AHFE Open AccessExploring The Use of ChatGPT4 API in Approaching Math Word Problems
Abstract
With the evolving educational landscape precipitated by the COVID-19 pandemic, online education becomes increasingly prevalent. Much help is needed to provide innovative solutions to address the challenges faced by both students and teachers during this time of crisis. This paper describes an independent research project conducted by a pair of high school students between April 2023 and February 2024, under the mentorship of a senior research scientist at the National Institute of Education in Singapore. The project investigates various methods of Tesseract OCR text recognition, OpenCV image processing, Flask web development and OpenAI’s Large Language Models to improve mathematics-solving applications.Our program extracts text using Tesseract OCR, utilising it as input for the GPT-4 API, enabling a conversational presentation of mathematics problems. Users interact by inputting the image address of the math problem that they would like the AI to solve, and GPT-4 provides solutions with detailed step-by-step explanations. OpenCV improves the provided image’s quality such as making the text or diagrams more distinct to reduce the possibility of them being misinterpreted. Through evaluation by testing with different types of maths problems of varying difficulty, our findings underscore the potential for advanced language models in educational tools, offering interactive and intuitive maths problem-solving experiences. There were a few limitations encountered during experimentation, such as challenges with extraction of non-Latin alphabets and accuracy of the OpenAI’s Large Language Modules when solving more complex diagram problems, highlighting the need for further refinement to enhance the system's robustness and adaptability. Future work involves addressing these limitations to broaden the system's applicability for educational purposes and beyond.
Keywords: mathematics education, large language models, optical character recognition
DOI: 10.54941/ahfe1005481
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