Artificial intelligence is increasingly embedded in English language learning, but evidence on generative AI and large language models (LLMs) remains fragmented across skills, learner groups, and tool designs. This systematic review synthesized evidence on personalized learning, automated feedback, speaking, writing, vocabulary, assessment, English for specific purposes (ESP), and physical education. Reporting followed PRISMA 2020. Scopus, PubMed, and ScienceDirect returned 403 records; 14 duplicates were removed, 389 were screened, 54 reports were sought, 48 full texts were assessed, and 17 studies were included. The corpus spanned experimental, quasi-experimental, comparative, mixed-methods, qualitative, survey, review, and system-development designs. Where reported, participant samples included 40 undergraduates, 79 graduate students, and 327 primary pupils, while several design and qualitative studies did not report a single numeric sample. Narrative/thematic synthesis identified four themes: feedback and writing support; speaking and personalized tutoring; changing learner-teacher roles; and affective and ethical conditions. The evidence is recent and heterogeneous, limiting quantitative pooling. FICO was used as an internal appraisal rubric; reviewer-level logs were not retained, so Cohen’s kappa and aggregate FICO scores were not reconstructed retrospectively. Future research should prioritize controlled longitudinal designs, validated outcome measures, transparent reporting, data privacy, and under-represented ESP and sport-science populations.
Lentera Negeri, Kristian Burhan, M. Desman et al.· Lentera Negeri· 0 citations
This systematic review synthesized empirical evidence on large language models (LLMs) and generative AI for English-language learning, assessment, and academic writing, with implications for English for specific purposes (ESP) in sport sciences. Following PRISMA 2020, a structured Scopus TITLE-ABS-KEY search identified 871 records; 50 peer-reviewed journal studies published between 2024 and 2026 met the eligibility criteria. Studies were synthesized using a three-stage thematic approach based on coding, descriptive themes, and analytical themes. Methodological reporting and applicability were appraised using a transparent four-domain FICO rubric. Screening was conducted by one reviewer; therefore, inter-rater reliability statistics were not calculable retrospectively. Four analytical clusters were identified: language-skills learning (8 studies), assessment and evaluation (8), academic writing and feedback (30), and adoption, perception, and learner agency (4). Evidence generally favoured structured, teacher-mediated GenAI use, while LLM assessment showed high reliability in some rubric-based tasks but variable validity. GenAI was most consistently useful for feedback, revision, and self-regulated learning. Because designs and outcomes were heterogeneous, no pooled effect size was calculated. Future research should prioritise discipline-specific ESP studies, longitudinal designs, transparent validation, and responsible AI integration.
Kristian Burhan, M. Desman, Article Info· JRTI (Jurnal Riset Tindakan...· 0 citations
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