The Responsible Development of Automated Student Feedback with Generative AI

Abstract--Contribution: This paper identifies four critical ethical considerations for implementing generative AI tools to provide automated feedback to students. Background: Providing rich feedback to students is essential for supporting student learning. Recent advances in generative AI, particularly with large language models (LLMs), provide the opportunity to deliver repeatable, scalable and instant automatically generated feedback to students, making abundant a previously scarce and expensive learning resource. A visualisation of Bloom's revised taxonomy, modified from [6]. Intended Outcomes: The goal of this work is to enable the use of AI systems to automate mundane assessment and feedback tasks, without introducing a "tyranny of the majority", where HE release of powerful language technology tools based on generative language modelling (e.g., ChatGPT, GPT-are going to use AI tools in their working lives, we should 4(o), Claude, Gemini, Llama; [1]-[3]), marked a significant aim to train them in their use. For example, While assessment is a clear space of development for days after the release of ChatGPT, students, educators, and this type of educational technology, we argue that the real the public alike discovered the potential of the application potential of generative language modelling can be found in for assisting with a range of teaching and learning tasks, but student feedback. E. D. Lindsay is with the UNESCO Centre for Problem Based Learning M. Zhang is with the Department of Computer Science, Aalborg University, A.C. Meyers Vænge 15, 2450 København SV, Denmark. A. Johri is the Director of the Technocritical Research on AI, Learning J. Bjerva is with the Department of Computer Science, Aalborg University, Manuscript revised on July 31, 2024. Hence, this current state has common patterns of student answers and standardize responses effectively locked some engineering courses into a focus, to them, rather than having to make bespoke responses to where a particular set of questions are iterated over.