Integrating AI tools into engineering education: Jordanian Universities’ case study

The integration of generative Artificial Intelligence (AI) tools in education has gained significant attention from various researchers worldwide. The main objective in this study is to examine the impact of using generative AI tools in engineering education. A comprehensive survey was administered to engineering students across various universities in Jordan. The survey is designed to evaluate students’ awareness, usage patterns, perceived benefits, and challenges associated with utilizing AI tools in engineering education in Jordan. The Unified Theory of Acceptance and Use of Technology (UTAUT) is employed as a theoretical framework to investigate the factors influencing engineering students’ usage of AI tools in their academic activities. The study revealed a strong inclination among engineering students toward the use of generative AI. A significant majority of 89% utilized AI tools to enrich their understanding of academic material, while 57.5% expressed a preference for AI-assisted learning over traditional methods such as textbook reading. Notably, the analysis identified a statistically significant difference in usage frequency based on the language of instruction (p-value=0.00). Findings revealed that students studying in English language show higher levels of AI adoption compared to students studying in Arabic. These findings highlight evolving learning behaviors and the growing role of AI in shaping educational experiences.

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Publication Details

Journal
International Journal of Mechanical Engineering Education
Published
2026-01-05
DOI
https://doi.org/10.1177/03064190251410482
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
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article

Integrating AI tools into engineering education: Jordanian Universities’ case study

Ruba Alamad, Heba Alzaben, Khaled Mahmoud Mattar, Abed Alhameed Alhabahba
International Journal of Mechanical Engineering Education
Artificial Intelligence in Healthcare and Education
article

Integrating AI tools into engineering education: Jordanian Universities’ case study

Ruba Alamad, Heba Alzaben, Khaled Mahmoud Mattar, Abed Alhameed Alhabahba
article en

Abstract

The integration of generative Artificial Intelligence (AI) tools in education has gained significant attention from various researchers worldwide. The main objective in this study is to examine the impact of using generative AI tools in engineering education. A comprehensive survey was administered to engineering students across various universities in Jordan. The survey is designed to evaluate students’ awareness, usage patterns, perceived benefits, and challenges associated with utilizing AI tools in engineering education in Jordan. The Unified Theory of Acceptance and Use of Technology (UTAUT) is employed as a theoretical framework to investigate the factors influencing engineering students’ usage of AI tools in their academic activities. The study revealed a strong inclination among engineering students toward the use of generative AI. A significant majority of 89% utilized AI tools to enrich their understanding of academic material, while 57.5% expressed a preference for AI-assisted learning over traditional methods such as textbook reading. Notably, the analysis identified a statistically significant difference in usage frequency based on the language of instruction (p-value=0.00). Findings revealed that students studying in English language show higher levels of AI adoption compared to students studying in Arabic. These findings highlight evolving learning behaviors and the growing role of AI in shaping educational experiences.

International Journal of Mechanical Engineering Education
Quality Education
Openalex Percentile: Top 97%
Artificial Intelligence in Healthcare and Education
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Integrating AI tools into engineering education: Jordanian Universities’ case study — Ruba Alamad, Heba Alzaben, et al. · International Journal of Mechanical Engineering Education (2026) | TGRS Research Map | TGRS