Quizzler is an AI-powered mobile learning app designed to deliver highly personalized, engaging, and effective learning experiences. Developed as a research-backed project at Thakur College of Engineering & Technology, Mumbai, Quizzler leverages advanced machine learning to overcome the limitations of traditional education.
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Adaptive Assessments
Dynamically adjust quizzes and assignments based on user performance, identifying strengths and targeting weaknesses. -
Personalized Learning Paths
Recommendations of resources and exercises are tailored to individual learning styles and progress. -
Focus Mode
Creates a distraction-free study environment, reducing interruptions and enabling deep concentration. -
Exam Mode
Provides a secure testing environment with features such as single-window enforcement and anti-cheating measures. -
AI-driven Insights
Smart feedback and analytics to guide learners and educators for optimized outcomes.
Quizzler uses a hybrid machine learning architecture:
- Decision Tree: Predicts future performance using historical quiz and activity data.
- Support Vector Machine (SVM): Classifies users into learning styles (Visual, Auditory, Kinaesthetic, Read/Write).
- Bayesian Network: Models the probability of outcomes based on student characteristics and activity.
- Recurrent Neural Network (LSTM): Analyzes sequential learning data for deeper trend prediction.
- K-Nearest Neighbors: Recommends strategies and resources that worked for similar learners.
These models work together to personalize every aspect of the learning journey.
- 15% increase in average test scores for students using Quizzler versus traditional methods.
- Reports of higher engagement, motivation, and personal ownership from users.
- Mixed-methods evaluation: Quantitative academic improvement and qualitative learner satisfaction.
Full research: See Quizzler-RBL-Paper.pdf
Prerequisites:
- Flutter
- Clone this repo: git clone https://github.com/TanmayN22/Quizzler.git
- cd Quizzler
- Install dependencies:
- flutter pub get
- Run:
- Frontend: Flutter, Dart
- Backend / ML: Python (scikit-learn, TensorFlow/PyTorch), API integration as required
- Security: Strong encryption and privacy for all user data
- Privacy Focus: Student data is securely handled, with strict privacy protocols.
- Bias Mitigation: Algorithms are tested and improved to prevent educational disadvantage.
- Equity: Quizzler aims to bridge the tech divide, but device/internet access is still required.
- Piyush Das
- Kaustubh Gondkar
- Tanmay Nayak
Artificial Intelligence & Data Science, Thakur College of Engineering & Technology, Mumbai
- Issues, feature requests, and pull requests are welcome!
- For direct contact, email authors at the addresses listed in the research paper.
Quizzler is making education more personalized, fair, and effective—empowering every learner to reach their full potential.