genai-in-education
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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
The authors propose a pedagogical framework that combines Systemic Functional Linguistics (SFL) with fine-tuned prompt literacy to support English learners' use of GenAI image generators in multimodal writing. Drawing on ESL student photo essay projects, they argue that integrating multimodal and GenAI literacies is more effective than teaching them separately. The framework guides students in designing prompts using the ideational, interpersonal, and textual metafunctions.
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.
- PaperComputers and Education: Artificial Intelligence21 Jun 2026
Students’ multimodal prompting practices as epistemic work in AI literacy development
Sylvana Sofkova Hashemi
A study of 28 postgraduate students using a university-provided GenAI tool found that prompting strategies range from basic input-output to strategic, iterative, and dialogic practices. Prompting emerges as an epistemic practice where students critically interpret and refine AI outputs, with multimodal tasks revealing challenges in translating abstract meaning into machine-readable prompts. While students develop evaluation and creation competencies, ethical dimensions of AI literacy remain underdeveloped.
Original abstract
: As generative artificial intelligence (GenAI) rapidly transforms higher education, critical questions arise about how students engage with these open-ended tools and the implications for learning. This study provides empirical insight into this research gap investigating (1) the prompting strategies students develop when interacting with a university-provided GenAI tool and (2) how engagement in prompt engineering activities shapes their understanding of GenAI and AI literacy. Data were collected in an exploratory workshop with 28 postgraduate students engaged in collaborative multimodal prompting tasks, including the creation of short stories or poems and corresponding images. Students’ self-documented prompting histories and reflections were analysed qualitatively using reflexive thematic analysis, guided by frameworks for prompting methods and AI literacy. The findings show that students’ prompting strategies vary along a continuum from basic input-output use to strategic, iterative, and dialogic practices. Prompting emerges as a central epistemic practice through which students critically interpret, refine, and negotiate AI-generated outputs. Multimodal engagement exposes challenges in translating abstract meaning into machine-readable prompts, fostering awareness of system limitations, bias, and the need to actively construct coherence across modalities. While students demonstrate developing competence in evaluation and creation, ethical dimensions of AI literacy remain underdeveloped. The findings provide empirical insight into how AI literacy develops through hands-on engagement with GenAI, positioning prompting as an epistemic practice through which students learn to interpret, negotiate, and guide AI-generated outputs, while highlighting the value of iterative, reflective, and multimodal learning designs that foster critical, strategic, and responsible engagement with AI.