Discourse and Writing/Rédactologie  Volume 35, 2025  http://journals.sfu.ca/dwr  57   Special Issue Editorial  Introduction: The Present and Future(s) of Writing in the Age of Artificial Intelligence/Le Présent et le(s) Futur(s) de la Rédaction à l  ère de l  Intelligence Artificielle    Erin Vearncombe1  Assistant Professor, Teaching Stream, Institute for the Study of University Pedagogy, University of Toronto Mississauga    Cara Violini2  Writing Specialist, Write Site, Athabasca University    Introduction In the three years since the release of ChatGPT in November 2022, countless academic articles have  opened  with  that  very  (or  a  very  similar)  phrase,  marking  a  transitional  moment  for  the  study  of  writing.  The  proliferation  of    Since  the  release  of  ChatGPT     openers  is  indicative  of  the  real  disruption caused by generative AI (GenAI) in academic contexts and, we suggest, a tendency to focus  on technological response (how do we detect AI use? Do we ban AI tools? Seek to integrate them into  teaching  and  learning?  Are  Large  Language  Models  (LLMs)  drafting  tools?  Search  engines?  etc.)  rather than writing itself. This special issue of Discourse and Writing/Rédactologie recenters writing  in  these  conversations,  thinking  through  what  writing  is,  what  it  does,  and  why  and  how  writing  matters, in the age of artiNicial intelligence.  The  articles  in  this  issue  examine  the  implications  of  GenAI  tools  across  diverse  teaching  and  learning  contexts,  disciplinary  situations,  and  pedagogical  frameworks.  Read  together,  however,  their diversity converges around a shared insight into writing as a relational act.   Writing matters,    contributors Thieme and Amell straightforwardly assert. Writing matters, and continues to matter,  as we negotiate extremes of AI-interaction, from avoidance to adoption (to reference Saunders and   Discourse and Writing/Rédactologie  Volume 35, 2025  http://journals.sfu.ca/dwr  58   Boldt  s  framework),  and  from    hype    to    grave  worry    (Thieme  &amp;  Amell).  Writing  continues  to  matter, but it will continue to matter more, our contributors collectively suggest, when it is privileged  as a site of relationship. These articles move away from thinking about writing as a skill towards  writing  as  a  process  that  works  to  enact  relationship,  specifically  in  the  context  of  knowledge  construction. The increasing integration of GenAI tools into the relational spaces of writing amplifies  previously existing challenges around the teaching and learning of writing (Eaton et al.), making us  reconsider the humanness of knowledge-making; a humanness that has always been foundational,  but  may  have  perhaps  become  taken  for  granted.  Our  authors  help  us  to  think  through  essential  questions  of  embodied  attention  (Banting;  Veselka  et  al.),  situated  intention  (Thieme  &amp;  Amell),  community recognition (Saunders &amp; Boldt; Li &amp; Lin), and dialogic integrity and trust (Eaton et al.;  Thieme &amp; Amell).  The aim of our call for papers was to solicit research into the impact that technologies based on  artificial intelligence are having and will have on academic and professional writing, its teaching and  practice. We sought empirical and theoretical work that would explore the appropriation of GenAI  tools in academic contexts; the re-conception of writing-related identities (teacher, learner, member  of a given discourse community); writing in and across genres; ethics, plagiarism and postplagiarism  (see Eaton, 2023); writing processes and practices; and translingual writing strategies, to list a few  categories from that initial call. Certainly, the papers collected in this issue speak explicitly to these  categories  and  to  prevalent  questions  about  efficiencies  versus  efficacies.  They  also  speak  to  the  multiple ways in which GenAI  s entry into academic writing contexts both reveals and threatens the  relational foundations of knowledge-making. Our contributors invite us to consider epistemological  questions  about  how  knowledge  comes  into  being  through  human  interaction  and  how  we  might  (and should) reassess contemporary advancements of relational epistemologies (see, for example,  Bearman &amp; Ajjawi, 2023; Markauskaite et al., 2022).   Thinking through Writing in the Age of Artificial Intelligence In order to point to the distinctly human value of attention in contrast to the outputs of GenAI, poet  Meghan O  Rourke, in a July 2025 guest essay for The New York Times, referenced a stanza from the  Mary Oliver poem   Sometimes  :       Instructions for living a life:      Pay attention.   