A Framework for Responsible Development of Automated Student Feedback
  with Generative AI
Euan D Lindsay Aditya Johri Johannes Bjerva
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.