Speaker
Description
This study presents an AI-enhanced reimagining of a traditional needs analysis (NA) used to develop an English for Specific Purposes (ESP) syllabus for probationary hotel receptionists at Regalia Gold Hotel in Nha Trang, Vietnam. The research engaged 25 participants—22 receptionists aged 24–35 and 3 stakeholders (a Front Office Manager, Senior Receptionist, and ESP instructor). Data was collected through surveys, semi-structured interviews, and on-site observations. Findings revealed that listening and speaking skills were the top priorities, with learners strongly preferring interactive role-play, task-based learning, and instructors with hospitality industry experience. Core communicative functions—such as handling guest inquiries, telephone etiquette, small talk, and complaint resolution—shaped a functional syllabus grounded in real-world workplace needs. To scale and sustain this training, the study proposes integrating Artificial Intelligence (AI) into ESP delivery. Tools such as AI chatbots, automated pronunciation feedback, and adaptive learning platforms can provide individualized, on-demand simulations and formative assessment. These systems also enable ongoing NA by tracking performance data and adjusting instruction accordingly. However, implementation feasibility depends on institutional buy-in. While AI offers scalability and personalization, some stakeholders—particularly hotel managers—express hesitation due to initial investment costs and perceived disruption to traditional training structures. Thus, this study advocates for a hybrid, context-sensitive model: grounded in human-centered pedagogy, but strategically enhanced by AI to meet evolving linguistic and industry demands.
Keywords: AI in TESOL, ESP syllabus, needs analysis, hotel receptionists, adaptive learning, AI feasibility, Vietnam hospitality.
