AI in Research Writing: Insights and Recommendations for Course Design
Conducted by Diana Akhmedjanova, Svetlana Suchkova and Natalia Zharkova, the study explores the experiences and perceptions of university instructors who use AI tools to support their research writing and publication practices. The authors analysed data from two one-day immersion courses involving the use of AI tools, which were delivered through the Academic Writing Centre (AWC). Their findings were published in the article ‘Teaching Research Writing with AI: A Case Study of Academic Development Courses in Higher Education’ and directly informed the redesign of the AWC’s professional development offerings.
Data comprised post course trainee feedback covering course usefulness, content, assignments, activities, instructor performance and feedback, and self reported involvement, as well as semi-structured interviews with instructors about teaching and writing with AI tools.
The results point to a high level of enthusiasm—paired with thoughtful caution. Participants rated the courses extremely highly, with average scores of 9.4 and 9.5 out of 10, and many said they would readily recommend them to colleagues. Faculty members particularly valued the hands-on format, practical assignments, and detailed feedback from instructors.
For many participants, the courses opened up new possibilities. They reported discovering AI tools they had not previously used and learning concrete strategies for tasks such as conducting literature reviews, refining research questions, revising drafts, and selecting appropriate journals. As one participant noted,
I realised that a literature review can be done in one day by combining different tools.
At the same time, interviews with instructors revealed a measured and reflective approach to AI use. Most described relying on AI primarily for literature searches, language checking, and data transcription, while remaining cautious about more advanced forms of text generation. Ethical concerns were a recurring theme: how AI systems process uploaded texts, how paraphrasing tools operate, and where the boundaries of acceptable academic practice lie.
How ethical is it to use AI technologies for academic purposes? How does the system use the text we upload to paraphrase or edit?
The study highlights the growing role of writing centres as key actors in shaping AI literacy in higher education. As AI continues to evolve and new tools emerge at a rapid pace, writing centres offering AI-focused courses need to stay informed about recent developments and the functionality of AI resources. At the same time, they should broaden their focus beyond students and systematically examine faculty experiences with AI to strengthen course design and better respond to the needs of instructors and researchers.
Drawing on the experiences of course participants and instructors, this study proposes a set of practical recommendations for designing AI-supported writing courses. The authors emphasise the advantages of practice-oriented formats that allow participants to experiment with AI tools and apply them to their own research tasks, rather than merely demonstrating tool functionality. The findings have already had a direct impact on the HSE Academic Writing Centre, with several courses being redesigned to include a stronger focus on AI ethics and literacy, extended formats, and continued emphasis on hands-on learning.
However, a course designer should consider such factors as (1) trainees’ previous experience, (2) a careful selection of AI tools and easy access to them, (3) time for familiarization with the tools and completing tasks, (4) classroom management, especially in groups with various levels of AI literacy, and (5) goal setting to ensure a sense of achievement in order to develop effective courses.
The scholars hope that future research will focus on the long-term effects of AI writing courses, how sustainable tool use is, and how effective different instructional formats are. They also highlight the importance of studying how AI literacy develops among different groups of academics, and the impact of AI-assisted feedback on writers' voice and critical thinking.