The Responsible Development of Automated Student Feedback with Generative AI

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. 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. 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 the needs of minorities in the long tail are overlooked because they are difficult to automate. Application Design: This paper applies an extant ethical framework used for AI and machine learning to the specific challenge of providing automated feedback to student engineers. The task is considered from both a development and maintenance perspective, considering how automated feedback tools will evolve and be used over time. Findings: This paper identifies four key ethical considerations for the implementation of automated feedback for students: Participation, Development, Impact on Learning and Evolution over Time.