A Hybrid Gaze Pointer with Voice Control

Accessibility in technology has been a challenge since the beginning of the 1800s. Starting with building typewriters for the blind by Pellegrino Turri to the on-screen keyboard built by Microsoft, there have been several advancements towards assistive technologies. The basic tools necessary for anyone to operate a computer are to be able to navigate the device, input information, and perceive the output. All these three categories have been undergoing tremendous advancements over the years. Especially, with the internet boom, it has now become a necessity to point onto a computer screen. This has somewhat attracted research into this particular area. However, these advancements still have a lot of room for improvement for better accuracy and reduced latency. This project focuses on building a low-cost application to track eye gaze which in turn can be used to solve the navigation problem. The application is targeted to be helpful to people with motor disabilities caused by medical conditions such as Carpel Tunnel Syndrome, Arthritis, Parkinson’s disease, tremors, fatigue, and Cerebral Palsy. It may also serve as a solution for people with amputated limbs or fingers. For others, this could end up being a solution to situational impairments or a foundation for further research. This tool aims to help users feel independent and confident while using a computer system

Eye tracking and artificial intelligence for competency assessment in engineering education: a review

In recent years, eye-tracking (ET) methods have gained an increasing interest in STEM education research. When applied to engineering education, ET is particularly relevant for understanding some aspects of student behavior, especially student competency, and its assessment. However, from the instructor’s perspective, little is known about how ET can be used to provide new insights into, and ease the process of, instructor assessment. Traditionally, engineering education is assessed through time-consuming and labor-extensive screening of their materials and learning outcomes. With regard to this, and coupled with, for instance, the subjective open-ended dimensions of engineering design, assessing competency has shown some limitations. To address such issues, alternative technologies such as artificial intelligence (AI), which has the potential to massively predict and repeat instructors’ tasks with higher accuracy, have been suggested. To date, little is known about the effects of combining AI and ET (AIET) techniques to gain new insights into the instructor’s perspective. We conducted a Review of engineering education over the last decade (2013–2022) to study the latest research focusing on this combination to improve engineering assessment. The Review was conducted in four databases (Web of Science, IEEE Xplore, EBSCOhost, and Google Scholar) and included specific terms associated with the topic of AIET in engineering education. The research identified two types of AIET applications that mostly focus on student learning: (1) eye-tracking devices that rely on AI to enhance the gaze-tracking process (improvement of technology), and (2) the use of AI to analyze, predict, and assess eye-tracking analytics (application of technology). We ended the Review by discussing future perspectives and potential contributions to the assessment of engineering learning