Exploring EFL Teachers’ Perceptions of Artificial Intelligence Integration in E-Assessment Design: Implications for Efficiency and Objectivity

Abstract

The present study explores EFL educators’ perceptions of AI-generated questions used in electronic examinations and examines whether these perceptions vary according to educators’ educational stage, years of teaching experience, and academic specialization. Data were collected through a validated questionnaire administered to EFL teachers working in public education, and the study employed an empirical research design. The data were analyzed using descriptive statistics, including means and standard deviations, as well as inferential statistics, such as the Kruskal–Wallis test and the independent samples t-test. The findings indicate that teachers generally hold strong, positive perceptions of AI-generated e-examination tasks, particularly regarding objectivity, reduced bias, accurate feedback, time efficiency, and alignment with Bloom’s cognitive taxonomy. Teachers also reported a high level of satisfaction with the integration of artificial intelligence in testing and assessment practices. Furthermore, the inferential analysis revealed no statistically significant differences in teachers’ perceptions based on educational stage, years of teaching experience, or academic specialization, suggesting that educators share broadly similar professional views regarding the use of AI-assisted assessment. Overall, the study highlights the considerable potential of artificial intelligence to enhance fairness, efficiency, and pedagogical quality in EFL assessment. At the same time, it underscores the importance of maintaining human oversight, ethical guidance, and a culture of continual professional development to support the responsible and effective implementation of AI technologies in educational assessment.