Discourse and Writing/Rédactologie  Volume 35, 2025  http://journals.sfu.ca/dwr    Be astonished.      59   Tell about it.   (Oliver, 2008).     It is through paying attention, O  Rourke suggests, that we become human, or enact our humanity;  attention is the headwater of individuality, and in writing about the objects of our attention, writers  expand our human   sense of what it means to be alive   (O  Rourke, 2025).  LLMs cannot pay attention, of course, either to the casual, small astonishments of human life or to  the interpretation of a Mary Oliver poem; as Banting reminds us in this issue, LLMs   can no more  close-read than they can read at all.   The point here is not simply to assert,   GenAI cannot do what  humans can do.   Rather, our focus is on what human work remains essential to our writing processes  across  contexts  and  disciplines.  As  contributors  Thieme  and  Amell  write,    the  social  nature  of  research means that awareness of our relations    to other researchers, to community members, to  larger audiences    matters to the quality of our research and writing.   In writing contexts mediated  by  GenAI  (whether  we  are  aware  of  that  mediation  or  not),  how  do  we  maintain  space  for  the  relational  work  of  paying  attention  and  telling  about  it  that  is  essential  to  academic  knowledgemaking?  This issue is published in the midst of scholarly debate about relational epistemology, a view of  knowledge-creation  as  an  interleaving  of  human  and  non-human    actors  .  Bearman  and  Ajjawi  (2023)  offer  the  following  deNinition  of  relational  epistemology:    knowledge  is  not  a  singular  property of an individual or a machine but is contextualised within particular relationships between  people, things and spaces.   Bearman and Ajjawi (2023) build explicitly on Johnson and Verdicchio  s  (2017)  articulation  of  AI  systems  as    sociotechnical  ensembles   combinations  of  artefacts,  human  behaviour, social arrangements and meaning.   Within this framework, GenAI is not a tool introduced  into  writing  practices  and  processes,  but  an  agent  (or  agentive  actor)  reconNiguring  primary  relational  dynamics  through  which  knowledge-making  occurs.  This  understanding  of  relational  knowledge-making resonates with Eaton  s (2023) advancement of   hybrid writing   under conditions  of    postplagiarism.    Eaton  (2023)  moves  us  beyond  binary  thinking  about  human-  versus  AIgenerated text (and all of the detective work and gotcha! moments we devote to distinguishing the  two), asserting,     Discourse and Writing/Rédactologie  Volume 35, 2025  http://journals.sfu.ca/dwr  60    hybrid writing co-created by human and artiNicial intelligence is becoming prevalent      and will soon become the norm. Text generated by artiNicial intelligence tools is not static.      It can be edited, revised, reworked, and remixed. The result can be a product that is neither   fully written by a human, nor by an AI, but one that is hybrid. Trying to determine where the human  ends and where the artiNicial intelligence begins is pointless.  The suggestion, it seems, is that we adapt to these conditions of hybrid knowledge-production,  accepting  that  the  wires  of  human  and  machine  cannot  be  disentangled  (to  extend  the  metaphor  further: we need not be concerned about whether the blue or red wire needs to be cut, as there are  no real stakes attached to cutting either wire).    Relationships  between  people    is  privileged  in  Bearman  and  Ajjawi  s  deNinition  (though  not  necessarily  with  the  purposefulness  we  suggest  is  essential),  and  the  contributions  in  this  issue  indicate  that  these  human  relationships  must  continue  to  be  privileged.  Similarly,  while  hybrid  authorship may not dissolve the interpersonal, human-relational foundations of knowledge-making,  these hybrid conditions, our authors argue, do require us to be more intentional about cultivating and  protecting these foundations. We know that writing in academic contexts is not the encoding of preexisting knowledge into a text. Writing is a knowledge-making activity (Estrem, 2016); it is a process  through which knowledge comes into being through a writer  s engagement with sources and genres,  the dialogic relationship between writer and audience, the writer  s positioning on the threshold of or  within (or against!) a given discourse community, the writer  s processes of drafting, seeking feedback,  and revising. Between the lines of these engagements is relationship: writing enacts relationships  between the writer and their subject matter (a way we might conceive of intent), writer and reader,  writer and discourse community, writer and disciplinary conventions.  