Eye tracking and artificial intelligence for competency assessment in engineering education: a review - Ndiaye, Yakhoub; Lim, Kwan Hui; Blessing, Lucienne (Frontiers in Education, 2023)
In recent years, eye-tracking (ET) methods has 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 perspective, little is known on how ET can be used to provide new insights on, and ease the process of instructor assessment. Traditionally, engineering education are assessed through timeconsuming and labor-extensive screening of their materials and learning outcomes. With regard to this, and coupled with the subjective, open-ended dimensions of engineering design for instance, assessing competency has shown some limitations. To address such issues, alternative technologies such as artificial intelligence (AI) which has the potentiality to massively predict, repeat instructors' tasks with higher accuracy, have been suggested. To date, little is known about the effects of combining AI and ET (AIET) technics to gain new insights on the instructor perspective. We conducted a review over the last decade (2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020)(2021)(2022) in engineering education to study the latest research focusing on this combination to improve engineering assessment. The review was conducted in four (4) databases (Web of Science, IEEE Xplore, EBSCOhost, and Google Scholar) and included specific terms in the topic of AIET in engineering education . The research identified two types of AIET applications mostly focusing 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, Eye tracking and artificial intelligence for competency assessment 2 This is a provisional file, not the final typeset article predict and assess the eye tracking analytics (application of technology). We ended the review discussing future perspectives and potential contributions for assessing engineering learning.