This study explores the integration of AI-powered tools in English language pedagogy at Isabela State University, focusing on their impact on instructional methods, student engagement, and learning outcomes. Using a qualitative phenomenological approach, data were collected through interviews, focus group discussions, and document analysis from educators, students, and administrators. Findings indicate that AI enhances teaching efficiency by automating feedback and personalizing learning experiences. However, concerns about overreliance on AI, contextual inaccuracies, and reduced critical thinking persist. While students appreciate AI-driven learning, they still require human guidance for deeper linguistic comprehension. Challenges such as technological accessibility and educator training also hinder effective AI implementation. The study underscores the need for a balanced approach that integrates AI with traditional teaching methods while addressing its limitations. Institutions should invest in infrastructure and pedagogical training to maximize AI’s potential in language education. These insights contribute to optimizing AI integration in academic settings.
Jane C. Caliboso (Tue,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: