we are very happy to share that we have been given the opportunity to read the following papers during the 12th International Conference on Human Interaction and Emerging Technologies, in the track on Artificial Intelligence, Computing and Intelligent Design. IHIET 2024 will be held in Venice from the 26th till the 28th of August.
The abstract of Exploring the use of the ChatGPT-4 Application Programming Interface (API) in approaching Maths problems reads:
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.
The abstract of An exploration of machine learning and reinforcement learning to support learner well-being reads:
With the high levels of stress in Singapore, mental and emotional well-being is an important health and social issue today. Research has shown the positive effects of pet ownership on mental and emotional well-being, however challenges of owning a pet in Singapore such as pet licensing restrictions, high costs, fear of losing a pet, a busy lifestyle and even allergies may deter pet lovers from owning a pet. Thus, we propose a technology-driven solution to emulate the useful effects of pets while mitigating the challenges of pet ownership. This project focuses on designing emotion recognition and reinforcement learning models as a stepping stone to individualise responses to a person’s emotions. Our approach utilises the output from the emotion recognition model as an input in the proposed reinforcement learning algorithm. Hence, the paper first compares pre-trained and custom trained facial recognition models, and postulates the use of physiological signals via hardware sensors to further enhance the emotion recognition model. This is inspired from the ability of pets to perceive and respond to different emotions based on facial expressions and physiological signals like heart rate. The paper then outlines the development of novel K-Bandit algorithms in reinforcement learning tested on simulated reward functions, with the aim of optimising parameters for individualised responses to a person’s emotions. Since reinforcement learning is typically used in simulation scenarios, this paper works towards developing a model that will eventually learn a person’s preferences in real time by monitoring their emotional changes. To conclude, this project has showcased the feasibility of facial expressions and physiological signals for emotion recognition, and established the effectiveness of our proposed parameter optimisation functions in the K armed bandit reinforcement learning model to customise responses based on an individual’s emotions. We hope this paper can act as a basis for future works in creating a human-friendly prototype to emulate man’s best friend.
Both papers will be read on the 27th of August, in the session on AI, Computing, and Intelligent Design.
