مسعود آزادنیا

ChatGPT-assisted language instruction is gathering irresistible momentum in today’s fast-moving, AI-governed world of educational enterprises. The current scoping review article sought to analyze and synthesize the existing literature on the complementary use of ChatGPT in second/foreign language (L2/FL) education and pedagogy. Aligned with Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA‑ScR), the study adopted a systematic approach to evidential data search and analysis. A total of 28 peer-reviewed articles retrieved from well-established databases, such as Scopus and Web of Science (WoS), were reviewed to map the scope and consequences of the growing body of literature on using ChatGPT as an L2/FL learning/teaching tool. Centering around five predetermined variables, including setting, design, focus area, objective, and concluding remarks, the content or thematic analysis results helped to shape a clear picture of the literature under review. The literature-derived evidential data also referred to the fruits of ChatGPT-assisted instruction under three distinct headings: learning outcomes, pedagogical outcomes, and cognitive-affective impetus for language learning and teaching. The review results also revealed the repercussions of the instruction mainly concerned with ethical and technical concerns. Given the immature but fast-paced nature of the literature on the instructional model, this early attempt, though provided a brief sketch of the research scope and chief outcomes of the model, needs to be complemented with future systematic reviews to fully unlock the potential of language pedagogy well suited to the demands of today’s technology-governed education.

This study compared the Discussion section of theses written by M.A. English L1 and L2 students regarding lexical sophistication. Certain linguistic features were applied to investigate whether texts written in English L2 had any similarities or differences with native speakers’ (NSs) texts. To achieve this, 20 English L2 theses authored by Iranian M.A. students of Islamic Azad University of Isfahan (Khorasgan), were sampled. As such, 20 English L1 theses written by M.A. English NSs of the same major in the US and UK were randomly downloaded as a comparison corpus of English L1. The corpora were later uploaded to Coh-Matrix that processes text and discourse at different levels of language. Two main statistical procedures for data analysis were the MANOVA (Multivariate Analysis of Variance) and Discriminant Function Analysis. According to Coh-Metrix analysis, the results revealed certain similarities and differences between the corpora. Specifically, more CELEX content words were used in the NSs theses. However, the differences between the two corpora did not reach a statistical significance in terms of other indices of lexical sophistication (i.e., polysemy, concreteness, hypernymy, age-of-acquisition scores, and lexical diversity). Concerning these similarities and differences, academic writing pedagogy and theses writing abilities of Iranian English L2 learners can be improved by applying both word processing tools like Coh-Metrix, appropriate writing strategies and techniques according to the given results of current contrastive corpus study.

Research on native vs. non-native formulaic language use in academic texts, despite its wealth in scope and frequency, lacks an inclusive conceptualization of a non-native language learning context. Impressed by such a flawed approach, the bulk (if not all) of studies in the field compared the use of different multi-word strings in the academic discourse of either foreign or second language learners with a native baseline. The current study sought to address the gap, focusing on the structural and functional use of lexical bundles in two culturally parallel corpora developed in two non-native learning context modes: English as a foreign (EFL) and second (ESL) language. To this end, research reports written by Iranian Applied Linguistics MA and Ph.D. learners studying in different universities in Iran and English-speaking provinces of Canada were compared by a structurally similar native corpus, running cross-tabulation, Chi-square, and residual analysis analyses. The results revealed a significant association between language learning context and lexical bundle use on a functional level. The contextual variations yielded significantly different patterns of use concerning several micro-functions underlying text-oriented and research-oriented functions. Compared to functional differences, the between-corpus structural differences were inconspicuous, specifically concerning micro-structures constituting noun, prepositional, and verb phrase-related bundles. The study embraced the notion that EFL writers need to have immense exposure and enhanced language input available in ESL and native learning contexts to foster a native-like formulaic language.  

The literature on second/foreign language (L2/FL) discourse is replete with corpus-based studies into the use of various features representing lexical proficiency. Nonetheless, the lexical construct of English for academic purposes (EAP) texts developed by postgraduates majoring in teaching English as a foreign language (TEFL) still sounds like a relatively unexplored domain that merits further multi-dimensional investigation. To narrow the gap, the authors in the current study set out to evaluate the lexical richness of a corpus containing doctoral dissertations written by Iranian TEFL students in terms of lexical density, diversity, and sophistication. Taking advantage of the computational tool Coh-Metrix to analyze the lexical features, the corpus was analyzed in comparison with a first language (L1) baseline containing doctoral dissertations written by English native speakers. The comparative analysis of the L1 and L2 corpora revealed that the texts written by Iranian TEFL learners were lexically less diverse but more sophisticated. Additionally, the lexical density of the L2 corpus exceeded that of the L1 one in terms of nouns and adjectives. Based on the results drawn from a discriminant function analysis (DFA), the features representing lexical sophistication and density were found to be the best predictors of lexical richness since they could significantly discriminate between the two sub-corpora. The findings may provide new insights into the ways of evaluating and enhancing the lexical richness of FL/L2 written discourse.