Employing artificial intelligence technologies to enhance educational process quality in health education institutions in Yemen

This study aims to examine the role of employing artificial intelligence (AI) technologies in enhancing the quality of the educational process in university-level health education institutions within the Yemeni context. A descriptive analytical study design was adopted. Data were collected via a questionnaire administered to a sample of 280 respondents, including faculty members and academic leaders from university health education institutions. The instrument consisted of AI technologies, the quality of the educational process, and the challenges and requirements for effective AI implementation. The findings revealed that the level of AI utilization was moderate, and the overall quality of the educational process was also moderate from the respondents’ perspective. The results further revealed a statistically significant positive correlation between the use of AI technologies and the quality of the educational process. Moreover, the results revealed that the most influential AI applications for enhancing educational quality included educational data analysis, support for academic decision-making, and personalized learning content. In contrast, the major challenges identified were weak technological infrastructure, insufficient training programs, and the absence of clear institutional policies. On the basis of the study findings, a proposed model for employing AI applications to increase the quality of the educational process was developed. This study represents a scientific and practical contribution to the field of artificial intelligence in education, as it addresses a contemporary issue and offers innovative, applicable solutions suitable for Arab and developing contexts, thereby contributing to the improvement of university health education quality and community service. Future research should adopt longitudinal and mixed-method designs across diverse geographical regions while examining student-centered perceptions and formulating robust institutional governance policies for ethical AI adoption in resource-constrained educational environments.