Eye tracking and artificial intelligence for competency assessment in engineering education

A review

Bibliographic Data

ID22163386
AuthorsYakhoub Ndiaye (0000-0001-5259-0127, Singapore University of Technology and Design, corresponding author), Kwan Hui Lim (0000-0002-4569-0901, Singapore University of Technology and Design), Luciënne Blessing (0000-0001-9612-1357, Singapore University of Technology and Design)
Year2023
Volume8
Publication date2023-11-03
Open AccessYes
TypeARTICLE
VenueFrontiers in Education (JOURNAL)
Journal identifiersISSN: 2504-284X • E-ISSN: 2504-284X
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/feduc.2023.1170348
OpenAlexW4388291772
LanguageEN
References cited49

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

Engineering education · Engineering management · Eye tracking · Pedagogy · Computer Science · Engineering · Gaze Tracking and Assistive Technology · Psychology · Visual and Cognitive Learning Processes · Artificial Intelligence

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