genai-literacy
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- PaperTESOL Quarterly16 Jul 2026
Fostering Fine‐Tuned Prompt Literacy for Multimodal Writing: A Systemic Functional Linguistics‐Informed Framework
Xiao Tan, Chaoran Wang, Ruonan Zhao
This paper introduces a Systemic Functional Linguistics-informed pedagogical framework for fine-tuned prompt literacy in GenAI-assisted multimodal writing. It demonstrates that separating instruction on multimodal and GenAI literacies hinders effective multimodal composition, and that the framework, which guides students in designing prompts based on ideational, interpersonal, and textual metafunctions, enhances GenAI-integrated digital multimodal composing in ESL writing courses.
Original abstract
The rise of GenAI‐powered image generation tools presents new opportunities for English learners to engage in digital multimodal composing (DMC) in both creative and critical ways. At the same time, it calls for a reconceptualization of DMC pedagogy in language education. In this article, we build on the concept of fine‐tuned prompt literacy (Kang & Yi, 2023) in the context of GenAI‐assisted multimodal writing and introduce a pedagogical framework informed by Systemic Functional Linguistics (SFL) to support its development and implementation. We conceptualize fine‐tuned prompt literacy at the intersection of multimodal literacy and GenAI literacy, emphasizing the need for students to envision meaningful GenAI outcomes and engage with GenAI tools critically and strategically. Drawing on our research on ESL students' photo essay creation with GenAI image generators, we show that separating instruction on multimodal and GenAI literacies may hinder students' process of creating multimodal resources in an intentional and rhetorically effective way. To address this gap, we present an SFL‐informed framework that guides students in designing prompts based on the ideational, interpersonal, and textual metafunctions. Teaching observation and students' writing from a first‐year ESL writing course illustrates the framework's potential to enhance GenAI‐integrated DMC instruction. This brief report offers both theoretical insights and practical strategies for teaching multimodal writing in the era of GenAI.
- PaperReCALL13 Jul 2026
Fostering generative AI-supported self-regulated learning (GenAI-SRL) in informal digital language learning through literacy and interactions: A two-stage PLS-SEM-ANN approach
Xiaoqi Wang, Lawrence Jun Zhang
A study of 343 Chinese university foreign language learners used PLS-SEM and ANN to examine how GenAI literacy and interactions influence self-regulated learning (SRL) in informal digital language learning. Awareness and evaluation dimensions of GenAI literacy predicted GenAI-SRL, while usage and ethics did not. Student–student, student–teacher, and student–GenAI interactions facilitated GenAI-SRL, with student–student interaction as the strongest predictor, and awareness and evaluation partially mediated the effects of student–student and student–teacher interactions.
Original abstract
Generative artificial intelligence (GenAI) enables foreign language learners to extend their learning beyond formal instruction and develop their autonomy. However, research has not adequately examined how learners regulate their learning with GenAI or how their GenAI literacy and multiple types of interactions influence their self-regulated learning (SRL) in GenAI-supported informal digital language learning settings. We address this gap by analyzing data from 343 Chinese university foreign language learners through partial least squares structural equation modeling (PLS-SEM) and artificial neural networks (ANN). PLS-SEM showed that awareness and evaluation significantly predicted GenAI-supported SRL (GenAI-SRL), whereas usage and ethics did not. Student–student, student–teacher, and student–GenAI interactions emerged as facilitators of GenAI-SRL. These three interaction types also significantly influenced most GenAI literacy dimensions, with three of them predicting awareness, usage, and evaluation, while only student–student and student–GenAI interactions significantly predicted ethics. Mediation analysis demonstrated that awareness and evaluation partially mediated the effects of student–student and student–teacher interactions on GenAI-SRL. The mediating pathways through student–GenAI interaction were not significant. ANN models identified student–student interaction as the strongest predictor of GenAI-SRL. These findings inform GenAI literacy development and the design of systems to support GenAI-SRL in informal learning contexts.