Each of our contributors attends, in their own ways, to the conditions under which knowledgemaking should remain humanly relational. If knowledge is   held between actors   (Bearman &amp; Ajjawi,  2023), we have to think about which actors are in view, in what relationships, doing what kind of  work? Which relationships are sustained, and how are they enacted? For example, a graduate student  using  GenAI  to  reNine  their  language  while  maintaining  intellectual  ownership  of  their  ideas,  who  critically  evaluates  AI  suggestions  against  disciplinary  norms  in  conversation  with  peers  and  instructors,  seeks  active  relationship  with  knowledge-making  processes  and  the  discourse  community they seek to join (Li &amp; Lin). Conversely, a learner who ofNloads all their intellectual and  rhetorical labour to GenAI, who submits text without engaging its content, without intention apart  from the receipt of a grade, opts out of the relational work through which knowledge is built; the   Discourse and Writing/Rédactologie  Volume 35, 2025  http://journals.sfu.ca/dwr  61   reader cannot authentically respond without the relationality of intention and trust (Veselka et al.;  Thieme &amp; Amell).   The contributions to this special issue invite us to pay attention to the relationships that matter  within academic contexts of knowledge-production. They also reveal what is threatened when these  relationships are disrupted or displaced by AI-mediated interactions that signal without substance,  offering what Banting calls   ritual worship in the absence of belief.   If knowledge-making depends  upon human actors engaging intentionally with each other, whether that engagement is in the form  of texts, disciplinary conventions, peers, instructors, tutors, etc., this work cannot be offloaded to AI  without fundamentally changing what we mean by knowledge, and most specifically, writing as a  knowledge-making act: that essential human act of paying attention, being astonished, and telling  about it.   Overview of the Special Issue   Simulated Social Action: Assessing Generative AI  s Attempts at the Rhetoric of Literary Criticism    Sarah Banting, Mount Royal University  Banting  s article examines whether large language models (LLMs) can produce a professional piece  of  literary  criticism  by  comparing  LLM  outputs  to  the  characteristic  rhetorical  moves  of  research  articles in English studies. While LLMs successfully signal genre, ultimately, they fail to perform the  social actions that constitute literary criticism as a form of humanistic knowledge-making. LLMs can  recirculate linguistic patterns but cannot participate in the dialogic, motivated engagement through  which knowledge emerges in literary studies.      Where We  re At, What We Must Know, and Where We Can Go: A Systematic Review of Research about  Writing and ArtiPicial Intelligence    Christopher Eaton, Isabella Belmonte, Talla Enaya, Sarah Flood, Zainab Khalil, Anthony Makwanda,  Mian  Muhammad  Ahmed  Shah,  Alexia  Toma,  Tiffany  Wang,  Connor  Yu,  University  of  Toronto  Mississauga  Eaton et al. develop a thematic map of early research conversations on artiNicial intelligence, writing,  and writing pedagogies in order to point to important next steps as the scholarly record on AI and  writing  develops.  This  collaborative  review  by  a  university  instructor  and  nine  undergraduate  students identiNies Nive prominent themes: AI literacy frameworks, evaluating AI outputs, human-AI   Discourse and Writing/Rédactologie  Volume 35, 2025  http://journals.sfu.ca/dwr  62   rhetorical collaborations, AI and bias, and academic integrity. Analysis reveals signiNicant gaps in the  current scholarly record, including the need for empirical data on how students evaluate AI outputs  in different disciplinary contexts, more nuanced understanding of human-AI rhetorical collaboration  beyond  efNiciency  metrics,  and  consolidated  approaches  to  AI  literacy  that  build  on  existing  pedagogical knowledge. The review concludes that while conceptual frameworks have proliferated  rapidly, the Nield lacks sufNicient empirical evidence about how these frameworks function in practice  and  how  students  actually  engage  with  AI  tools  in  their  writing  processes.  