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ASTRA – AI Solutions for Time-Restricted Academics

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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 emotional labour, as well as 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?

What other research are we doing in this space?

We are:

  • developing ResearchComplianceBot that helps academics with the research compliance process ie applying for data protection registration and ethics clearance.
  • exploring how AI can help time-poor academics to conduct peer review

People

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.

Publications

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 advances, 5, 100114.