AI-Powered Personalization in ESP: Enhancing Learner Autonomy and Engagement in English for Professional Contexts

Abstract

The educational landscape has been greatly transformed by recent advances in Artificial Intelligence (AI) to facilitate highly personalized and adaptive learning environments. Personalization is particularly important in English for Specific Purposes (ESP) where teaching focuses on the linguistic and communicational needs of professionals in specific domains. Yet, despite the large-scale integration of AI tools into wider education, there is a significant gap in conceptual literature on how AI can best be employed to boost learner autonomy and engagement in ESP contexts. This conceptual paper investigates the transformative role of Artificial Intelligence (AI) in enhancing personalization, learner autonomy, and engagement within English for Specific Purposes (ESP) instruction. While AI tools such as intelligent tutoring systems, chatbots, adaptive learning platforms, and learning analytics have increasingly permeated general language education, their pedagogical integration into the specialized contexts of ESP remains under-theorized. To address this gap, the study synthesizes findings from 30 peer-reviewed journal articles published between 2015 and 2025, employing a thematic literature review approach to derive a comprehensive, interdisciplinary framework for AI-enhanced ESP learning. Five main themes emerged from the synthesis: (1) personalized learning paths through adaptive technologies, (2) real-time, AI-powered feedback, (3) chatbot-facilitated learner autonomy, (4) gamification-supported engagement, and (5) ethical and pedagogical considerations in AI integration. These dimensions were examined across diverse ESP domains including engineering, business, academic writing, healthcare, and tourism revealing how AI-driven instruction can address domain-specific linguistic and professional communication needs. The proposed framework emphasizes the central role of personalization in supporting autonomous, engaging learning experiences, while also underscoring the need for ethical, context-aware design and responsible instructional alignment. This paper contributes a structured conceptual model that bridges applied linguistics, educational technology, and AI studies, offering both theoretical insight and practical guidance. Thus, it highlights the need for human-AI complementary, ethical for AI supplemented ESP pedagogy are discussed including the transparency and domain compliance. It underscores the significance of human-AI collaboration, ethical transparency, and domain alignment in the implementation of AI-enhanced ESP pedagogy. This study underscores the necessity for ethical, human-centered AI integration in ESP instruction and advocates for ongoing empirical research to substantiate the proposed framework, thereby guaranteeing scalable, inclusive, and pedagogically robust applications of AI in global language learning environments