GENERATIVE AI AND ENGINEERING EDUCATION: MEASURING ACADEMIC PERFORMANCE AMIDST SOCIOECONOMIC CHALLENGES IN YEMEN

Abstract

This study assessed the impact of generative artificial intelligence (GenAI) usage on the academic outcomes of engineering students within the context of Yemen's unique socioeconomic challenges. This study employed a quantitative approach through a structured 5-point Likert-scale questionnaire, which was distributed to 277 engineering students from Taiz University, Yemen. In a resource-constrained environment, the results exhibited a strong correlation between GenAI usage and engineering students’ overall grades and skills development. Additionally, findings showed that socioeconomic challenges that students face in Yemen have moderately hindered students from the effective usage of GenAI for educational purposes, which is a key finding for higher education institutions (HEIs). Also, statistical results showed that almost all respondents are familiar with GenAI tool usage, while 89.17% use ChatGPT as a fundamental component of learning. Of course, the integration of GenAI into education has become inevitable, compelling HEI policymakers to regulate its use and formally adopt it as a primary source of learning systems. Students and educators should obtain continuous training to effectively benefit from GenAI while conforming to ethics and boosting their intellectual capabilities and skills. Raised concerns that the students overreliance on AI tools could undermine their problem-solving abilities and practical skills development while complicating students’ evaluation process for educators. The outcomes of this study could serve as a foundational reference for policymakers, educators, and students in Yemen and similar settings. It also recommends in-depth studies that cover other educational contexts and respondents from other states, rather than undergraduate engineering students in the present study.