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The rapid integration of artificial intelligence (AI) tools into English language education has created new demands on teachers, who must navigate both the potential and limitations of these technologies. This mixed-methods study examines English as a Foreign Language (EFL) teachers’ perceptions of AI tools, focusing on usage practices, perceived opportunities, challenges, and professional development needs. Drawing on the Technology Acceptance Model and an AI-TPACK perspective, a questionnaire was distributed to 28 EFL teachers working across multiple educational levels and international contexts. Likert scale items measured perceived usefulness, ease of use, confidence, and concerns, while open-ended questions elicited detailed accounts of benefits, risks, and training expectations. The findings show that teachers overwhelmingly use ChatGPT (89.3 percent) and value AI primarily for lesson planning efficiency and learning personalization, with about two-thirds agreeing that AI makes planning more efficient and supports tailored activities. At the same time, substantial proportions report concerns about ethical issues, student overreliance on AI, and the accuracy of AI-generated content, each cited by roughly two-thirds of respondents. Almost half of the teachers indicate they have not received sufficient training, revealing a readiness gap between their willingness to use AI and their preparedness to do so responsibly. The qualitative analysis highlights a dual identity in teacher discourse, where AI is framed both as a powerful productivity assistant and as a potential threat to pedagogical autonomy and student creativity. The study extends technology acceptance theorizing by situating TAM constructs within an AI-TPACK lens and by relating teacher perceptions to recent evidence on Kosovan students’ trust–usability paradox in AI-supported language learning (Author, Year). It offers practical implications for AI-focused professional development, institutional policy, and the design of teacher-centered AI integration frameworks.
Braha et al. (Mon,) studied this question.