Fine-Tuned Prompt Literacy for GenAI-Mediated L2 Writing: An Interaction-First Learning-and-Accountability Framework

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Authors: Joohoon Kang, Jongsung Won
Cited by: 1
References: 26

Abstract

Generative AI (GenAI) is reshaping second language (L2) writing not only by altering how learners generate, revise, and refine text but also by changing how writers justify, disclose, and remain accountable for AI-mediated decisions. Yet much prompt-literacy work still treats prompting as output optimization or leaves it under-theorized as a general ability to “use AI well.” This conceptual article addresses that gap by reconceptualizing Fine-Tuned Prompt Literacy (FTPL) as an interaction-first learning-and-accountability framework for GenAI-mediated L2 writing. We argue that prompt literacy should be understood not simply as better prompting, but as the trained ability to set communicative and genre constraints, interrogate provisional AI outputs, corroborate claims, revise prompts and texts iteratively, and document accountable uptake decisions. To clarify FTPL’s theoretical distinctiveness, we position it in relation to AI literacy, critical GenAI literacy, and prompt literacy research, and define four interlocking dimensions—learner empowerment, prompt optimization, critical evaluation, and ethical responsibility. We further operationalize the framework through observable interactional indicators, process evidence, and assessment/accountability implications relevant to instructional and institutional contexts. By reframing prompt literacy as a genre-sensitive and ethically accountable interactional competence, this article offers a conceptual model for studying and designing GenAI-mediated writing beyond product improvement alone.

Fields of Study & Subjects

Second Language Acquisition and Learning EFL/ESL Teaching and Learning Artificial Intelligence in Healthcare and Education

Publication details

Journal / source
Applied Sciences
Publisher
MDPI AG
Volume
16
Issue
9
Pages
4198–4198
Publication date
24 April 2026
Language
EN