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Persuasive arguments attribute academics' persistent struggles in making time for writing to the increasing demands of a marketised sector on the academic role (Dickson-Swift et al. 2009; Macleod, Steckley, and Murray 2012). Whilst a significant body of literature promotes different writing interventions as potential solutions, the challenge of building and sustaining a culture in which their positive outcomes are maintained remains. In this paper, I explore the practices of academics writing for publication purposes. Drawing on the concept of academic writing as 'identity work (French 2020), I demonstrate that writing practices are entwined with wider academic and institutional identities, which either work for or against building sustainable writing cultures. I argue for a methodological shift in how writing initiatives are researched, drawing a distinction between my ethnographic case study of one writing group and others that focus either on retrospective accounts or analysing correlations between writing groups and productivity. Specifically, I argue for a focus on the ongoing process of becoming a writer rather than on its production. The study builds on existing literature to explain why writing groups are experienced as valuable to participants. It argues that they offer more than simply protected space for writing; they reframe participants' understandings of themselves and their academic identities and reintegrate research writing with other aspects of their role. This identity work occurs within visible, protected spaces for writing, where participants work both individually and in community, and reflection and dialogue are central. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) has increasingly influenced higher education, notably in academic writing where AI-powered assisting tools offer both opportunities and challenges. Recently, the rapid growth of generative AI (GAI) has brought its impacts into sharper focus, yet the dynamics of its utilisation in academic writing remain largely unexplored. This paper focuses on examining the nature of human-AI interactions in academic writing, specifically investigating the strategies doctoral students employ when collaborating with a GAI-powered assisting tool. This study involves 626 recorded activities on how ten doctoral students interact with GAI-powered assisting tool during academic writing. AI-driven learning analytics approach was adopted for three layered analyses: (1) data pre-processing and analysis with quantitative content analysis, (2) sequence analysis with Hidden Markov Model (HMM) and hierarchical sequence clustering, and (3) pattern analysis with process mining. Findings indicate that doctoral students engaging in iterative, highly interactive processes with the GAI-powered assisting tool generally achieve better performance in the writing task. In contrast, those who use GAI merely as a supplementary information source, maintaining a linear writing approach, tend to get lower writing performance. This study points to the need for further investigations into human-AI collaboration in learning in higher education, with implications for tailored educational strategies and solutions. [ABSTRACT FROM AUTHOR]