We know students are using generative AI (GenAI) at unprecedented rates—92% of UK undergraduates report incorporating it into their academic work, up from 66% the previous year (Freeman, 2025), while global surveys indicate 86% overall usage, with 54% employing it weekly (Digital Education Council, 2024). We know a vibrant resistance movement persists within writing studies and beyond, advocating against GenAI’s encroachment on cognitive rigor, originality, and ethical authorship (Sano-Franchini et al., 2024; Eaton, 2023). And we know that outside our field, researchers in cognitive science, neuroscience, labor economics, and human-computer interaction are producing a torrent of studies on GenAI’s effects on reasoning, creativity, mental effort, agency, and occupational futures (Lee et al., 2025; Anderson & Rainie, 2022; Gimbel et al., 2025).
As an evidence-based community, writing studies must claim the center of this conversation by rigorously investigating how GenAI reshapes composing, literacy, and human expression. This section invites contributions—especially literature reviews and syntheses—from scholars mapping these impacts, translating findings across disciplines for students, teachers, and knowledge workers. Articles should synthesize emerging empirical work on GenAI’s role in cognition, creativity, learning, agency, and reasoning, highlighting both affordances and risks to human-centered writing practices.
Researchers across disciplines are advancing vibrant scholarly dialogues on GenAI’s implications for writing and cognition. In cognitive science and education, studies reveal GenAI’s dual effects: it streamlines drafting and ideation but risks “cognitive debt” through reduced neural engagement and overreliance, potentially eroding independent thinking (Kosmyna et al., 2025; David et al., 2024). Literature reviews in human-computer interaction underscore shifts in creative processes, where GenAI augments idea generation yet homogenizes outputs, challenging authorship and stylistic voice (Kauttonen et al., 2024; Gero & Chilton, 2019). Ethical and philosophical inquiries, drawing from writing studies traditions, interrogate agency amid superintelligence forecasts, emphasizing transparency frameworks like aiTARAS to safeguard integrity (Bozkurt, 2024; Bostrom, 2014). These perspectives converge to reframe literacy not as solitary invention but as mediated collaboration, demanding interdisciplinary evidence to guide pedagogy and policy.
Major Research Questions
Central questions from these dialogues include:
- Human-AI Collaboration in Academic Writing
- How do students and researchers interact with GenAI tools during composing, and what patterns emerge in hybrid workflows (Kauttonen et al., 2024)?
- How does GenAI affect writing quality, originality, and integrity, including risks of plagiarism or bias (Bozkurt, 2024; Ward et al., 2024)?
- What is the value of unassisted writing, and under what conditions should GenAI be deployed or restricted (Eaton, 2023)?
- Impact on Education, Writing, and Human Agency
- How does GenAI influence critical thinking, creativity, and skill development in higher education (Lee et al., 2025; Freeman, 2025)?
- What benefits and drawbacks arise from integrating GenAI into classrooms, including potential deskilling (Digital Education Council, 2024; Kosmyna et al., 2025)?
- How does GenAI reliance diminish or bolster human agency, especially as models approach superintelligence (Anderson & Rainie, 2022; Bostrom, 2014)?
- Ethical Considerations and Societal Impact
- How can transparency, fairness, and accountability be ensured in GenAI use for research and education (Bozkurt, 2024)?
- What ethical challenges does AI-generated content pose for authorship, intellectual property, and academic integrity (Eaton, 2023; Moxley, 2025a)?
- How do biases, misinformation, privacy risks, and labor displacements manifest in GenAI applications (Gimbel et al., 2025; Bender et al., 2021)?
- Challenges to Traditional Notions of Composing
- How does GenAI alter creative processes and originality in writing (Gero & Chilton, 2019; Begum, 2025)?
- How should we redefine literacy and skills when GenAI produces human-like text (Ong, 2002; Moxley, n.d.)?
- What role remains for human writers amid autonomous AI composition, and how might superintelligence reshape education and knowledge work (Bostrom, 2014; Hsu, 2025)?
Research Methodologies
Scholars draw on diverse methods to probe these questions, rooted in varied epistemological traditions. We encourage submissions employing any rigorous approach, including those bridging writing studies with adjacent fields.
- Qualitative Research Methods
- Interviews and focus groups: Capturing experiences with GenAI in composing (e.g., autoethnographies of partnership stages; Kauttonen et al., 2024).
- Ethnographic studies: Observing AI integration in classrooms or workplaces (Barrett & Pack, 2023).
- Case studies: Analyzing specific GenAI impacts on writing outcomes (Kosmyna et al., 2025).
- Quantitative Research Methods
- Surveys and questionnaires: Gauging usage prevalence and attitudes (Freeman, 2025; Digital Education Council, 2024).
- Experimental studies: Testing effects on originality or cognitive load (Lee et al., 2025; Ward et al., 2024).
- Statistical analysis: Identifying trends in large datasets (David et al., 2024).
- Textual Analysis
- Content analysis: Evaluating AI-generated texts for coherence and bias (Bender et al., 2021).
- Discourse analysis: Probing language patterns in human-AI hybrids (Vee, 2023).
- Mixed Methods Research
- Integrating qualitative insights with quantitative metrics for holistic views (e.g., surveys plus neural imaging; Kosmyna et al., 2025).
- Philosophical and Ethical Inquiry
- Normative ethics: Debating authorship via theories of creativity (Elbow, 1983; Moxley, 2025b).
- Epistemological studies: Examining knowledge production in AI eras (Ong, 2002).
- Critical theories: Critiquing power dynamics and inequities (Sano-Franchini, 2025).
