Providing rich feedback to students is essential for supporting student learning. Recent advances in generative AI, particularly within large language modelling (LLM), provide the opportunity to deliver repeatable, scalable and instant automatically generated feedback to students, making abundant a previously scarce and expensive learning resource. Such an approach is feasible from a technical perspective due to these recent advances in Artificial Intelligence (AI) and Natural Language Processing (NLP); while the potential upside is a strong motivator, doing so introduces a range of potential ethical issues that must be considered as we apply these technologies. The attractiveness of AI systems is that they can effectively automate the most mundane tasks; but this risks introducing a "tyranny of the majority", where the needs of minorities in the long tail are overlooked because they are difficult to automate. Developing machine learning models that can generate valuable and authentic feedback requires the input of human domain experts. The choices we make in capturing this expertise -- whose, which, when, and how -- will have significant consequences for the nature of the resulting feedback. How we maintain our models will affect how that feedback remains relevant given temporal changes in context, theory, and prior learning profiles of student cohorts. These questions are important from an ethical perspective; but they are also important from an operational perspective. Unless they can be answered, our AI generated systems will lack the trust necessary for them to be useful features in the contemporary learning environment. This article will outline the frontiers of automated feedback, identify the ethical issues involved in the provision of automated feedback and present a framework to assist academics to develop such systems responsibly.

翻译：为学生提供丰富的反馈对于促进学习至关重要。生成式AI（尤其是大语言模型）的最新进展，使重复性、可扩展且即时生成的自动反馈成为可能，从而将以往稀缺且昂贵的教育资源变得充裕。从技术角度看，得益于人工智能与自然语言处理领域的突破，这种方法是可行的；尽管潜在收益极具吸引力，但在应用这些技术时，必须审慎考量随之而来的系列伦理问题。AI系统的优势在于能高效自动化最琐碎的任务，但这可能引发"多数人暴政"风险——长尾小众群体的需求因难以自动化而被忽视。开发能生成有价值、真实性反馈的机器学习模型，需要人类领域专家的参与。在捕捉这些专业知识时，我们关于"谁的、何种、何时、如何"的选择，将深刻影响最终反馈的性质。模型的维护方式则决定了反馈能否适应背景、理论及学生群体过往学习概况的阶段性变化。这些问题不仅关乎伦理，更涉及操作层面。若无法解答，AI生成系统将缺乏在当代学习环境中发挥实际效用的信任基础。本文旨在勾勒自动反馈的前沿进展，揭示其伦理困境，并提出一个框架以协助学术界负责任地开发此类系统。