An app to encourage users to engage in interdisciplinary learning

Abstract

This paper presents the development of an application to encourage users to engage in interdisciplinary learning. By assessing interdisciplinary-natured essays, the interdisciplinary analytical tool will leverage LLMs to generate feedback for the users. As a modern leading-edge application that harnesses the potential of Artificial Intelligence, it aims to address the gaps in conventional assessment frameworks and embrace interdisciplinary learning, a concept growing in importance amidst a dynamically evolving world. This work will also explore different assessment methodologies, such as the use of contextualisation in analysis and the effects of LLM rationalisation on feedback quality through the Self-Taught Reasoner (STaR) framework. The user studies, which involved students who had taken the CC0002 course in NTU, offer much positivity and optimism for the potential of such a tool in the classroom. The findings outline how Generative AI can be used effectively as a learning assistant to support students in their interdisciplinary coursework, as well as provide insights into how this tool can be improved upon and scaled up to cover a larger area of their university education.