Artificial Intelligence

Gen AI and Student Feedback Literacy Coventry led Studentship

Coventry University

Not stated

Location
Coventry, United Kingdom
Funding
Funded PhD Project (Students Worldwide)
Application deadline
15 November 2026

About the project

About the Project Introduction This PhD project is part of the Cotutelle arrangement between Coventry University, UK and Deakin University, Melbourne, Australia. The supervision team will be drawn from the two universities. This studentship will start at Deakin University, Melbourne, Australia. Please note this project will be starting at Coventry University Whilst the motivations and behaviours that inform student GenAI use are evolving rapidly, institutional knowledge of the nature and role of these in the feedback processes is not. We have little understanding of the ‘when’, ‘why’, and ‘how’ of student engagement with GenAI to mediate learning, from tool choice, to prompt generation, to response interpretation and application, and then what relationship these behaviours have on engagement and success. We understand little of the nature of these behaviours across differently performing students, disciplines, contexts or time. This study therefore asks how student feedback literacy and feedback processes need to evolve in a context of widespread GenAI use. Project details The title is given, and research questions and methodological approaches are broadly indicated. We expect applicants to refine these. Research question: How does student feedback literacy and feedback processes need to evolve in a context of widespread GenAI use? Indicative sub-questions include: When and in what circumstances do students use GenAI for feedback purposes? Both when prompted by staff or the course design and when not; What influences lead students to use GenAI for feedback purposes, and what specific practices do students engage in?; What kinds of learning does this lead students to/ what does it lead them away from? It is anticipated that a mixed-methods, exploratory approach to design will be adopted, combining, for example only, semi-structured interviews, and digital footprints/artefacts (e.g. evidence of tool choice/usage, including samples of GenAI-generated feedback exchanges), samples of learner-instructor (human-human) feedback exchanges, curricular documentation and institutional data on student background and outcomes. University students based in Deakin University and/or Coventry University will be purposefully sampled, according to, for example, discipline, level of study, and background. Candidates are welcome to identify a particular higher education context or group of learners on which to focus, and to craft project design according to their research expertise. Tailored responses to the broad question/s laid out should be informed by theories of feedback and assessment, with attention to language. We encourage candidates from all disciplines to bring novel perspectives and methodological approaches to refining their response to the questions identified, and we expect candidates to significantly narrow the focus. Careful consideration should be given to the research expertise and contexts of the supervisory team (i.e. Prof. David Boud at CRADLE and Dr. Siân Alsop at GLEA) to ensure the viability of the proposed thesis. Note that two mirror projects will be undertaken to address to this thesis title, one based at Coventry University and one based at Deakin University. Adverts are separate. Funding Tuition fees and stipend Benefits The successful candidate will receive comprehensive research training including technical, personal and professional skills. All researchers at Coventry University (from PhD to Professor) are part of the Doctoral and Research College, which provides support with high-quality training and career development activities. This is an exciting opportunity to study a PhD as part of a cotutelle arrangement between Coventry University, UK and Deakin University, Melbourne, Australia. The PhD Student will graduate with two PhDs, one from Deakin University and one from Coventry University, each of which recognises that the program was carried out as part of a jointly supervised doctoral program. Candidate specification Applicants must meet the admission and scholarship criteria for both Coventry University and Deakin University for entry to the cotutelle programme. Applicants should have graduated within the top 15% of their undergraduate cohort. This might include a high 2:1 in a relevant discipline/subject area with a minimum 70% mark (80% for Australian graduates) in the project element or equivalent with a minimum 70% overall module average (80% for Australian graduates). A Bachelor's degree in a relevant field requiring at least four years of full-time study, and which normally includes a research component which is equivalent to at least 25% of a year’s full-time study in the fourth year, with achievement of a grade for the project equivalent to a H1 standard or 80%. OR a Masters degree, with a significant research component, in a relevant subject area, with overall mark at minimum Distinction. In addition, the mark for the Masters thesis (or equivalent) must be a minimum of 80%. Please note that where a candidate has 70-79% and can provide evidence of research experience to meet equivalency to the minimum first-class honours equivalent (80%+) additional evidence can be submitted and may include independently peer-reviewed publications, research-related awards or prizes and/or professional reports. Language proficiency (IELTS overall minimum score of 7.0 with a minimum of 6.5 in each component). The potential to engage in innovative research and to complete the PhD within a prescribed period of study. How to apply To find out more about the project, please contact b2358@coventry.ac.uk >(Director of studies at Coventry University) david.boud@deakin.edu.au )(Director of studies at Deakin University) All applications require full supporting documentation, a covering letter, plus a 2000-word supporting statement showing how the applicant’s expertise and interests are relevant to the project.

Research areas

Artificial IntelligenceHigher EducationEducation