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These submissions were received from Advance HE members as part of the AI Garage member benefit project in 2023 - 24. This project aims to provide a snapshot of the different kinds of AI projects in progress within the sector, as such we cannot take responsibility for the content of the submissions themselves. Projects featured here are not endorsed or supported by Advance HE. Please reach out directly to the teams to find out more about their work. Find out more about the project.

Please note that we will upload new submissions on a weekly basis to ensure that we have a constant flow of new resources for the sector. 

 

AI Garage – Can Generative AI be used to deliver personalised yet sustainable feedback to large cohorts?

Can Generative AI be used to deliver personalised yet sustainable feedback to large cohorts?  

Lead institution: Keele University School of Medicine 

Country: United Kingdom

Project primary contact: Nazim Ali, Lecturer and Deputy Director of Education (n.ali1@keele.ac.uk)

Project secondary contact: Sarah Aynsley 

Project Title: Can Generative AI be used to deliver personalised yet sustainable feedback to large cohorts?  

The project is: In Progress 

Project Summary: 

It is recognised that providing written feedback which is detailed and specific is time-consuming and requires it to be delivered in a timely manner for student uptake. However, providing personalised feedback is unsustainable especially within a large cohort of students. Thus, there is a need to find ways to provide personalised feedback. We are exploring the use generative AI (GIA) models as feedback tools to provide constructive and teacher-like personalised feedback. As proof of concept, we have developed a ChatGPT based system which can provide feedback on students’ work. We used a panel of teachers to evaluate our approach and they reported that the ChatGPT derived feedback was detailed and constructive. We perceive that a GIA based feedback system could offer significant pedagogical benefits such as real-time feedback which would maximise student engagement with feedback. As such we are currently undertaking a full-scale study to test our approach within a large cohort of students with the aim of gathering their experiences and perceptions of using GIA as a personalised learning tool.  

Impact: To improve student learning by providing personalised and real-time feedback. 

Audience: The project will directly impact students and how they engage with feedback. 

This output is part of a member project - AI Garage: Creating the Future Now which collects and curates cutting-edge practice examples of AI. You can explore other submissions here.

Resource type:
Institution:
Queen Mary University of London