Attitudes of Faculty Members at the Colleges of Education, Salahaddin University, Towards Employing Artificial Intelligence Technologies to Enhance the Effectiveness of the Teaching Process

Abstract

       This research aimed to investigate the attitudes of faculty members at the Colleges of Education, Salahaddin University, towards employing Artificial Intelligence (AI) technologies to enhance the effectiveness of the teaching process. It also sought to identify their level of knowledge and awareness of these technologies and to uncover the most prominent challenges they face. The study adopted the descriptive-analytical methodology. The research population consisted of all (698) faculty members in the Colleges of Education at Salahaddin University-Erbil, from which a random sample of (100) faculty members was selected across five colleges. The researchers utilized a questionnaire composed of (30) items distributed across four main domains: Cognitive Attitudes, Affective Attitudes, Behavioral Attitudes, and Obstacles and Challenges. The instrument's validity was confirmed by a panel of experts in curriculum, teaching methods, and measurement and evaluation to ensure the items' relevance to the study's objectives and domains. Furthermore, the instrument's reliability was established through appropriate statistical tests, and an analysis of other psychometric properties confirmed the questionnaire's suitability for field application. The findings revealed that the overall level of faculty attitudes towards employing AI technologies was moderate, with a mean score of (3.42), and the level of awareness and knowledge of AI technologies was also moderate, with a mean of (3.28). The results also indicated statistically significant differences (α ≤ 0.05) in attitudes attributable to years of experience, in favor of faculty with less experience. No statistically significant differences were found based on gender, academic specialization, or college. The most significant challenges identified were the lack of specialized training, inadequate technological infrastructure, and insufficient time for professional development. The study concluded with recommendations for designing specialized training programs, improving the technological infrastructure, and establishing a dedicated center for educational AI at the university.