system architecture, core development, AI/RAG implementation, readiness engine, progress tracking, roadmap, and overall project development
Muhammad Rafay	Co-Lead Developer & AI Engineer — Contributed to project ideation, system design, core development, AI functionality, implementation, and overall project development
Muhammad Abdullah	Project & Presentation Contributor — Contributed to the project and prepared the presentation
Malik Muhammad Anees	Project & Media Contributor — Contributed to the project and created the project demonstration video

Team Links

Muhammad Shakeel Rasheed — GitHub · LinkedIn
Muhammad Rafay — LinkedIn
Muhammad Abdullah — LinkedIn not provided
Malik Muhammad Anees — LinkedIn not provided

👨‍💻 Author

Muhammad Shakeel Rasheed

Co-Lead Developer & AI Engineer
BS Artificial Intelligence — The Islamia University of Bahawalpur

📄 License

This project is licensed under the MIT License.

See the LICENSE file for details.

🎓 EduPath AI

Analyze your journey. Track your progress. Build your future.

**Built with Python • Streamlit • Google Gemini • ChromaDB • ReportLab**

⭐ If you find EduPath AI helpful, consider starring the repository.

Name		Name	Last commit message	Last commit date
.devcontainer		.devcontainer
.streamlit		.streamlit
data		data
src		src
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
app.py		app.py
requirements.txt		requirements.txt

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🎓 EduPath AI

Privacy-First Academic Companion & Longitudinal Career Navigator

📌 Overview

🎯 Problem Statement

1. Syllabus Disconnect

2. Generic AI Advice

3. No Longitudinal Tracking

4. Scattered Achievements

5. Privacy Concerns

💡 Solution

✨ Key Features

📄 1. Academic Transcript Analysis

📚 2. University Syllabus Grounding

RAG Pipeline

📊 3. 100-Point Career Readiness Score

Readiness Formula

🛠️ 4. Skill Gap & Remediation Engine

Example

🗺️ 5. Adaptive Four-Year Roadmap

🤖 6. Grade-Aware AI Advisor

🎯 7. Verified Opportunities

📈 8. Longitudinal Progress Tracking

Example

🏆 9. Achievements & Milestone Tracking

Achievement Flow

📑 10. Downloadable Progress Audit & Portfolio

🧠 System Architecture

🔄 Continuous Student Progress Cycle

📊 Progress Report Generation

🔐 Privacy & Security

Current privacy-oriented mechanisms include:

Sensitive Files

🧰 Technology Stack

📂 Project Structure

🧩 Module Responsibilities

app.py

academic_analysis.py

achievements.py

career_analysis.py

progress_tracker.py

rag.py

report_generator.py

roadmap.py

storage.py

utils.py

🚀 Installation

1. Clone the Repository

2. Create a Virtual Environment

Windows

Linux / macOS

3. Install Dependencies

4. Configure Environment Variables

5. Run the Application

🔑 Google Gemini Configuration

🧭 User Workflow

📈 Example Readiness Dashboard

🔁 From Static Profile to Longitudinal Profile

🏗️ Development Philosophy

Grounded AI

Student-Centered

Longitudinal

Explainable

Privacy-Aware

Action-Oriented

🧪 Example Student Journey

🌱 Future Development

🌐 Live Demo

👥 Team & Contributions

Team Links

👨‍💻 Author

Muhammad Shakeel Rasheed

📄 License

🎓 EduPath AI

About

Topics

Resources

`app.py`

`academic_analysis.py`

`achievements.py`

`career_analysis.py`

`progress_tracker.py`

`rag.py`

`report_generator.py`

`roadmap.py`

`storage.py`

`utils.py`