StudyPilot AI Syllabus PDF to Personalized Revision Planner and Quiz Generator | Groq + Django

What StudyPilot AI Does

Most students don't fail exams because they skipped studying. They fail because they studied the wrong topics at the wrong time. StudyPilot AI solves that planning problem. A student uploads their syllabus as a PDF, enters the exam date and how many hours they can study each day, and the system builds a revision timetable that fits their situation. It also covers the practice side: for any topic in the plan, the student can generate a fresh multiple-choice quiz in seconds, with explanations for every answer.

The AI layer runs on the Groq API using LLaMA 3.3 70B, which is fast enough that syllabus parsing and quiz generation feel close to instant. PyMuPDF handles text extraction from the PDF. Django manages users, schedules, quiz history, and analytics, and SQLite stores everything. That keeps the project light enough to run on any student laptop.

Project Features

Syllabus Understanding

• PDF upload with text extraction through PyMuPDF, including multi-page university syllabi
• Groq-powered parsing that breaks raw syllabus text into subjects, units, and individual topics
• Automatic difficulty and weightage estimation for each topic, which the student can edit before the plan is created

Personalized Revision Schedule

• Planner inputs for exam date, daily study hours, weak subjects, and rest days
• Day-by-day timetable that gives harder and weaker topics more time
• Built-in spaced repetition, so each topic comes back for revision after 1, 3, and 7 days
• Buffer days reserved before the exam for full-syllabus revision
• Calendar view built with FullCalendar, with tick-off tracking for completed sessions
• Automatic rescheduling that moves missed sessions forward without breaking the rest of the plan
• Downloadable PDF version of the timetable generated with ReportLab

AI Quiz Generator

• Topic-wise MCQ generation through Groq, with easy, medium, and hard levels
• Structured JSON prompting, so every quiz renders cleanly with four options and one correct answer
• Instant scoring with a short explanation for each question
• Full quiz history stored per student

Progress Analytics

• Chart.js dashboard showing accuracy per topic, study streaks, and schedule completion
• Weak-topic detection: topics scoring below 60 percent are flagged and given extra slots in the plan
• Django admin panel for managing users, syllabi, schedules, and quiz records

Applications

• Semester exam preparation for college and university students
• Competitive exam revision where the syllabus is published as a PDF
• Coaching centres that want to hand students a structured, trackable study plan
• Self-learners following an online course outline
• EdTech prototypes that need AI-driven personalization

Who It's For

This project fits BCA, MCA, BTech CSE, BSc IT, and MSc CS students looking for an AI final year project in the education domain that evaluators can relate to immediately. Every examiner has watched students struggle with revision, so the problem needs no convincing. It also suits students who want hands-on experience with large language model APIs, prompt engineering, and PDF processing in a real Django application. If you are exploring more options, browse the full range of AI and machine learning final year projects on CodeAj Marketplace.

Why Choose This Project

• Relevant problem: Study planning is something every student deals with, which makes the viva discussion natural and easy to defend.
• Modern AI stack: Using Groq with LLaMA 3.3 shows you can work with current LLM tooling, not just older ML libraries.
• Real logic beyond the API call: The scheduling algorithm, spaced repetition intervals, and weak-topic feedback loop are written in plain Python, so you have your own logic to explain.
• Clean Django architecture: Separate apps for accounts, syllabus, planner, and quizzes, following the same structure as our other Django projects with source code.
• Free tier friendly: Groq offers a generous free API tier, so the project costs nothing to run during development and demos.
• Pre-built project report: Includes the abstract, system design, DFDs, module descriptions, and testing chapters.

Want another education-focused AI project for comparison? The AI college placement prediction system takes a machine learning approach to student outcomes and pairs well with this one.

Add-On Services

• Project Setup + Source Code Explanation: We install the project on your laptop over Google Meet, Zoom, or AnyDesk and walk you through every module.
• Idea Implementation: Need extra features such as flashcards, a chatbot tutor, or multi-language support? We can build them for you.
• Custom Project Report, Research Paper, and PPT: Written to match your college format and submission guidelines.