A new interactive training programme focused on using large language models (LLMs) for textual analysis has been created by Sarah Jenner (Lecturer in Child and Adolescent Health) and Professor Mary Barker (Deputy Director of the IfLS) with support from the and in collaboration and co-design with industry-leader .
The forward-thinking training, led by research expert (Research Manager at Ipsos UK), is grounded in the ethical, methodological, and philosophical considerations of using artificial intelligence in research. Attendees have benefitted from specialist guidance and expertise to explore how LLMs can support qualitative and mixed-method analysis to summarise the various themes of texts.
Over 40 academics, from postgraduate researchers to professors, were able to apply the methods that they had learned to analyse their own text-based content through a combination of practical exercises and collaborative discussions led by Sarah, alongside Dimitris.
Three training sessions have been delivered thus far, with the latest forming part of the successful AI Festival hosted by the in June. The fourth and final session is open to invited external researchers only and will take place in September at the Ipsos offices in London.
Speaking about the benefits of the training, Sarah explains:
鈥淎doption of artificial intelligence is increasing in almost every aspect of our society, including life sciences and academia. Learning to utilise the technologies emerging from AI and LLMs will help our researchers save time to be spent delivering innovative research in their fields.鈥�
The training follows the publication of a that was produced by Sarah for her PhD project, the analysis for which was conducted in collaboration with Ipsos. Speaking on behalf of Ipsos UK, Dimitris explains the importance of AI accessibility to researchers:
“Many of the attending researchers had little to no experience with coding or LLMs but now have the knowledge and confidence to use the technology to conduct analysis at a foundational level from which they can build upon鈥�.
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