ARIEL: Revolutionising student support with generative AI – supporting assessment writing and reducing staff workload
ARIEL: Revolutionising student support with generative AI – supporting assessment writing and reducing staff workload
Where did the research take place?
The study site has not been established. Author addresses may differ from where the research occurred.
A plain-language reading has not been prepared for this paper yet.
Original abstract
The School of Medicine found that there had been a number of student inquiries regarding assessment writing, this is not a new phenomenon, but it does pose a significant challenge year-on-year. Many of these questions relate to information that is available in existing assessment documents, yet students often overlook these and opt to email staff. This situation can lead to an increase in workload for staff, who may repeatedly address the same queries via email. To address this issue, our team developed an innovative solution: an assessment chatbot (named ARIEL) powered by generative AI (GenAI) via co-pilot studio. The objective of ARIEL’s creation was to support students in their assessment writing, while simultaneously reducing staffs’ email workload. ARIEL was designed to provide instant, accurate answers to common assessment-related questions by leveraging the information contained within the assessment documents and utilising appropriate websites. This not only ensured that students received timely assistance, at any time of the day, but has also allowed staff to have more time with other work-related duties. The success of ARIEL has demonstrated the potential of GenAI in various educational contexts. This technology can be extended to support academic writing by providing students with guidance on structuring essays, citing sources, and adhering to academic standards. Additionally, it can streamline administrative processes by offering instant responses to queries about placement information, course or module details, and student wellbeing support signposting. By automating these routine tasks, staff can dedicate more time to personalised student interactions and other high-priority responsibilities. Our presentation will detail the development and implementation of ARIEL, highlighting the integration of GenAI technology and the collaborative efforts of the staff at the School of Medicine. We will also discuss the positive impact on both students and staff, including improved student satisfaction and significant reductions in staff workload. By sharing our experiences and insights, we aim to demonstrate the potential of GenAI in enhancing educational support systems and streamlining administrative processes. Overall, this poses questions and stimulate debate on ‘what else could GenAI chatbots be used for?’.