AI-Driven Needs Analysis in ESP : Designing English Syllabus and Materials for Pharmacy Students in Indonesian Higher Education

Abstract

English for Specific Purposes (ESP) plays a vital role in preparing students for academic and professional success, particularly in health sciences where discipline-specific communication skills are essential. In Indonesian higher education, pharmacy students often face challenges due to generic ESP syllabi that fail to address their specialized needs. Traditional needs analysis provides valuable insights but remains limited by its reliance on self-reported data and lack of scalability. To address this gap, this study integrates Artificial Intelligence (AI) into the process of needs analysis and syllabus design for pharmacy-focused ESP instruction. We show how surveys, interviews, and classroom observations were complemented by AI-driven tools such as Natural Language Processing, machine learning, and text mining to uncover subtle patterns in linguistic deficiencies and learning preferences. Results indicate that the AI-enhanced syllabus produced targeted modules in academic reading, technical vocabulary, scientific writing, and oral communication, supported by authentic pharmacy texts and adaptive exercises. Evaluation through pre- and post-tests revealed significant improvements in vocabulary mastery, reading comprehension, writing quality, and oral fluency, while student and instructor feedback confirmed the materials’ relevance and practicality. The study demonstrates that AI-driven approaches can enrich ESP pedagogy, offering a replicable model for curriculum innovation in professional education.