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The integration of Artificial Intelligence (AI) is revolutionizing English for Specific Purposes (ESP) education.This article explores the transformative potential of advanced AI tools like natural language processing (NLP) and large language models such as GPT-3 in generating highly customized, dynamic learning materials precisely tailored to learners' diverse professional and academic needs across fields like engineering, finance, healthcare and more.By strategically harnessing AI's capabilities, educators can develop immersive, contextualized, interactive learning experiences that effectively target the specialized linguistic, communicative and domain-specific competencies demanded in various professions and industries.The synergistic collaboration between AI's sophisticated content creation abilities and human educators' subject matter expertise and pedagogical skills promises to radically enhance learner engagement and motivation through personalized, adaptive instruction optimized for individual learning styles and goals.Ultimately, this human-AI synergy can significantly improve targeted language acquisition outcomes and real-world communication readiness within ESP contexts.This comprehensive article provides an in-depth examination of current practical applications of AI in ESP, such as automated generation of job-specific dialogues, scenarios and exercises.It analyzes the profound pedagogical implications, including opportunities for self-directed, exploratory learning and richer formative assessment enabled by AI.Key challenges like mitigating AI biases, validating factual accuracy, and integrating AI responsibly alongside instructor roles are also discussed.Additionally, the article offers a forward-looking perspective on AI's future potential for data-driven curriculum design, intelligent tutoring systems, automated skills assessment, and personalized online learning experiences within ESP education.Ethical guidelines and best practices for responsible, equitable AI utilization are emphasized throughout.
Rudik et al. (Mon,) studied this question.