Gunet Eroglu is an Asst Professor of Computer Science at the Bahçeşehir University, where she has focused her research on developing technology to assist individuals with dyslexia.
One of her most notable contributions is the development of the Auto Train Brain (ATB) system, which utilizes artificial intelligence (AI) and machine learning (ML) to personalize dyslexia assessments and training.
Dyslexia is a neurological disorder affecting an individual's reading and spelling ability. It is estimated that around 10-15% of the population has dyslexia, making it one of the most common learning disorders.
Traditionally, dyslexia assessments have been done through standardized tests administered by trained professionals. However, these assessments can be time-consuming, expensive, and not always accessible to all individuals.
The ATB system developed by Eroglu and her team aims to address these issues by using AI and ML to create personalized assessments and training. The system utilizes a user's reading and typing patterns to identify areas of difficulty and create a tailored program to improve their reading and spelling skills.
One of the key benefits of the ATB system is its ability to adapt to the user's progress, constantly updating and adjusting the training program as the user improves. This allows for a more efficient and effective way to address dyslexia.
The ATB system has been tested in several studies and has shown promising results in improving reading and spelling skills for individuals with dyslexia. Eroglu and her team are continuing to research and develop the system to makeit widely available to individuals with dyslexia.
In conclusion, Gunet Eroglu's research in developing the Auto Train Brain (ATB) system, is a promising solution that can help individuals with dyslexia improve their reading and spelling skills, using AI and machine learning to personalize assessments and trainings. This can help reduce the time and costs associated with traditional dyslexia assessments, and make them more accessible to a wider population.
