Teaching writing when the sentences sound better than the thinking

Affordances and Constraints of AI-Generated Writing Feedback

The findings suggest that AI-generated feedback supported targeted revision when it is accessible, interpretable, and aligned with classroom assessment criteria.

STUDENTS’ PERCEPTIONS OF GRAMMARLY AS AN AI-ASSISTED WRITING TOOL IN ACADEMIC WRITING

The growing use of artificial intelligence in language learning has encouraged the development of digital tools that support academic writing, and Grammarly is one of the most widely used applications. This study explored EFL students’ perceptions of Grammarly as an AI-assisted writing tool in academic writing. A qualitative case study was conducted with seventeen fourth-semester students from the English Language Education Study Program at Universitas PGRI Ronggolawe Tuban during the 2025/2026 academic year. Data were collected through classroom observation, questionnaires, and semi-structured interviews, then analyzed using the interactive model of Miles, Huberman, and Saldaña. The findings show that students generally perceived Grammarly positively because it helped them identify grammatical errors, improve sentence structure, and revise writing more efficiently. Grammarly also increased students’ confidence and supported more independent learning through immediate feedback. However, participants also reported several challenges, including inaccurate suggestions, dependence on internet connectivity, and the need to evaluate AI-generated feedback critically. The study concludes that Grammarly is a useful complementary tool that should support, not replace, instructors’ guidance. Integrating Grammarly into EFL writing instruction with appropriate pedagogical support may help improve students’ academic writing skills and promote learner autonomy.

Studies in Second Language Learning and Teaching

Teacher-AI Collaborative Feedback in Senior High School English Writing Teaching

The increasing application of artificial intelligence (AI) in English writing instruction has brought significant changes to the way writing feedback is delivered. While AI-powered writing tools can efficiently identify language errors and provide immediate revision suggestions, effective writing instruction still depends on teachers’ professional guidance. Rather than replacing teachers, AI should be regarded as an instructional partner that supports the feedback process. From this perspective, teacher-AI collaborative feedback has become an important approach to improving writing teaching in senior high schools. Based on formative assessment theory and sociocultural theory, this paper discusses the necessity of teacher-AI collaboration in English writing instruction and proposes a collaborative feedback model that combines AI-generated language feedback with teachers' pedagogical guidance. It is argued that such collaboration not only improves the efficiency of writing feedback but also enables teachers to focus on students’ higher-order writing development, including content organization, logical thinking, and communicative competence. The paper further discusses the pedagogical implications of integrating AI into classroom writing instruction and emphasizes that teachers remain central to the learning process in the age of artificial intelligence.

Chinese Undergraduate L2 Writers' Metacognitive Regulation When Using Teacher and AI Feedback: A Qualitative Study

Generative artificial intelligence is increasingly used alongside teacher feedback in second-language writing, yet limited attention has been paid to how learners regulate their writing when engaging with these two feedback sources. This qualitative study explored the reported metacognitive regulation of five Chinese undergraduate non-English majors who had experience using both teacher feedback and generative AI tools in L2 writing. Data were collected through semi-structured interviews and analysed using reflexive thematic analysis. The findings indicated considerable variation in participants' planning, monitoring, checking and revision practices. Some participants described systematic regulation involving outlining, paragraph-level monitoring and revisions extending beyond explicitly marked problems, whereas others mainly corrected errors directly identified by teachers or AI tools. Most participants regarded teacher feedback as more authoritative for content, organisation, genre and assessment expectations, while AI feedback was valued primarily for its immediacy and support with grammar, vocabulary and sentence-level expression. Participants who reported clearer goals and more active monitoring also perceived greater benefits from feedback, although the study does not establish objectively measured improvement in writing ability. The findings highlight the importance of feedback literacy and learner agency in human-AI feedback environments. Pedagogically, students may benefit from explicit guidance on comparing, evaluating and selectively applying feedback from different sources.

AI-Empowered English Writing Learning: A Review and Reflection Based on a Teaching Practice

Considering pre-service teachers’ dual identity as both “learners” and “future teachers,” the paper explores pathways for developing their writing learning and teaching competencies in the AI era.