Cost-Effective Language Labs: Implementing an AI Chatbot-Based Speaking Module in Government College and Evaluating Its Impact on Students' Communicative Competence and Willingness to Communicate.

Abstract

This study evaluates a cost-effective AI chatbot-based speaking curriculum at a Sindh government college to address shortages in oral English skills. In a quasi-experimental, pre-test/post-test control-group design with 200 undergraduate students, the module was tested on two primary variables: objective communicative competence, measured using a performance-based rubric, and Willingness to Communicate (WTC), measured using a standardized scale. Quantitative analysis showed that the experimental group that used the chatbot alongside regular instruction showed significant improvements in communicative competence and WTC, with large effect sizes, and that these gains were favorably associated with module usefulness and reduced foreign language anxiety, according to correlational analysis. AI chatbots are powerful, context-sensitive teaching tools that directly target emotional speech impediments and create a low-anxiety practice environment. The study found that such modules can transform basic computer labs into dynamic language learning hubs in resource-constrained public education systems.

Keywords: AI Chatbot, Communicative Competence, Willingness to Communicate (WTC), Cost-Effective Language Lab, Government College, Quasi-Experimental Design.