This  snapshot  of  early  scholarship  provides  a  foundation  for  more  focused,  practice-based  research  as  the  conversation  develops.      Doctoral  Students    Use  of  Generative  ArtiPicial  Intelligence  (GenAI)  in  Academic  Writing:  Their  Engagement with AI-Powered Writing Tools    Vickie W. K. Li , The Centre for Applied English Studies, The University of Hong Kong, &amp; Linda H.F.  Lin, English Language Centre, The Hong Kong Polytechnic University  Research  on  postgraduates,  especially  EAL  doctoral  students    engagement  with  GenAI  remains  relatively  limited;  yet,  these  students  are  expected  to  demonstrate  a  higher  level  of  writing  proNiciency to effectively disseminate their research within the academic community. To address this  gap,  Li  and  Lin  s  study  examines  how  EAL  PhD  students  engage  with  GenAI  tools  behaviourally,  cognitively,  and  affectively  in  a  thesis-writing  course.  This  study  has  signiNicant  pedagogical  implications regarding: i) the support needed by EAL PhD students to effectively use GenAI in their  writing; and ii) possible ways to integrate GenAI technology into academic writing courses.       Avoid, Adopt, Adapt: Positions on GenAI in Canadian Writing Centres    Gillian Saunders &amp; Natalie Boldt, University of Victoria   Saunders  and  Boldt  report  on  an  environmental  scan  of  31  Canadian  university  writing  centre  websites  to  assess  how  these  critical  support  spaces  are  publicly  responding  to  generative  AI  in  student writing. The analysis reveals a fragmented landscape: as of summer 2024, only three writing  centres had explicit public statements on GenAI use (one taking an   avoid   position, two an   adopt  ),  while 47% offered no public GenAI-related resources at all. This silence is particularly concerning  given that writing centres traditionally serve as spaces where students negotiate scholarly identities  and try on disciplinary discourses. The authors argue that writing centres' omission from broader   Discourse and Writing/Rédactologie  Volume 35, 2025  http://journals.sfu.ca/dwr  63   institutional  AI  conversations  represents  a  missed  opportunity,  as  writing  centres  are  ideally  positioned to help students develop critical GenAI literacy while maintaining the relational, processoriented approaches central to writing development.       Generative AI and Socio-Rhetorical Views of Writing    Katja Thieme, University of British Columbia &amp; Brittany Amell, Carleton University  Thieme  and  Amell  argue  that  conversations  about  generative  AI  and  writing  instruction  must  be  grounded  in  core  disciplinary  insights  from  writing  studies,  particularly  concepts  of  intention,  process,  and  trust.  Drawing  on  rhetorical  genre  theory,  the  authors  demonstrate  that  academic  writing  enacts  relationships  through  which  students  develop  contextual  awareness,  agency,  and  authentic  positionality  relative  to  a  given  discourse  community.  The  article  examines  how  AIgenerated text disrupts these relational dynamics as AI lacks genuine communicative intent, making  it unclear whose intentions are being recognized when students submit AI-assisted work. Similarly,  AI cannot participate in the trust-building networks essential to academic knowledge-making. The  authors  contend  that  pedagogical  decisions  about  AI  integration  must  prioritize  maintaining  the  conditions  under  which  writing  can  remain  a  social,  dialogic  practice  through  which  knowledge  emerges.      Always  Watched,  Always  Watching:  The  Role  of  Surveillance  in  Writing  Educator  Experiences  with  Generative ArtiPicial Intelligence    Mercedes Veselka, York University, Lorelei Lingard, Western University, Kathryn Hibbert, Western  University &amp; Mary Ott, York University  Veselka et al. examine how generative AI is reshaping teacher identities and pedagogical agency in  writing  instruction,  focusing  particularly  on  the  pressures  of  surveillance,  understood  through  Foucault  s metaphor of the panopticon, in AI-mediated contexts. Drawing on narrative inquiry with  two writing instructors, the study indicates that generative AI ampliNies existing power dynamics in  teaching and learning as well as institutional constraints, creating conditions where educators must  navigate  conNlicting  expectations  between  fostering  genuine  learning  relationships  and  enforcing  compliance.  