Today marks the launch of AI.RDN+, a new £3.4m initiative led by Aston University and the University of Leeds, with partners including UKCGE, Jisc, Vitae and NCUB. The network will assess AI tools available for doctoral research and promote responsible AI uptake and innovation by researchers. Our role includes running a national network for researcher developers, conducting sector-wide consultation, and producing an insights report.
This feels like the right moment to think through what AI means for researcher development practice. These comments draw on conversations with practitioners across the sector, as well as our recent Researcher Development Benchmarking work with Research Consulting, where AI emerged as both a skills issue and a research culture issue. We’ll be exploring some of these questions further in two separate Vitae workshops in March.
Question 1: If AI fundamentally changes the nature of doctoral research, what are the implications for researcher development?
The impact of AI on doctoral research is being felt differently across disciplines, but the underlying question is the same. AI could compress PhD timelines. Researchers are using it for data analysis, literature reviews and more. But if the purpose of the PhD is partly developmental, what happens when AI does the doing? There’s a risk that we produce researchers who can use AI effectively but haven’t built the deep understanding that comes from sustained engagement with their subject. This is an issue for all employers of researchers.
For researcher developers, this raises questions about whether and how we need to deliberately create developmental opportunities that might previously have emerged naturally from the research process. If AI assists with analysis, how do we ensure researchers develop critical thinking capabilities? What experiential learning opportunities need to be explicitly designed rather than assumed to emerge from research tasks?
Question 2: How are researcher developers using AI?
This question is less well understood, with a patchwork of approaches and much uncertainty across the sector. Many institutions are starting to use AI in their own practice, primarily to save time and expand capacity. This includes drafting workshop materials that can then be tailored for specific disciplines, accelerating administrative tasks, and updating content quickly. One institution we know of is experimenting with AI-generated video content that can be rapidly updated when needed, recognising the importance of keeping pace with change while maintaining engagement.
Some researcher developers and careers professionals are experimenting with custom GPTs, with potential to free up human advisers for more complex cases and in-depth guidance, or to provide institution-specific Q&A. But the critical questions haven’t been answered yet. How do we maintain quality control when AI is providing careers advice? What’s the ethical position? How do we find that balance between innovation and responsibility? And what might the implications be for the mental health and wellbeing of researchers?
Question 3: What does the RDF look like through an AI lens?
The RDF refresh considered AI and other emerging technologies, but it’s worth thinking about the wider transformation. If the principle is that researchers should remain the expert, with AI enhancing thinking rather than replacing it, what does this mean for the capabilities we prioritise in researcher development? Using AI in a critical and ethical way while maintaining ownership and academic integrity requires effective prompting techniques and critical evaluation of every AI-generated insight or suggestion.
In the age of AI, some of the most important descriptors might be reflexive practice, critical thinking and analysis, research integrity and ethics, and development-focused practice. How might these play out at phase 1 level:
- Reflexive practice: recognising that AI tools embed particular perspectives from their training data, being aware that this might limit originality, starting to think about what alternatives it might be missing
- Critical thinking and analysis: developing the ability to validate AI-generated analysis independently, beginning to identify when it has misidentified patterns or missed context
- Research integrity and ethics: seeking guidance on policies for AI use, understanding how to attribute AI assistance transparently, recognising the difference between AI as a tool and AI as an unacknowledged co-author
- Development-focused practice: recognising the need for ongoing AI literacy development, identifying which AI skills are relevant to career goals, building awareness of how AI might affect career trajectories
AI.RDN+ will help us develop a clearer sense of what the researcher development community needs to be thinking about as AI transforms the knowledge, skills and capabilities required of effective researchers. We’ll be feeding this back through sector-facing outputs.
Get involved
Watch this space for opportunities to participate in the AI.RDN+ researcher developer network and the sector-wide survey. We want to amplify the breadth of experience across the sector.
In the meantime, interested in learning more or exploring these questions in further depth? We’re running two workshops in March that explore AI across the research lifecycle and how to use it effectively in your own practice – details and sign-up on the Vitae website.
Author: Dr Yolana Pringle