Math-Lab: A Math Companion

Abstract

MathLab is a substitute knowledge curriculum vitae designed to help students from elementary to high school with their studies. It offers a variety of interactive and AI-powered features to help students better understand and solve complex math problems. Key features include AI Tutor, where students can learn or review topics and even upload news to enhance learning; Air Solve, where students can draw math problems in the air and the system will instantly answer; Quick Solve, an AI-based video generator that creates videos that solve given problems and explain the solution, allowing students to extract rules and procedures to make them more understandable; and Quick Solve, which is an AI-powered video generator that allows students to extract rules and procedures to make them more understandable. The platform also includes AI-powered automatic code generation and repair, where the system generates rules to solve problems and self-corrects errors. The main goal of MathLab is to provide ongoing academic support to weak and struggling students by enabling self-paced and successful learning.

Introduction

I. Introduction

MathLab is an AI-driven educational platform designed to assist students from Grade 1 to HSC in learning mathematics more effectively. It specifically targets students who struggle with traditional methods or lack access to personalized academic support.

Main Goal:
To personalize, simplify, and enhance the learning experience by making math interactive, accessible, and engaging using artificial intelligence and modern technologies.

Key Features

1. AI Tutor

   • Provides custom learning paths.

   • Accepts media inputs (images, PDFs) and gives interactive, tailored explanations.

   • Supports self-paced learning.

2. Air Solve

   • Gesture recognition feature using OpenCV + MediaPipe.

   • Students can draw problems in the air; the system recognizes and solves them in real time.

   • Encourages active and hands-on learning.

3. Quick Solve

   • Automatically generates code for solving math problems.

   • Includes video explanations and error correction logic if the code fails.

   • Helps build computational thinking and coding skills.

4. Practice Sets

   • Students can create and download custom practice problems in PDF format.

   • Tracks progress and supports offline practice.

5. Graph Plotting

   • Allows dynamic manipulation of graph parameters.

   • Helps visualize functions and understand relationships between variables.

6. Code Generation & Fixation Automation

   • AI dynamically generates required functionalities on demand, optimizing system resources.

   • Errors are auto-detected and fixed in real-time using logged diagnostic data.

Literature Survey – Highlights

• Research covers AI in video generation, air-writing recognition, gesture detection, digit recognition, and educational personalization.

• Identified challenges include:

  • Limited student engagement

  • Lack of real-time feedback

  • Poor visualization tools

  • Inadequate support for self-learners

MathLab’s Response to Gaps:

• Enhances engagement via interactive tools like Air Solve and graph plotting.

• Provides instant, real-time AI support through Quick Solve and AI Tutor.

• Bridges the digital divide with accessible features, offline materials, and automated support.

III. Methodology

• Combines multiple technologies (AI, computer vision, deep learning) to provide a multifaceted educational tool.

• Emphasizes real-time processing, student-centric design, and flexibility for various learning styles.

IV. System Architecture

1. Input Layer – Accepts text, files, gestures.

2. Processing Layer – Analyzes input using AI models, solves problems, handles error recovery.

3. Output Layer – Provides graphs, animated videos, and step-by-step solutions.

4. Backend – Built in Python, ensuring secure data handling, real-time response, and system integration.

V. Results and Interface

• A user-friendly dashboard enables easy access to tools.

• Components like Quick Solve, AI Tutor, and Practice Sets are displayed via interactive UI modules.

• Students can request support or resolve issues via a Contact Us feature.

? Overall Impact

MathLab offers a holistic educational platform by:

• Making learning math fun and engaging.

• Providing personalized, on-demand assistance.

• Integrating modern technology (AI, gesture control, automation) to support all learners.

Conclusion

MathLab is an innovative educational platform designed to provide students with advanced learning tools to help them succeed in mathematics. It uses artificial intelligence, interactive gestures, and real-time graphing to provide a fun and effective comprehensive learning experience. Features like AI tutor, quick problem solving, and air problem solving provide students with immediate help for difficult problems, while practice sets and graph plotting features provide ongoing practice and understanding. The platformâs code generation and auto-correction capabilities provide immediate solutions to coding problems, further enhancing its usability. MathLab is not just a problem solving tool. It is a comprehensive solution aimed at facilitating better learning, especially for students from disadvantaged backgrounds. MathLab aims to make a positive and lasting impact on the educational environment by making mathematics more accessible to all students by providing accessible, interactive, and personalized education.

