Perspectives of English Language Teachers in Iran on the Role of Artificial Intelligence in Correcting Grammatical Errors

Abstract

The present study aimed to investigate the effectiveness of artificial intelligence (AI)-based tools in correcting grammatical errors and to evaluate the perspectives of English as a Foreign Language (EFL) teachers in Iran regarding the opportunities, challenges, and practical implications of integrating these technologies into English language instruction. This study employed a mixed-methods research design combining quantitative and qualitative approaches. The statistical population consisted of Iranian EFL teachers, from whom 41 participants were selected through convenience and purposive sampling. Quantitative data were collected using a researcher-made questionnaire grounded in the Technology Acceptance Model (TAM), measuring perceived usefulness, perceived ease of use, attitudes toward AI, and operational challenges associated with AI implementation. The reliability of the questionnaire was confirmed through Cronbach’s alpha coefficients ranging from 0.79 to 0.88. In the qualitative phase, semi-structured interviews were conducted to explore teachers’ experiences and perceptions in greater depth. Quantitative data were analyzed using descriptive statistical procedures, while qualitative interview data were examined through thematic analysis to identify recurrent themes related to infrastructural, pedagogical, systemic, and ethical concerns. The findings demonstrated that teachers perceived AI-based grammar correction tools as significantly more effective than traditional correction methods in mechanical and rule-based domains such as punctuation and article usage. AI received substantially higher mean scores in punctuation correction (M = 4.6) and article usage (M = 4.4) compared to traditional approaches. However, no meaningful difference was observed in correcting tense-related errors, while traditional teacher-led methods remained significantly superior in addressing coherence and semantic organization. Thematic analysis further revealed that the most frequently reported barriers to AI implementation were infrastructural limitations, including unstable internet access and inadequate hardware facilities (85%), followed by pedagogical concerns such as students’ over-reliance on AI and superficial learning of grammatical concepts (78%). Teachers also emphasized systemic constraints linked to test-oriented educational policies (65%) and ethical concerns including data privacy and algorithmic bias (45%). Overall, the findings indicated a positive but cautious attitude among Iranian teachers toward AI integration in grammar instruction.