The  authors  argue  that  meaningful  pedagogical  responses  to  AI  require  institutional  environments  that  prioritize  collaborative  learning  processes  over  punitive  surveillance,  creating   Discourse and Writing/Rédactologie  Volume 35, 2025  http://journals.sfu.ca/dwr  64   space  for  teachers  and  students  to  negotiate  roles  and  explore  uncertainties  together  rather  than  positioning educators as watchful gatekeepers of   authentic   work.   Final Words We, the editors of this issue, would like to extend our thanks to the contributors of this volume, who  turned their work around in record time in order to publish as soon as possible. We would like to  express  our  gratitude  to  the  excellent  team  of  reviewers  who  gave  us  their  time  and  constructive  feedback. Thank you to the Journal Editors, Jordana Garbati, Taylor Morphett, Vale  rie Delavigne, and  Sara Vecchiato for your continuing conversation and support. Finally, the editors would like to express  our  gratitude  for  the  opportunity  to  work  together,  as  we  have  really  enjoyed  working  with  one  another throughout the compilation of this special issue.    Endnotes 1. Correspondence may be addressed to erin.vearncombe@utoronto.ca.   2. Correspondence may be addressed to cviolini@athabascau.ca.    References Bearman and Ajjawi. (2023). Learning to work with the black box: Pedagogy for a world with artiNicial  intelligence. British Journal of Educational Technology, 54, 1160  1173.   https://doi.org/10.1111/bjet.13337   Eaton,  S.E.  (2023).  Postplagiarism:  transdisciplinary  ethics  and  integrity  in  the  age  of  artiNicial  intelligence  and  neurotechnology.  International  Journal  for  Educational  Integrity  19(1),  1      10.  https://doi.org/10.1007/s40979-023-00144-1  Estrem, H. (2015). Writing is a knowledge-making activity. In L. Adler-Kassner &amp; E. Wardle (Eds.),  Naming What We Know: Threshold Concepts of Writing Studies (pp. 19    20). Utah State University  Press. https://doi-org.myaccess.library.utoronto.ca/10.7330/9780874219906   Johnson, D.G. &amp; Verdicchio, M. (2017). Reframing AI discourse. Minds and Machines, 27(4), 575    590.  https://doi.org/10.1007/s11023-017-9417-6   Markauskaite, L., Marrone, R., Poquet, O., Knight, S., Martinez- Maldonado, R., Howard, S., Tondeur, J.,  De Laat, M., Buckingham Shum, S., Gas  evic  , D., &amp; Siemens, G. (2022). Rethinking the entwinement   Discourse and Writing/Rédactologie  Volume 35, 2025  http://journals.sfu.ca/dwr  65   between artiNicial intelligence and human learning: What capabilities do learners need for a world  with AI? Computers and Education: ArtiPicial Intelligence, 3, 100056.   https://doi.org/10.1016/j.caeai.2022.100056   Oliver, M. (2008). Red bird: Poems. Beacon Press.  O  Rourke, M. (2025, July 18). I teach creative writing. This is what A.I. is doing to students. The New  York Times. https://www.nytimes.com/2025/07/18/opinion/ai-chatgpt-school.html

## Références

and Ajjawi. (2023). Learning to work with the black box: Pedagogy for a world with artiNicial intelligence. British Journal of Educational Technology, 54, 1160–1173. https://doi.org/10.1111/bjet.13337

S.E. (2023). Postplagiarism: transdisciplinary ethics and integrity in the age of artiNicial intelligence and neurotechnology. International Journal for Educational Integrity 19(1), 1 – 10. https://doi.org/10.1007/s40979-023-00144-1

Estrem, H. (2015). Writing is a knowledge-making activity. In L. Adler-Kassner & E. Wardle (Eds.), Naming What We Know: Threshold Concepts of Writing Studies (pp. 19 – 20). Utah State University Press. https://doi-org.myaccess.library.utoronto.ca/10.7330/9780874219906

D.G. & Verdicchio, M. (2017). Reframing AI discourse. Minds and Machines, 27(4), 575 – 590. https://doi.org/10.1007/s11023-017-9417-6

L., Marrone, R., Poquet, O., Knight, S., Martinez- Maldonado, R., Howard, S., Tondeur, J., De Laat, M., Buckingham Shum, S., Gaš ević , D., & Siemens, G. (2022). Rethinking the entwinement between artiNicial intelligence and human learning: What capabilities do learners need for a world with AI? Computers and Education: ArtiPicial Intelligence, 3, 100056. https://doi.org/10.1016/j.caeai.2022.100056

Oliver, M. (2008). Red bird: Poems. Beacon Press.

O’Rourke, M. (2025, July 18). I teach creative writing. This is what A.I. is doing to students. The New York Times. https://www.nytimes.com/2025/07/18/opinion/ai-chatgpt-school.html