DefinePK

Abstract

The global ascendancy of Artificial Intelligence (AI) presents a transformative potential for English Language Teaching (ELT), particularly in contexts grappling with systemic pedagogical challenges. This study investigates the integration of AI tools in undergraduate ELT in Pakistan, a setting characterized by large class sizes, traditional teaching methodologies, and significant resource constraints. Employing a sequential explanatory mixed-methods design, the research collected quantitative data via surveys from 350 students and 45 instructors, and qualitative data through semi-structured interviews, focus group discussions, and a quasi-experimental study on Automated Writing Evaluation (AWE) tools. The findings reveal a critical dissonance: while students demonstrated strong receptivity and AWE use led to a statistically significant improvement in writing performance, the adoption is severely hampered by a profound digital divide and inadequate technological infrastructure. Instructor perspectives, analyzed through the TPACK framework, indicated cautious optimism tempered by a significant lack of pedagogical training and confidence, underscoring a deficit in the necessary knowledge to integrate AI effectively. Further analysis using the UTAUT model identified unreliable internet, lack of institutional support, and ethical concerns as primary barriers to behavioral intention to use AI. The study concludes that the promise of AI in Pakistani ELT is contingent upon a systemic, multi-faceted approach that moves beyond mere tool provision. It necessitates concurrent investment in robust digital infrastructure, comprehensive teacher professional development focused on pedagogical integration, and the formulation of context-sensitive ethical guidelines to ensure AI acts as a bridge for educational equity rather than a further source of division.
Keywords: Artificial Intelligence (AI), English Language Teaching (ELT), Pakistan, Technological Pedagogical Content Knowledge (TPACK), Automated Writing Evaluation (AWE), Digital Divide