AI-Based Smart Teaching Assistant for Personalized Exam Preparation 
(Android Application)


Project Domain / Category
Mobile Application / Artificial Intelligence (AI)


Abstract / Introduction
Preparing for exams is often challenging for students of Virtual University of Pakistan, as they need to sift through large amounts of study material and video lectures to organize their learning. This project proposes an AI-based smart teaching assistant that helps VU students prepare for exams by automating the process of creating concise notes from textbooks or handouts and evaluating their knowledge through quizzes. The system will allow users to upload complete study materials, and it will automatically summarize each chapter, generating key concepts and important topics. The platform will also create randomized MCQ-based tests to assess student understanding and preparation level, providing feedback to guide their studies. This personalized learning approach aims to improve student engagement and success rates.


Functional Requirements:
Following will be the functional requirements for the proposed project.
* FR1: Allow users (teachers or students) to upload textbooks, handouts, or other study materials in PDF or text format.
* FR2: Use AI and NLP algorithms to analyze the content and generate lecture-wise or chapter-wise summaries and key points.
* FR3: Automatically create short, concise notes for each lecture, chapter or section.
* FR4: Provide the ability to create virtual classes for different courses where students can access study materials and notes.
* FR5: Generate random MCQ-based quizzes from the study materials to evaluate student preparation.
* FR6: Provide a scoring system with immediate feedback for each quiz to help students identify their weak areas.
* FR7: Track student progress and allow teachers to view detailed reports on student performance.
* FR8: Implement adaptive learning features that adjust the difficulty of questions based on the student’s performance.
* FR9: Ensure a user-friendly interface with accessibility features for students and teachers.


Additional Considerations:
* Real-Time Summarization: Use a lightweight AI model like TensorFlowLite for summarizing text to ensure smooth performance on mobile devices.
* Offline Capabilities: Ensure that key features (e.g., accessing summaries, taking quizzes) work offline by storing data locally using SQLite.
* Cloud Integration: Use Firebase for storing larger data sets, managing user data, and synchronizing quiz performance across multiple devices.
* Performance Optimization: Since it's a mobile application, focus on optimizing the AI models for speed and low memory usage.




Tools:


* Development Environment/IDEs: Android Studio (Java/Kotlin), Firebase (for backend services)
* AI and NLP Tools:TensorFlowLite (for on-device AI models), NLTK or Hugging Face Transformers for NLP, Tesseract OCR (for extracting text from images if needed)
* Other Tools Required: SQLite (for local database storage), Firebase (for user authentication, database, and cloud storage), Retrofit (for network operations), XML (for UI design)




Supervisor:
Name:Waqas Ahmad
Email ID:waqas.ahmad@vu.edu.pk
Skype ID:waqas_vu