An experimental creative writing course: rewriting classical myths with ChatGPT

Companionship and Control: Experiencing ChatGPT as Writer-Identity Work in L2 Academic Writing

Abstract The rise of generative artificial intelligence (AI) is reshaping the landscape of academic writing. Existing research has largely emphasized functional aspects of tools such as ChatGPT (e.g., efficiency, accuracy, and plagiarism) while often overlooking how writers actually experience such tools. Little is known about the phenomenon of encountering ChatGPT as a second/foreign language (L2) academic writer seeking to establish an authorial voice and develop a professional identity. As such, this study explores in-depth how three Chinese international students enrolled in a UK MA TESOL (Pre-Service) program experienced ChatGPT as both advanced L2 writers and future teachers. Employing Interpretative Phenomenological Analysis (IPA), data were generated through semi-structured interviews about the participants’ prior experiences with ChatGPT, participant-produced recordings of a recent writing session, and stimulated recall sessions based on those recordings. Treating the data sources holistically for each participant and then comparing across participants disclosed three Group Experiential Themes: “Writing that Sounds Better but Means Less,” “Emotional Companionship and Psychological Safety,” and “Taming the Algorithm.” These findings suggest that ChatGPT can be experienced as a mediating presence in the cognitive, emotional, and identity work of multilingual writers. They also reveal a paradox: felt companionship that must be actively controlled. In this sense, the experience of ChatGPT can be a space where identity work occurs through the encounter (e.g., in resisting homogenization, asserting stance, and re-owning phrasing). For TESOL postgraduates, the tool then becomes intertwined with the development of both academic literacy and a professional, authorial voice.

Critical Thinking in a GenAI World: Evaluating the Efficacy of ChatGPT-Assisted Essay Writing in ESL Classrooms

The brisk integration of Generative Artificial Intelligence (GenAI), particularly Large Language Models (LLMs) like ChatGPT, has fundamentally disrupted educational paradigms, specifically second language acquisition (SLA) and English as a Second Language (ESL) writing instruction. While GenAI tools offer unparalleled scaffolding for grammar, vocabulary generation, and structural organisation, their impact on higher-order cognitive skills remains highly contested. This study evaluates the empirical efficacy of ChatGPT-assisted essay writing in university-level ESL classrooms, focusing specifically on its effects on students’ critical thinking (CT) skills. Using a mixed-methods convergent parallel design over a 15-week academic semester, this study examined $N = 124$ undergraduate ESL students split into an experimental group (utilising ChatGPT as a structured co-writing and dialogue partner) and a control group (utilising conventional process-writing methodologies). Quantitative data were gathered using pre- and post-test assessments scored via a validated Critical Thinking Rubric (evaluating argument analysis, evidence evaluation, alternative perspectives, and bias recognition) along with automated linguistic analysis of student essays. Qualitative data were obtained via semi-structured focus group interviews and student reflective journals. The quantitative findings indicate that while the experimental group demonstrated significant improvements in surface-level linguistic accuracy, lexical density, and structural cohesion, their independent scores in evidence evaluation and independent thesis formulation were statistically lower than those of the control group when ChatGPT assistance was removed in unassisted proctored post-tests ($p < .05$). The qualitative thematic analysis revealed a phenomenon termed "cognitive outsourcing," where students routinely accepted AI-generated assertions without epistemological verification. However, a sub-cohort trained in aggressive prompt engineering and dialectical inquiry demonstrated elevated critical engagement. This paper proposes a “Dialectical AI-Scaffolding Framework” designed to shift ChatGPT from a product-generation tool to an intellectual sparring partner, ensuring that GenAI advances rather than compromises critical thinking in ESL pedagogy.

Integrating Micro Fiction through Genre Based-Approach for EFL Creative Writing

Creative writing is believed to be challenging for EFL learners because it requires idea generation, content management, and organization skills. This conceptual paper is presenting a pedagogical framework which incorporates micro fiction with Genre-Based Approach (GBA) to develop EFL students’ creative writing by focusing on the creation of alternative plots. The paper advocates for employing micro fiction, a short narrative generally told within 100-500 words, as core text type since its brevity and precision will lighten the cognitive load on the learner. The integration follows GBA’s systematic five-stage instructional cycle, which consist of Building Knowledge of the Field (BKOF), Modeling of the Text (MOT), Joint Construction of the Text (JCOT), Independent Construction of the Text (ICOT), and Assessment S Reflection. This methodical progress takes students through micro fiction examples’ analysis, the creation of their micro fiction together, and the production of unique autonomous works. Such activities strengthen genre knowledge, sharpen linguistic clarity, and enhance critical thinking and teamwork. From several empirical studies, it can be known that this approach is effective in teaching writing. Thus, this paper concluded that integration of micro fiction through GBA is recommended for enhancing students’ creative writing.

When AI-Mediated Feedback Writing Works and When It Does Not: Lessons Learned from Two Classroom Cases

Doing DDL with AI: Classroom Interactions in Collaborative Writing

Abstract In the last few decades, data-driven learning and generative artificial intelligence (AI) applications have become increasingly visible in academic writing instruction. The data-driven learning approach promotes students’ lexical and morphological awareness by encouraging them to explore language in context, while generative AI tools offer new possibilities for idea generation, text development, and linguistic feedback. However, limited process-based studies exist on how these two approaches work together in collaborative writing in the classroom and how teacher guidance shapes this process. This study aims to explore how B2-level students enrolled in a university preparatory program use data-driven learning and generative AI tools in the collaborative opinion essay writing process. Designed as a qualitative classroom-based case study, the research was conducted over a seven-week module. Structured student diaries, teacher journals and semi-structured interviews were used in the data collection process. Findings indicate that tool use was constructed through peer negotiation and teacher mediation. Students used generative AI for idea generation and corpus tools for lexical verification and contextual checking. The study highlights the critical role of pedagogical design and teacher facilitation in DDL-AI-assisted writing instruction.

Writing as Orchestration: Reconceptualizing the L2 Writing Construct in AI-Mediated Environments

Traditional second language (L2) writing instruction and assessment frequently emphasize unaided, timed production, a model that no longer fully represents the communicative realities of AI-mediated contexts. This conceptual article aims to reconceptualize the L2 writing construct for educational settings in which generative AI is routinely and legitimately used. The study uses a theory-driven integrative conceptual synthesis. Sources were located through purposive searching of Scopus, ERIC, Web of Science, and Google Scholar, supplemented by citation chaining and journal hand-searching, and screened against stated inclusion criteria across two streams: foundational scholarship on mediated cognition, genre, literacy, and validity, and work on generative AI and writing published from 2020 onward. Forty-seven sources were retained for close analysis, spanning sociocultural learning theory, activity theory, distributed cognition, multiliteracies research, computer-assisted language learning, and language assessment scholarship. Analysis proceeded through manual thematic coding of construct-relevant claims, conducted by the first author and independently reviewed by the second. The resulting orchestration model defines AI-mediated writing as the purposeful coordination of human judgment with machine-generated output under conditions of authorial responsibility. It specifies four interdependent competencies: prompting, critical evaluation, adaptation, and ethical accountability. The analysis shows that traditional dimensions of writing, including coherence, organization, language use, critical thinking, and audience awareness, are not displaced by AI-mediated writing but redistributed across these competencies. The paper also identifies specific challenges for L2 writers, especially the difficulty of evaluating and reshaping fluent AI-generated output in a language still being acquired. The article recommends process-visible assessment designs, genre-specific orchestration tasks, and empirical validation studies that examine construct structure, scoring reliability, and consequential validity.