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Leveraging AI to Support Underrepresented Scholars

Are you an academic who is using LLMs in your work? We’d love to hear from you. Complete this survey and we’ll send you a token of our appreciation or find out more about this project here.

Image created by Hywel Jenkins with ChatGPT4o

This project delves into the challenges faced by academics, particularly women and non-binary individuals, who bear the burden of invisible labour in the academy due to increased teaching, service, and administrative tasks when compared to their male colleagues. These non-research activities often go unnoticed but significantly impact academics’ time for research-related work and career progression.

We propose using Large Language Models as a potential solution. By leveraging LLMs, academics could alleviate some of the invisible labour, allowing them more time for research.

Funded by UCL Research Culture, we are currently undertaking research to explore how LLMs can support academics in managing their invisible labour, ultimately creating a more equitable academic environment. Take part in our survey.

Research Questions

  1. What is the nature of the invisible labour carried out by academics?
  2. What are the current practices of academics using LLMs to support their invisible labour?
  3. How might future tools based on LLMs be designed to support academics with their invisible labour?


This project is being developed by Jon Mella, Sarah Frampton, Prof Anna Cox Dr Anna Dowthwaite, with assistance from MSc students Yvonne Chang and Anya Emmons.


Mella, J., Frampton, S., & Cox, A.L. (2024) Unseen Work: Leveraging Generative AI for Invisible Academic Labour. MCI-WS09: Workshop on Generative Artificial Intelligence in Interactive Systems: Experiences from the Community

Cox, A. L., & Mole, S. E. (2024). Five questions on improving diversity, equity and inclusion in UK bioscience research or “How can UK bioscience be changed so that those from marginalised groups can thrive?”BBA advances5, 100114.