Artificial Intelligence in ESP and EAP Pedagogy : A Scoping Review of Practices, Challenges, and Future Directions

Luluk Iswati; Abd. Rajab

doi:10.55606/jupensi.v6i2.7513

Authors

Luluk Iswati Universitas Muhammadiyah Yogyakarta
Abd. Rajab Universitas Muhammadiyah Makassar

DOI:

https://doi.org/10.55606/jupensi.v6i2.7513

Keywords:

Artificial Intelligence, EAP, ESP, Generative AI, Scoping Review

Abstract

Artificial intelligence (AI) has rapidly transformed language education by providing innovative tools that support teaching, learning, and assessment. In English for Specific Purposes (ESP) and English for Academic Purposes (EAP), AI technologies such as ChatGPT, automated writing evaluation, grammar assistants, machine translation, and intelligent tutoring systems have increasingly been adopted to facilitate academic and professional communication. However, evidence regarding AI implementation in ESP/EAP remains fragmented across educational technology, applied linguistics, artificial intelligence in education, and language assessment research. This study aims to map current AI-mediated pedagogical practices, identify major challenges, and propose future research directions for AI integration in ESP and EAP. A scoping review methodology guided by the Joanna Briggs Institute framework and PRISMA-ScR reporting guidelines was employed. Literature was identified using the Population–Concept–Context (PCC) framework, resulting in 45 relevant sources consisting of empirical studies, review articles, policy reports, methodological references, technical papers, and ESP/EAP theoretical works. The findings reveal that AI has primarily been utilized for writing feedback, revision support, materials development, prompt-based learning, simulated communication, AI literacy, and assessment redesign. Nevertheless, concerns remain regarding hallucination, linguistic bias, disciplinary authenticity, privacy, academic integrity, and learner overdependence. The review concludes that effective AI implementation in ESP/EAP requires human verification, genre-based pedagogy, AI literacy, learner agency, and transparent assessment practices. These findings provide practical implications for language teachers, curriculum developers, and higher education institutions seeking responsible AI integration.

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