ESP-based sociolinguistic exercises with AI integration for technical students

Purpose: This study aims to investigate the potential of integrating English for Specific Purposes (ESP) methodology with artificial intelligence (AI) technologies to enhance sociolinguistic competence among technical university students. Traditional ESP courses often prioritize linguistic aspects such as grammar and vocabulary, leaving pragmatic and context-sensitive communication underexplored. Research Methodology: The research adopts a conceptual and descriptive design based on Hutchinson and Waters’ ESP framework and Canale and Swain’s communicative competence model. The study synthesizes recent empirical findings on AI-assisted language learning, particularly the use of chatbots, speech tools, and digital applications to strengthen sociolinguistic competence. Results: Findings from prior studies indicate that AI integration significantly improves learner motivation, oral confidence, and engagement. AI-supported tools foster better pragmatic awareness, register and dialect practice, and create positive classroom climates. Additionally, AI enhances both teacher perspectives on digital integration and learners’ linguistic competence development. Conclusions: The article proposes a methodological model of AI-assisted ESP sociolinguistic exercises. This integration is expected to enrich technical students’ communication skills by embedding sociolinguistic awareness into ESP instruction. Limitations: The study is conceptual in nature and relies on secondary data. Future empirical testing in classroom settings is required to validate the proposed model. Contribution: This research contributes a pedagogical framework that demonstrates how AI can complement ESP in fostering sociolinguistic competence, offering practical recommendations for technical higher education contexts.

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