ai-assisted-writing
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- PaperTESOL Journal21 Jul 2026
Purpose, Audience, Context, and Culture: A Framework for AI ‐Assisted Writing in ELT Classrooms
Flora D. Floris, Willy A. Renandya
The PACC framework (Purpose, Audience, Context, Culture) is proposed as a conceptual model for AI-assisted writing in ELT. Grounded in Systemic Functional Linguistics and Genre Theory, it guides students in designing AI prompts and evaluating AI-generated texts. The framework offers a practical approach for teachers to integrate AI tools into writing instruction while maintaining focus on communicative goals.
Original abstract
Artificial intelligence (AI) tools can help students draft and revise texts more efficiently. However, AI‐generated texts may sound fluent but still fail to meet the needs of readers. This article proposes the PACC framework as a conceptual model for AI‐assisted writing in ELT. Informed by Systemic Functional Linguistics and Genre Theory, PACC focuses on four elements: purpose, audience, context, and culture. Students use these elements to design AI prompts and to evaluate and revise the AI‐generated text. This article explains the theoretical basis of the framework, shows how it can be applied in writing lessons, presents an integrated classroom example, and discusses possible implementation challenges. The PACC framework contributes to ELT writing instruction by giving teachers and students a practical and meaningful way when preparing AI prompts and when reviewing AI‐generated texts.
- PaperAssessing Writing2 Jun 2026
AI-assisted vibe coding with Base44: Creating personalized tools for argumentative writing
Mengru Pan, Kexin Li, Chun Lai
The authors present AI-assisted vibe coding, a no-code approach using the Base44 platform, enabling learners to create personalized argumentative writing tools rather than passively receiving AI feedback. This repositioning aims to foster deeper cognitive processing and metacognitive engagement. The article illustrates workflow examples and discusses instructional integration and limitations.
Original abstract
Existing scholarship abounds in pedagogical efforts to leverage the advantages of GenAI to support argumentative writing. Yet, these efforts have mainly positioned learners as consumers of GenAI feedback and support on writing, which is often associated with concerns of cognitive atrophy and metacognitive laziness. The emergence of AI-assisted vibe coding makes it possible for learners to play a more active role in creating personalized AI solutions. The repositioning of learners’ roles in relation to GenAI may induce deeper cognitive processing and greater metacognitive engagement. This article introduces AI-assisted vibe coding, a novel no-code development approach that enables learners as technology creators by designing and developing their own personalized argumentative writing tools. We present Base44, a full-stack, AI-powered platform that facilitates this process, and illustrate how students can leverage it to create personalized tools for addressing authentic argumentative writing challenges through concrete workflow examples. We explore the potential of integrating AI-assisted vibe coding into argumentative writing instruction, examine its limitations, and offer directions for future teaching practice and research.
- PaperLanguage Learning & Technology1 Jan 2026
Distributed agency in AI-assisted L2 writing
Pin-Hsi Patrick Chen, Yichun Liu
A longitudinal qualitative study of Taiwanese university students examined how distributed agency between human writers and generative AI shapes L2 writing across intentionality, forethought, self-reactiveness, and self-reflectiveness. Findings show that distributed agency both supports and constrains learner engagement depending on how it is exercised.
Original abstract
This longitudinal qualitative study examines distributed agency between human writers and generative AI in the context of L2 writing. Grounded in Bandura’s theory of agency, the study analyzes students’ written texts, reflective accounts, and AI interaction logs collected from Taiwanese university students. The findings indicate that human-AI distributed agency shapes the enactment of L2 writing across intentionality, forethought, self-reactiveness, and self-reflectiveness. Moreover, distributed agency both supports and constrains learners’ engagement, depending on how it is exercised.