AI-Focused Elementary Math Tutor Module

Description: The goal of this project is to develop a C# library/module for educational apps that incorporates advanced AI techniques to dynamically personalize the learning experience. The module aims to adjust question difficulty, analyse performance, and identify student strengths and weaknesses in real-time. The key features include AI-powered student proficiency tracking using Natural Language Processing (NLP) for deeper understanding, Bayesian Networks or Markov Decision Processes for assessing strengths/weaknesses, and Reinforcement Learning to dynamically adapt question difficulty and timing. The module also includes adaptive question generation using AI to select questions matching the student's updated level and AI-driven performance assessment to automatically identify incorrect answer patterns and common mistakes. Key Features: • AI-powered Student Proficiency Tracking: o NLP analyses student explanations for deeper understanding of weaknesses. o Bayesian Networks or Markov Decision Processes assess strengths/weaknesses based on response patterns and difficulty. o Reinforcement Learning dynamically adapts question difficulty and timing based on real-time performance. • Adaptive Question Generation: o AI selects questions from a diverse pool matching the student's updated level, preventing repetition. o Natural Language Generation techniques can create original word problems tailored to student needs. • AI-driven Performance Assessment: o Automatically identify incorrect answer patterns and common mistakes to pinpoint individual weaknesses. o Analyze completion time and answer accuracy to evaluate mastery and suggest areas for improvement. • Comprehensive AI-based Reporting: o Generate personalized reports highlighting student progress, strengths, weaknesses, and recommended learning paths. o Use machine learning to predict future performance and potential learning gaps. Technical Requirements: • Standalone C# library for easy integration. • Advanced AI libraries and tools for NLP, Bayesian Networks, Reinforcement Learning, and Natural Language Generation. • Secure data storage and processing capabilities to protect student data privacy. • Target audience student ages from 4-to18 • Offer immediate feedback during practice sessions . Clarify the specific model choices within each category: • NLP: Which specific NLP model(s) do you plan to use (e.g., Transformers like BERT, GPT-3, or more traditional approaches like LSTMs)? • Bayesian Networks or Markov Decision Processes: Which type of model do you prefer for this task (Bayesian Networks or Markov Decision Processes)? • Reinforcement Learning: What RL algorithm will you use (e.g., Q-learning, SARSA, deep Q-networks)? • Natural Language Generation: Which NLG model(s) will you utilize (e.g., GPT-3, BART, T5)? 2. Briefly explain the reasoning behind your model choices: • Why did you select these particular models for each task? • What are their strengths and weaknesses in the context of adaptive math education? 3. Mention any specific libraries or frameworks you'll employ: • Will you use existing libraries for these models (e.g., spaCy for NLP, TensorFlow or PyTorch for RL), or will you develop custom implementations? Deliverables: • Fully functional AI-powered Adaptive Math Question Generator library with comprehensive documentation. Excepted input and output Input: • Student answers: • Time taken per question. • Additional student data (optional): Past performance, learning history, demographics, etc., can further personalize the adaptive learning experience. Output: • Immediate feedback: o Explanation: Provide concise explanations for both correct and incorrect answers, promoting understanding and self-correction. o Hints: Offer hints or alternative solution approaches when students struggle, fostering independent problem-solving skills. • Student progress report: o Performance trends: Track progress over time to identify areas of improvement and celebrate achievements. • Strengths and weaknesses: Highlight specific areas where the student excels or needs additional support. • Recommended learning paths: o Adaptive difficulty: Suggest questions of appropriate difficulty that build upon strengths and address weaknesses effectively. • Time suggestions: o Estimated time per question: Offer time estimates based on the question's difficulty and the student's historical performance. Testing and evaluation: Define plans for thorough testing and evaluation of the library's effectiveness and accuracy in improving student learning outcomes.