محمد پرویز

Advances in generative artificial intelligence (AI), particularly large language models, offer new possibilities for language learning. This study, grounded in sociocultural theory, examined the impact of an AI assistant ( Monica ) on English as a Foreign Language (EFL) students’ writing development. A total of 120 male students (aged 26–36, A2 proficiency) participated in a four-month in-service program. The participants were randomly assigned to either an experimental group, which engaged in AI-mediated collaborative writing, or a control group, which collaborated face-to-face. A sequential explanatory mixed-methods design was employed, integrating pretest–posttest measures, questionnaires and semi-structured interviews. Quantitative analyses (Split-Plot ANOVA) showed that the AI-assisted group significantly outperformed the control group in Organization, Language Use, and Mechanics, but not in Content or Vocabulary. Importantly, students’ perceptions shifted dramatically: While only 23% initially held positive expectations, 90% reported positive experiences post-intervention, highlighting the role of affective factors in technology adoption. Qualitative data confirmed cognitive, metacognitive, and motivational benefits of AI interaction, but also revealed challenges including overreliance on AI, uncritical acceptance of feedback, difficulty integrating new vocabulary, and cognitive overload when faced with multiple corrections. Sociocultural concerns, such as academic integrity, face-saving practices, and peer judgment, further influenced student engagement. From a theoretical standpoint, the study contributes to sociocultural scholarship by evidencing the ways in which student–AI interaction shapes and mediates writing development. Pedagogically, it calls for intentional scaffolding to foster critical and reflective practices, reframing AI not as a functional tool but as a meaningful collaborator in the learning process.

The present study aimed at exploring phrasal complexity features in data commentaries produced by graduate students and in research articles written by expert writers. To this end, 25 empirical RAs in the field of Applied Linguistics and 158 data commentaries generated by graduate students of English Language Teaching were comparatively examined. The results revealed that students approximated expert writers in terms of producing two linguistic features (i.e., N+N structures and nominalizations). However, they differed significantly from expert writers in generating four linguistic elements (i.e., attributive adjectives, appositive structures, of -genitives, and PPs as noun post-modifiers). The results also revealed that expert writers’ texts comprise varied presence of exceedingly complex patterns of pre-modification, triple/quadruple/quintuple (pre)modification, a hybrid of novel appositive structures, and multiword hyphenated adjectives. Conversely, graduate students’ language could be characterized by less variety, single/dual (pre)modification, a far less extensive range of noun-participle compounds functioning as nominal pre-modifiers, linguistically limited complex modifications, and minimally multifarious patterns of use associated with N+N formulations. Overall, the findings can give fresh insights into the needs of the L2 student writers in developing an academic text.