Reimagining EFL Learning: AI Tools vs. Traditional Methods in an Underserved Area

This study aims to compare the effectiveness of AI-enhanced learning with traditional teaching methods in English as a Foreign Language (EFL) education, focusing on their impact on learner progress and overall experiences. The research employs a mixed-methods design, incorporating both quantitative and qualitative data to provide a comprehensive understanding of the educational landscape. The primary data collection tool is a questionnaire developed using Google Forms, which enables participants to evaluate the effectiveness of AI tools compared to traditional EFL methods. The study is conducted in educational institutions in northern Pakistan and involves learners who have been exposed to both AI-supported instruction and traditional instruction. Data is analyzed using descriptive and inferential statistical methods to identify trends and correlations within the results. The findings indicate that AI-enhanced learning offers greater personalization, immediate feedback, and a more user-friendly experience. In contrast, traditional learning remains valuable for providing authentic human interaction, structured guidance, and interpersonal engagement, which are essential for holistic language development. This study contributes to the growing body of literature by offering a context-specific analysis of AI integration in EFL instruction within an underserved educational region. It provides a comparative perspective on digital versus traditional methods, helping educators and policymakers understand how AI can be integrated effectively without undermining the benefits of conventional teaching practices.