References

[1] S. Kumar, A. S. V. Prasad, \"Automated Generation of Animated Videos for Question-Based Explanations\", The International Journal of Innovative Engineering and Research (TIJER), TIJER, vol. 1, no. 1, pp. 1-8, 2023. [2] K. R. Gupta, S. G. Sharma, \"Real-Time Video to Text Summarization using Neural Network\", International Research Journal of Engineering and Technology (IRJET), IRJET, vol. 7, no. 12, pp. 3435-3442, 2023. [3] M. Kumar, J. L. J. Patil, \"Intelligent Video Editing: Incorporating Modern Talking Face Generation\", Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP), ICVGIP, vol. 16, no. 8, pp. 12-22, 2021. [4] H. B. Shukla, R. K. Rao, \"Image and Video Captioning Using Deep Learning\", International Journal of Creative Research Thoughts (IJCRT), IJCRT, vol. 10, no. 3, pp. 101-108, 2023. [5] R. K. Joshi, P. S. Dhamale, \"Generative AI (Gen AI) Based Video Generation for Classical Dance\", International Journal of Creative Research Thoughts (IJCRT), IJCRT, vol. 9, no. 1, pp. 22-29, 2023. [6] M. K. Nivangune, S. B. Ghawate, H. B. Kasar, \"Air-Writing Recognition System\", International Journal of Research and Analytical Reviews (IJRAR), IJRAR, vol. 10, no. 4, pp. 273-278, 2023. [7] A. K. R., N. H. R., P. J., \"Air Writing Detection and Recognition\", International Journal of Advanced Research in Science, Communication and Technology (IJARSCT), IJARSCT, vol. 2, no. 3, pp. 15-20, 2022. [8] K. Navya, M. Sowmyah, S. A. Amreen, \"A Machine Learning Approach for Air Writing Recognition\", International Journal for Research in Applied Science & Engineering Technology (IJRASET), IJRASET, vol. 11, no. 3, pp. 1234-1240, 2023. [9] P. Kumar, V. S. Vaishnavi, \"Virtual Board: Air Canvas using OpenCV and MediaPipe\", Journal of the Instrument Society of India, ISI, vol. 53, no. 2, pp. 123-130, 2023. [10] A. K. Arundhathy, A. Ashfiya, \"Air-Drawn Character Recognition using AI and Pattern Recognition Methodology\", International Journal of Novel Research and Development (IJNRD), IJNRD, vol. 8, no. 2, pp. 806-812, 2023. [11] S. Sharma, R. Gupta, \"Design and Implementation of Air-Writing Recognition\", Journal of Emerging Technologies and Innovative Research (JETIR), JETIR, vol. 5, no. 8, pp. 976-982, 2018. [12] S. M. Patil, A. R. Patil, \"Air-Writing and Recognition System\", International Journal of Creative Research Thoughts (IJCRT), IJCRT, vol. 10, no. 4, pp. 1234-1240, 2024. [13] C. H. Patil, S. S. Apte, \"An Air-Written Real-Time Multilingual Numeral String Recognition System\", Journal of Computer Science, JCS, vol. 20, no. 8, pp. 1712-1722, 2024. [14] A. Tripathi, A. K. Mondal, L. Kumar, \"SCLAiR: Supervised Contrastive Learning for User and Device Independent Airwriting Recognition\", arXiv preprint arXiv:2111.12938, arXiv, vol. 2021, no. 11, pp. 1-20, 2021. [15] C. Amma, M. Georgi, \"Airwriting: Hands-free Mobile Text Input by Spotting and Continuous Recognition of 3D-Space Handwriting with Inertial Sensors\", Proceedings of the 16th International Symposium on Wearable Computers, ISWC, vol. 16, no. 1, pp. 52-59, 2012. [16] M. Karan, G. Angadi, \"Transforming and Reforming the Indian Education System with Artificial Intelligence\", International Journal of Education and Development using Information and Communication Technology, IJEDICT, vol. 19, no. 1, pp. 45-60, 2023. [17] S. Sharma, R. K. Singh, \"Artificial Intelligence in Indian Higher Education Institutions: A Comprehensive Review\", Journal of Data Science and Management, JDSM, vol. 5, no. 2, pp. 101-115, 2023. [18] P. M. Ramteke, \"The Impact of Artificial Intelligence on Academic Activities in Indian Universities\", International Journal of Advanced Research in Computer Science, IJARCS, vol. 12, no. 4, pp. 89-95, 2023. [19] R. Venugopal, V. Mamatha, \"Impact of Artificial Intelligence (AI) on Teaching and Learning in India\'s Higher Education Sector\", IOSR Journal of Research & Method in Education, IOSR, vol. 13, no. 5, pp. 1-6, 2023. [20] N. Khan, D. Bhanushali, \"Strengthening e-Education in India using Machine Learning\", arXiv preprint arXiv:2105.15125, arXiv, vol. 2021, no. 5, pp. 1-12, 2021. [21] S. Ojha, A. Narendra, \"From Robots to Books: An Introduction to Smart Applications of AI in Education (AIEd)\", arXiv preprint arXiv:2301.10026, arXiv, vol. 2023, no. 1, pp. 1-18, 2023. [22] A. Gupta, P. Sharma, \"Assessing the Challenges and Opportunities of Artificial Intelligence in Indian Education\", International Journal of Global Advanced Research, IJGAR, vol. 2, no. 1, pp. 50-58, 2023. [23] G. Melquiond, \"Plotting in a Formally Verified Way\", arXiv preprint arXiv:2108.03974, arXiv, vol. 2021, no. 8, pp. 1-13, 2021. [24] P. Papaphilippou, \"Revisiting 3D Cartesian Scatterplots with a Novel Plotting Framework and a Survey\", arXiv preprint arXiv:2406.06146, arXiv, vol. 2024, no. 6, pp. 1-18, 2024. [25] M. I. Hossain, S. Kobourov, \"Research Topics Map: rtopmap\", arXiv preprint arXiv:1706.04979, arXiv, vol. 2017, no. 6, pp. 1-16, 2017. [26] S. Aghaei, A. Daeichian, \"A Graphical-Based Method for Plotting Local Bifurcation Diagram\", arXiv preprint arXiv:2105.10938, arXiv, vol. 2021, no. 5, pp. 1-12, 2021. [27] D. Drain, C. Wu, \"Generating Bug-Fixes Using Pretrained Transformers\", arXiv preprint arXiv:2104.07896, arXiv, vol. 2021, no. 4, pp. 1-14, 2021. [28] Z. Fan, Y. Liu, \"Automated Repair of Programs from Large Language Models\", Proceedings of the 45th International Conference on Software Engineering, ICSE, vol. 45, no. 1, pp. 1125-1135, 2024.

Copyright

Copyright © 2025 Dr. Renuka Deshpande, Ashish Santosh Kolekar, Niranjan Bhatu Patil, Nusrat Fatima Shamsher Malik. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.