An Empirical Study on the Effectiveness of Artificial Intelligence Tools in English Language Acquisition and Teaching Strategies within an ESG Framework

Abstract

This study examines the effectiveness of Artificial Intelligence tools in enhancing English language acquisition and teaching strategies within an Environmental, Social, and Governance perspective. A predictive modeling design was employed using a simulated cohort of 210 undergraduate students and 35 instructors. The study analyzed pre- and post-intervention outcomes alongside inferential statistical methods, including paired sample t-tests and regression analysis. The modeled results indicate significant improvements in vocabulary, pronunciation, and writing skills, with vocabulary showing the highest gain. Additionally, engagement indicators suggest increased learner motivation, autonomy, and preference for AI-supported learning environments. Teaching strategy outcomes further reveal a strong shift toward blended learning, interactive instruction, and facilitative teaching roles. Regression analysis identifies AI tool usage as the strongest predictor of performance improvement.
Findings also highlight that AI integration supports educational sustainability and inclusivity; however, governance challenges such as data privacy, algorithmic bias, and digital inequality remain critical concerns. Overall, the study concludes that AI tools can substantially enhance language learning outcomes when implemented within structured pedagogical and ethically grounded ESG frameworks.