Yarmouk University Students’ Perceptions when Utilizing AI-Driven Machine Translations applications for translating academic texts between English and Arabic

Abstract

This study investigates the utilization of AI-powered machine translation by students at Yarmouk University for translating texts between English and Arabic. It employs the uses and gratifications theory (UGT) to demonstrate the role of these applications in student’s academic pursuits. We designed a structured questionnaire to collect insights into students’ needs and satisfaction with these tools. The study aims to determine the degree to which these technologies support academic pursuits, encompassing participants from various faculties and majors at Yarmouk University. Based on preliminary findings from this study, Google Translate was the most preferred AI-driven machine translation among students, primarily because of its user-friendly interface and widespread utilization. While these applications satisfy most academic requirements, challenges persist regarding translation accuracy and clarity. Despite positive feedback from students regarding the ease of use, significant hurdles persist in translation quality.  This necessitates further enhancement and research in this field.

Highlights

1. The study investigates Yarmouk University students’ perceptions of AI-driven machine translation applications when translating academic texts between English and Arabic.
2. It applies Uses and Gratifications Theory (UGT) to explain students’ motivations, satisfaction levels, and acceptance patterns.
3. Findings show that Google Translate is the most widely used application (81.8%), primarily due to ease of use and accessibility.
4. While students report high levels of satisfaction and ease of use, translation accuracy remains the main reported challenge.
5. The study demonstrates that AI-powered translation tools are widely accepted across gender, faculty, and academic levels, indicating broad institutional integration.