This bibliometric study maps the intellectual structure and research evolution of Willingness to Communicate (WTC) in EFL and ESL contexts from 2016 to 2025. By examining 391 Scopus-indexed journal articles, the study applies performance analysis, co-citation analysis and co-occurrence analysis to identify publication trends, influential authors, key institutions and dominant thematic clusters. Findings reveal exponential growth in WTC research with strong foundations in ecological, sociocognitive and affective frameworks. Psychological constructs such as motivation, foreign language enjoyment and anxiety emerge as central drivers of communicative behavior while demographic and contextual variables remain comparatively under-integrated. The analysis also highlights increasing engagement with digital learning and artificial intelligence, though theoretical integration across clusters remains limited. Geographical concentration in Asian and Middle Eastern EFL contexts suggests the need for broader cross-contextual validation. By systematically synthesizing the field, this study contributes to evidence-based language pedagogy and supports inclusive, equitable and quality education aligned with SDG 4 (Quality Education). The findings inform sustainable language education practices that promote learner agency, communicative competence and lifelong learning in globalized educational environments.
Research Article Summaries (RASs), a structured abstract, remain underexplored in terms of their linguistic realization. This study investigates the distribution and rhetorical functions of five linguistic features in RASs: sentence types, verb tense and aspect, grammatical voice, first-person plural pronouns, and reporting words. A corpus of 115 RASs published in Science Special Issues between 2017 and 2020 was analyzed through manual, section-based coding of 2,488 sentences containing 63,019 words. The findings reveal that RASs employ linguistic features strategically across their four rhetorical sections. Complex sentences predominate (68.61%), particularly in the Results, whereas simpler structures are more frequent in the Conclusion, supporting clarity and accessibility. Present Simple is the most frequent tense, while Past Simple is strongly associated with the reporting of completed findings and Present Perfect with the continuing relevance of previous research. Active voice predominates (81.13%), indicating a contemporary preference for clarity and authorial agency, while the frequent use of we and complete absence of I demonstrate a controlled form of collective authorial presence. Reporting words also vary rhetorically, with assertive forms such as find, show, and reveal concentrated in the Results and more tentative forms such as suggest used in the Conclusion. These findings demonstrate that grammar in RASs functions as a rhetorical resource rather than a set of isolated structural choices. The study contributes to research on scientific discourse by providing a systematic explanation of RASs and offers practical implications for genre-based, corpus-informed EAP/ESP instruction aimed at supporting non-native English-speaking researchers in international publication.
Recent research has identified systematic differences between AI-generated and human-authored academic writing. However, most studies focus on argumentative essays, leaving the rhetorical effects of AI use on literature reviews underexplored. This paper addresses this gap through a controlled analysis of how AI paraphrasing reshapes argumentation and authorial stance in Arab EFL postgraduate literature reviews under content-preserving conditions.Using a paired corpus design, this study compares ten human-authored literature reviews produced in 2020, prior to the widespread use of large language models, with ChatGPT-paraphrased versions produced using a controlled prompt. This design isolates core AI transformation patterns. Analysis draws on Toulmin’s model of argumentation and Hyland’s stance framework, supported by corpus-based validation.Results indicated systematic rhetorical transformation. While ChatGPT preserved structural coherence and improved surface fluency, the AI-generated texts showed reduced rhetorical complexity and weakened authorial stance. Self-mention and reader inclusion were sharply reduced, hedging became more formulaic, warrants shifted from situated reasoning to generic principles, and contrastive and causal connectives occurred less frequently. These findings clarify which rhetorical features are most vulnerable under controlled AI paraphrasing and position AI paraphrasing as a concern for EFL academic writing pedagogy.
The importance of Gricean maxims in online text communication stems from the lack of prosodic features and non-verbal cues in AI tools that may lead to digital misunderstanding. As AI tools like ChatGPT continue to be integrated into English as a Foreign Language (EFL) classrooms, learners increasingly interact with AI interlocutors whose pragmatic abilities, patterns of adherence to Gricean maxims, and implicature generation may differ fundamentally from those of human speakers. This mixed-methods explanatory study compares AI-generated and human EFL learner communication with respect to adherence to Gricean maxims and the generation and interpretation of conversational implicature. The study sample comprised 84 intermediate-to-advanced EFL undergraduates at a Saudi university who completed a Discourse Completion Task (DCT), an Implicature Interpretation Task (IIT), and a 32-item questionnaire. A standardized prompting protocol was used to prompt ChatGPT to deliver responses to the same DCT scenarios as human participants. Analysis of quantitative data indicated that the AI responses complied with Grice’s maxims of quantity and manner but exhibited productive flouting, reflecting a diminished capacity to engage in the contextually sensitive maxim flouting necessary to generate conversational implicatures. Meanwhile, EFL learners flouted the maxims more intuitively - particularly in relation and quality - generating richer implicature, but their comprehension of AI-generated implicit meaning was poorer than that of human-generated implicature. The study concludes with theoretically informed suggestions for pragmatics-integrated EFL teaching and AI tool development, and proposes adopting generative AI systems tuned to pragmatic depth in language.
English as a First Additional Language (EFAL) plays a central role in primary education within multilingual contexts, yet many learners experience persistent difficulties in reading comprehension. The researchers adopted an interpretive qualitative approach. In this light, data were generated from ten Grade 6 EFAL teachers through face-to-face semi-structured interviews and non-participatory classroom observations. The study was conducted in rural primary schools in the Nquthu circuit, uMzinyathi district in KwaZulu Natal, South Africa. The findings revealed that teachers face interconnected challenges, including learners’ poor pronunciation and weak phonics skills, limited vocabulary, inadequate professional development opportunities and shortages of EFAL reading resources such as textbooks and dictionaries. These barriers significantly constrain learners’ ability to decode texts, construct meaning and engage meaningfully with comprehension activities. Despite these constraints, teachers employed a range of responsive strategies, including reading-aloud techniques, vocabulary games, dictionary use, scaffolding through guided explanation and the incorporation of supplementary reading materials from home. It was recommended that improving EFAL comprehension outcomes requires sustained teacher professional development, adequate resource provision and structured, learner-centred reading pedagogies.
