As the internationalization of higher education intensifies — particularly under China’s “Double First-Class” initiative — Chinese scholars are increasingly expected not only to publish in English but also to perform effectively in high-stakes spoken genres such as conference talks, keynote lectures, and doctoral defences. Yet pragmatic features such as hedging remain a persistent barrier, often leading to pragmatic failure in real-time interactions. This study addresses this challenge through an integrated approach that couples corpus-based diagnostics with pedagogical design. Specifically, it investigates the use of lexical verb hedges in Chinese scholars’ spoken academic English by comparing a self-compiled Chinese Scholars’ Academic Spoken English Corpus (CASEC; 696,009 words; 279 events) with the Michigan Corpus of Academic Spoken English (MICASE; 1,848,364 words; 152 events). Following Granger’s reappraised Contrastive Interlanguage Analysis 2.0, MICASE is treated not as a native-speaker benchmark but as one Anglo-American reference variety against which typical L2 patterns can be described. Quantitative analysis revealed that Chinese scholars significantly underused individualized stance markers (e.g., I think) and significantly overused collective, consensus-building markers (e.g., We see). Qualitative analysis interprets these patterns through the lens of cross-cultural pragmatics and genre constraints, suggesting a rhetorical strategy oriented toward collective harmony and the diffusion of authorial responsibility. Crucially, the study moves beyond diagnosis to propose a “diagnosis-to-treatment” pipeline, culminating in a step-by-step Data-Driven Learning (DDL) module complete with full classroom worksheets. The pedagogical design aims to raise scholars’ pragmatic awareness and enhance their international academic communication competence.
This study investigates how ChatGPT constructs knowledge, authority, and pedagogical discourse when responding to English grammar learning queries. While generative Artificial Intelligence (AI) tools are increasingly adopted in English as a Foreign Language (EFL) and English Education contexts, existing literature heav...
Aleesa Salaeh, Anis Maming, Hameedah Haeweasani et al.· Priviet Social Sciences Jour...· 0 citations
The analysis shows that most mismatches follow a recurring pattern in which a French passé composé reporting the students’ own methods, results, or attribution to prior research is rendered in the English present tense, blurring the distinction between the students’ specific findings and established disciplinary knowle...
Kaoutar Atarchi, Abdelkrim Elamari, Mohamed Marouane· International Journal of Lin...· 0 citations
This study examines similarities and differences between native and non-native academic English through a computational corpus-based framework to identify variation in lexical choices, lexical bundles, grammatical patterns, collocations, and academic stance.
N. Akhter, Fatima Khan, Hammad Malik et al.· Journal of Arts and Linguist...· 0 citations
This study investigates register variation in Pakistani English newspaper discourse through a Hallidayan Systemic Functional Linguistics (SFL) lens, addressing a persistent gap in existing Pakistani media research where field, tenor, and mode have rarely been examined together within one integrated framework. Grounded...
Hina Ajmi, Nadeem Anwar, Iqra Anam· TAMSAAL· 0 citations
Background:
This mixed-method study investigates the effectiveness of integrating Google Translate (GT) and Grammarly as a combined English-for-Academic-Purposes (EAP) learning and teaching intervention, aiming to improve the accuracy, clarity, and readability of Indonesian-to-English academic texts.
Methodology:
Th...