Impact of AI and Teaching Competency in the English Language: An Empirical Study.

Abstract

Artificial intelligence (AI) is increasingly being integrated into language classrooms through tools such as intelligent tutoring systems, automated writing evaluation, AI-based grammar and pronunciation checkers, chatbot-mediated conversation practice, and adaptive learning platforms. This study empirically examines the impact of AI integration on teaching competency in the English language among school and college-level English language teachers. Primary data were collected through a structured questionnaire from 216 English language teachers working in government and private educational institutions. The instrument measured artificial intelligence integration in teaching (AIIT) — encompassing AI awareness, tool adoption, perceived usefulness, and instructional application — and teaching competency in English language (TCEL), disaggregated into three sub-dimensions: pedagogical competency, assessment competency, and learner engagement competency. All constructs were measured on validated five-point Likert scales, and reliability was confirmed through Cronbach's alpha (0.80 to 0.88). Data were analyzed using Pearson correlation and simple linear regression. The results reveal that AI integration in teaching has a statistically significant and positive impact on overall teaching competency (β = 0.64, R² = 0.41, p < .001), pedagogical competency (β = 0.59, R² = 0.35, p < .001), assessment competency (β = 0.55, R² = 0.30, p < .001), and learner engagement competency (β = 0.61, R² = 0.37, p < .001). The findings indicate that teachers who adopt AI tools more extensively in their instructional practice report meaningfully higher levels of teaching competency across all measured dimensions, with the strongest effect observed for overall teaching competency and learner engagement. The paper concludes with theoretical and pedagogical implications, limitations, and directions for future research.