TEACHING SEMANTIC TRANSPARENCY IN BUSINESS COMPOUND TERMS VIA AI-ENHANCED PBL

Abstract

This study investigates the effectiveness of teaching the semantic transparency of business English compound terms through an AI-enhanced project-based learning (PBL) approach. Addressing the growing use of complex multi-word expressions in business discourse (e.g., blockchain strategy, data governance), the research develops a module integrating natural language processing (NLP) tools, AI-generated glossaries, and project-based tasks. The module was implemented with 150 second-year students from Northern University, specifically from the Faculties of Management, Economics, and Software Engineering, all of whom had intermediate (B1–B2) English for Specific Purposes (ESP) proficiency. Over the course of eight weeks, students engaged in terminological analysis, contextual interpretation, and digital project creation using AI tools such as ChatGPT and IBM Watson. The results reveal significant improvement in semantic understanding, with 90.6% of B1- level students advancing to B2 proficiency. This study highlights the pedagogical benefits of combining AI tools with PBL to enhance ESP vocabulary acquisition and offers practical recommendations for developing effective curriculum models in business English education.

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