How can posthumanism help us reframe AI-mediated literacy practices? And what implications does such reframing have for cultivating AI literacy in language and literacy education? This article explores these two imperative questions through a case study analyzing two multilingual undergraduate students’ meaning-making and meaning-negotiation intra-actions with AI technologies in a writing classroom. The case study reveals a productive tension between these students’ experiments with posthumanist literacy and their entrenched humanistic assumptions. Ultimately, through the case study, the authors hope to demonstrate that reframing and re-engaging with AI literacy through a posthumanist lens may offer students and educators a relational approach to developing and cultivating AI literacy.

• AI can offer useful writing feedback when used combined with peer review. • AI and peer responses were often similar and mutually reinforcing. • When AI and peer responses differed, the perspectives were often complementary. • Evaluating AI feedback fostered student agency and AI literacy. Cycles of drafting and revising are crucial for student writers' growth, and formative assessment plays an important role. However, many teachers lack the time or resources to provide feedback on drafts. While research suggests that AI feedback is high enough quality to be used for draft feedback, especially when assignment-specific criteria are used (Steiss et al., 2024), it must be used in a human-centered process. AI has the potential to reduce educational equity gaps in writing support (Warschauer et al., 2023), but when narrowly implemented, technologies can deepen divides (Stornaiuolo, et al., 2023). Peer and AI Review + Reflection (PAIRR) combines peer review best practices with AI review in an approach that emphasizes student agency and reflection. Using a mixed methods approach, this study examined student perceptions of AI utility in the context of peer review. Results indicate that AI tools offer useful feedback when combined with peer review. Students found the similarity between AI and peer feedback reassuring, while also valuing their complementary perspectives. Moreover, by evaluating AI outputs, students developed AI literacy, gaining familiarity with AI feedback's affordances and limitations while learning ethical ways to use AI in their writing processes.

This case study investigates how two ESL graduate students, Ian and Sam, use ChatGPT in their research writing after receiving a comprehensive tutorial based on Warschauer et al.’s (2023) AI literacy framework. We analyzed their engagement with ChatGPT across prompt categories including genre, content, language use, documentation, coherence, and clarity. Data were collected from research paper drafts, ChatGPT chat histories, and interviews. Data analyses included coding ChatGPT prompts, textual analysis of drafts, and thematic analysis of interview transcripts . Results show that while both participants utilized ChatGPT for understanding genre conventions and content development, they developed distinct approaches reflecting their individual backgrounds. Ian selectively used ChatGPT for specific assistance needs, while Sam engaged more systematically, particularly for APA style and coherence checks. Both approaches maintained academic integrity and scholarly voice, demonstrating that Generative AI tools can be effectively tailored to individual needs without compromising ethical standards. This study highlights how advanced ESL writers can adapt GenAI tools to their unique writing processes, offering insights into the diverse ways AI can enhance academic writing while preserving individual agency. The findings suggest that AI integration in academic writing can be customized to support diverse writing goals and backgrounds.

In this paper, I explore first-year students' self-reported preferences for choosing source material in a digital, research-based writing setting. I argue that widespread skepticism towards online information has led to an "objectivity bias," where students prefer sources perceived as neutral and objective. Through qualitative interviews, I report that this bias may result in an overreliance on data-driven and empiricist sources, often at the expense of valuable personal narratives and experiential knowledge. I highlight the role of digital platforms and search algorithms in shaping these preferences and discuss the implications for teaching information literacy.

The question of how generative Artificial Intelligence (Gen AI) will reshape communication is causing questions and concerns across the field of education, particular literacy and writing classrooms. Although important questions have surfaced surrounding the varied effects on writing instruction and ethical implications of AI in the classroom, there are calls for deeper investigations about how these tools might shape multimodal composing processes. This study builds upon this developing field by exploring how 21 university students in literacy education courses multimodally composed with generative AI and their perspectives on the use of AI in the classroom. Data sources included screen capture and video observations, design interviews, pre- and post- surveys, and multimodal products. Through qualitative and multimodal analysis, four main themes emerged for understanding preservice teachers’ multimodal composing processes: (1) composing was an iterative process of prompting guided by the AI tools, (2) composers exhibited two distinct processes when designing their projects, (3) AI shaped creative possibilities, and (4) play, humor, and surprise served a key function while composing. Preservice teachers’ perspectives also revealed insights into how AI shaped engagement with content, the importance of scaffolding AI in the classroom, and how ethics were intertwined with technical function and teaching beliefs.

• This study explores GenAI's role in multimodal composition, including Adobe Firefly and DALL·E. • GenAI reshapes the composition stages of invention, designing, and revising. • Despite its limitations, GenAI offers alternative solutions to wicked problems. • Post-GenAI use, students critically revise and iterate their compositions. • The study contributes to future research and teaching of AI-assisted composition. This study explores the integration of generative artificial intelligence (GenAI) design technologies, including Adobe Firefly and DALL·E, into the teaching and learning of multimodal composition. Through focus group discussions and case studies, this paper demonstrates the potential of GenAI in reshaping the various stages of the composition process, including invention, designing, and revising. The findings reveal that GenAI technologies have the potential to enhance students’ multimodal composition practices and offer alternative solutions to the wicked problems encountered during the design process. Specifically, GenAI facilitates invention by offering design inspirations and enriches designing by expanding, removing, and editing the student-produced design contents. The students in this study also shared their critical stance on the revision process by modifying and iterating their designs after their uses of GenAI. Through showcasing both the opportunities and challenges of GenAI technologies, this paper contributes to the ongoing scholarly conversations on multimodal composition and pedagogy. Moreover, the paper offers implications for the future research and teaching of GenAI-assisted multimodal composition projects, with the aim of encouraging thoughtful integration of GenAI technologies to foster critical AI literacy among college composition students.

