Objective:

To develop an intelligent learning system that adapts to each student’s learning pace, strengths, weaknesses, and preferences using AI/ML to offer personalized content recommendations, quizzes, and progress tracking.

Key Features:

Student Panel:

• Personalized course dashboard

• Skill assessment tests

• Adaptive quizzes and assignments

• Daily learning goals and reminders

• Visual progress tracker

• Learning style selector (visual, auditory, text-based)

AI Assistant:

• Recommends next best topics based on performance

• Tracks strengths/weaknesses per subject

• Suggests revision plans before exams

• Sends motivational and study tips

• Answers academic FAQs using NLP

Instructor Panel:

• Upload categorized content (videos, PDFs, quizzes)

• Set knowledge levels for content (Beginner, Intermediate, Advanced)

• View student progress insights

• Create personalized feedback for students

Admin Panel:

• Manage users, roles, and courses

• View system-wide performance analytics

• AI model training supervision and feedback

Tech Stack:

Workflow (Step-by-Step):

1. User Onboarding

• Student signs up and selects their subjects and learning preferences.

• An optional diagnostic test evaluates initial knowledge level.

2. Learning Profile Creation

• Based on the test, preferences, and learning history, the system:

  • Initializes a student learning profile

  • Identifies strong/weak areas

  • Sets a baseline for adaptive difficulty

3. Content Recommendation Engine

• AI system recommends the next best lesson, using:

  • Performance data (quiz scores, time spent)

  • Learning style (videos for visual learners, articles for readers)

  • Engagement analytics (drop-offs, skipped lessons)

4. Adaptive Quizzing

• Quizzes change difficulty based on student's performance:

  • If correct → increase difficulty

  • If incorrect → show hints or simpler questions

• ML model updates student’s proficiency score after every quiz.

5. Personalized Feedback

• After each session or quiz, the assistant:

  • Summarizes key takeaways

  • Recommends review material if needed

  • Suggests daily or weekly goals

6. Progress Tracking

• Dashboard shows:

  • Topic-wise performance graphs

  • Time spent per subject

  • Upcoming deadlines/goals

• Student can review past performance and AI-generated suggestions.

7. NLP Assistant

• Student can ask natural language questions like:

  • “What should I revise before my math test?”

  • “Explain Ohm’s Law”

• Assistant responds using:

  • Preloaded Q&A

  • Contextual search from course material

  • GPT-based fallback (optional)

8. Instructor Oversight

• Instructors get analytics dashboards with:

  • Students needing help

  • Dropout prediction alerts

  • Suggested improvements for learning materials

9. Admin Capabilities

• Admins manage users, monitor AI performance, retrain ML models, export data reports, and moderate flagged content or behavior.