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Artificial intelligence (AI) has become increasingly important and influential in reading and writing. The influx of social media digital spaces, like TikTok, has also shifted the ways multimodal composition takes place alongside AI. This study aims to argue that within spaces like TikTok, human composers must attend to the ways they write for, with and against the AI-powered algorithm.

Data collection was drawn from a larger study on #BookTok (the TikTok subcommunity for readers) that included semi-structured interviews including watching and reflecting on a TikTok they created. The authors grounded this study in critical posthumanist literacies to analyze and open code five #BookTok content creators’ interview transcripts. Using axial coding, authors collaboratively determined three overarching and entangled themes: writing for, with and against.

Findings highlight the nuanced ways #BookTokers consider the AI algorithm in their compositional choices, namely, in the ways how they want to disseminate their videos to a larger audience or more niche-focused community. Throughout the interviews, participants revealed how the AI algorithm was situated differently as both audience member, co-author and censor.

This study is grounded in critical posthumanist literacies and explores composition as a joint accomplishment between humans and machines. The authors argued that it is necessary to expand our human-centered notions of what it means to write for an audience, to co-author and to resist censorship or gatekeeping.

The purpose of this paper is to share findings from empirically driven conceptual research into the implications for English teachers of understanding generative AI as a “tool” for writing.

The paper reports early findings from an Australian National Survey of English teachers and interrogates the notion of the AI writer as “tool” through intersectional feminist discursive-material analysis of the metaphorical entailments of the term.

Through this work, the authors have developed the concept of “coloniser tool-thinking” and juxtaposed it with First Nations and feminist understandings of “tools” and “objects” to demonstrate risks to the pursuit of social and planetary justice through understanding generative AI as a tool for English teachers and students.

Bringing together white and First Nations English researchers in dialogue, the paper contributes a unique perspective to challenge widespread and common-sense use of “tool” for generative AI services.

This paper showcases how the PAR embedded in posthumanist perspectives enabled us to navigate several complexities in the field through methodological situatedness and pluralism. It also attempts to critically outline the drivers and barriers that shaped our capacities to engage with the PAR.

The Tamil Nadu state in the Bay of Bengal along the southeast coast of India is one of the six regions in the world where severe tropical cyclones originate throughout the year. Storm surges in this region are well known for their destructive potential due to strong winds and heavy rainfall. This paper describes our participatory action research (PAR) journey towards strengthening grassroots action by providing access to safe and affordable housing for cyclone-impacted households (CIHs) in the Villupuram district of Tamil Nadu, India. The PAR was guided by an adaptive innovation model (AIM) that draws inspiration from posthumanism, action research and reflective practice traditions.

The insights from the PAR insist that we must recognise and work with diverse knowledge systems and situated practices to develop meaningful disaster risk reduction (DRR) and climate adaptation strategies. Our approach has to be rooted in the lived experiences of various vulnerable groups, their entanglements with nature and their everyday struggles of interacting with a complex social-ecological system.

This paper is an outcome of a PAR in a cyclone-impacted village in Tamil Nadu, India. The discussions and findings of the paper are original in nature and have not been published elsewhere.

This study builds on previous theoretical work that considered artificial intelligence (AI) and its potential for creating “teacher-centaurs” whose labor could be accelerated through the use of generative AI (Fassbender, in review). The purpose of this paper is to use empirical methods to study centaur teachers and the division of labor (Durkheim, 1893/2013) that arise from outsourcing teaching tasks to AI.

Multiple case study (Stake, 2006) was used to collect data on two secondary English teachers who were early adopters of generative AI. Data included semi-structured interviews as well as ChatGPT chat logs, which helped in describing how teaching approaches evolved using AI technology.

Results showed that teachers used AI for planning, instruction and assessment. AI-augmented teaching practices allowed teachers to complete tasks with greater speed, which in turn increased stamina and short-term work–life balance. Given the novelty of AI, concerns about data privacy and academic integrity raised ethical questions.

ChatGPT’s rise to popularity in 2023 brought with it significant discussions about education, specifically how students would use AI primarily as a tool for plagiarism. This study takes a different focus, considering how early adoption of AI has begun changing teacher labor, offering implications for the future of the teaching profession.