In this paper, we demonstrate how studying the rhetorics of ChatGPT prompt writing on social media can promote critical AI literacies. Prompt writing is the process of writing instructions for generative AI tools like ChatGPT to elicit desired outputs and there has been an upsurge of conversations about it on social media. To study this rhetorical activity, we build on four overlapping traditions of digital writing research in computers and composition that inform how we frame literacies, how we study social media rhetorics, how we engage iteratively and reflexively with methodologies and technologies, and how we blend computational methods with qualitative methods. Drawing on these four traditions, our paper shows our iterative research process through which we gathered and analyzed a dataset of 32,000 posts (formerly known as tweets) from X (formerly Twitter) about prompt writing posted between November 2022 to May 2023. We present five themes about these emerging AI literacy practices: (1) areas of communication impacted by prompt writing, (2) micro-literacy resources shared for prompt writing, (3) market rhetoric shaping prompt writing, (4) rhetorical characteristics of prompts, and (5) definitions of prompt writing. In discussing these themes and our methodologies, we highlight takeaways for digital writing teachers and researchers who are teaching and analyzing critical AI literacies.

This paper examines ChatGPT's use of evaluative language and engagement strategies while addressing information-seeking queries. It assesses the chatbot's role as a virtual teaching assistant (VTA) across various educational settings. By employing Appraisal theory, the analysis contrasts responses generated by ChatGPT and those added by humans, focusing on the interactants’ attitude, deployment of interpersonal metaphors and evaluations of entities, revealing their views on Australian cultural practice. Two datasets were analysed: the first sample (15,909 words) was retrieved from the subreddit r/AskAnAustralian and the second (10,696 words) was obtained by prompting ChatGPT with the same questions. The findings show that, while human experts mainly opt for subjective explicit formulations to express personal viewpoints, the chatbot's preference goes out to incongruent ‘it is’-constructions to share pre-programmed perspectives, which may reflect ideological bias. Even though ChatGPT displays promising socio-communicative capabilities (SCs), its lack of contextual awareness, required to function cross-culturally as a VTA, may lead to considerable ethical issues. The study's novel contribution lies in the in-depth investigation of how the chatbot's SCs and lexicogrammatical selections may impact its role as a VTA, highlighting the need to develop students’ critical digital literacy skills while using AI learning tools.

The predictive relationship between handwriting automaticity and children's writing performance is well documented. However, less is known about the relationship between keyboarding automaticity and children's keyboard-based writing performance. In this exploratory study, we examined the unique contributions of automaticity in both writing modalities in predicting Grade 2 students (N = 49) paper-based and keyboard-based writing performance (i.e., compositional quality and fluency) after controlling for students’ literacy skills (i.e., spelling, word reading, and reading comprehension), attitudes toward writing, gender, and nesting due to classroom. Multilevel modelling results showed that automaticity predicted students’ paper-based compositional quality and keyboard-based compositional quality and fluency. Findings further suggested that the relationship between automaticity and writing performance was stronger in keyboard-based text composing than in paper-based text composing. These results reinforce the role of automaticity of transcription skills in predicating the writing performance of beginning writers across modalities and stress the significance of explicit pedagogy and frequent instances of practice to promote the mastery of transcription skills across modalities in the early years of schooling.

In this article, we draw on focus group interviews collected for the Wayfinding Project to explore how university alumni orient themselves as writers while participating in social media after graduation. By looking at alumni's self descriptions of their writing processes across public networks, we are able to trace pathways that recognize the rhetorical and communicative intentions of users, while also acknowledging the roles that serendipity, creativity, and the unexpected play in shaping these literate practices. Specifically, we point to how these alumni describe their experiences as they adapt to addressing audiences across different platforms and confront the “reach” of those platforms for engaging unexpected audiences. Several focus group participants use the term “branding” as a way to describe how they conceive of their writing across multiple social networks. These participants describe their public, networked writing as a form of managing their identities at the same time that they are “branding” themselves to manage the expectations of multiple audiences. In sum, our research shows us how the unexpected audiences generated through social media participation operate in tension with writers’ deliberate shaping of their messages and their self-presentation.

In response to the numerous ethical issues involving big data, this article positions the infrastructural dynamics of big data storage and circulation as a concern for social and environmental justice. After identifying how big data accumulate in place-based ecologies that are made vulnerable to sustain ever-increasing quantities of data, the author explains how most, if not all, digital writing practices are relationally tethered to often distant places. In response, the author argues for developing and sustaining critical infrastructure literacies where big data infrastructures are not perceived as ethereal, cloud-like entities, but as materialities with relations to place, land, water, history, climate, culture, nation, and much else. Attending to infrastructure with a cultural rhetorics orientation attentive to relationality, accountability, and story, the article details four critical practices that place digital citizens within relational matrices where they are asked to account for how data practices affect a constellation of people, places, and environments.

In this article, an analysis of multimodal representations in elementary school students’ descriptive texts about the mobile game Pokémon Go (PG) is used to discuss youths’ new literacy practices emerging from their out-of-school experiences (e.g., gaming, producing game-walkthroughs, and fan art). Social semiotic multimodal analyses of two students’ PG texts and their participation in talks around their texts are used to exemplify what occurs semiotically in the translation of meanings and designs across modes, media, and sites. Combining the analysis of the meaning potentials of multimodal representations with ethnographic accounts of their use in context produces the following findings: The PG writing task connects with the students’ life-worlds and the gaming context around PG prompts the design of their multimodal representations. The students are active creators of content and demonstrate purposeful and innovative uses of semiotic resources in self-representation, stance taking, and audience awareness. The iPad facilitates multimodal and digital crossovers between leisure activities and school subjects. The concluding discussion suggests how game-based literacy practices could be transferred into academic settings.