Bridging the Gap: Aligning Communicative Language Testing Principles with AI-Driven Assessment in Civil Aviation Ground Service English

Abstract

The integration of Communicative Language Testing (CLT) principles with AI-driven automated assessment poses a significant challenge in professional language testing. Addressing this issue within the specific context of Civil Aviation Ground Service English, this study explores pathways for their logical reconciliation. Through conceptual analysis and theoretical deduction, with a focus on human-AI interaction scenarios, we demonstrate that the synergy between CLT and AI stems from a shared focus on competency measurement. Key findings reveal that: (1) standardized competency dimensions in CLT can be operationalized into data-processable formats for AI; (2) within professional contexts, AI algorithms can be tailored using authentic service corpora to meet CLT’s demand for situational authenticity; and (3) a division of labor based on competency level—where AI handles standardized scoring of lower-order competencies and human-AI collaboration assesses higher-order competencies—effectively resolves the tension between CLT’s dynamic communication and AI’s static algorithms. Ultimately, the study constructs a three-dimensional integration framework encompassing “professional register,” “competency level,” and “human-AI division of labor,” offering a theoretical model for CLT-AI integration and a practical blueprint for innovating Civil Aviation Ground Service English assessment.