  Educational technology innovations leveraging large language models (LLMs)
have shown the potential to automate the laborious process of generating and
analysing textual content. While various innovations have been developed to
automate a range of educational tasks (e.g., question generation, feedback
provision, and essay grading), there are concerns regarding the practicality
and ethicality of these innovations. Such concerns may hinder future research
and the adoption of LLMs-based innovations in authentic educational contexts.
To address this, we conducted a systematic scoping review of 118 peer-reviewed
papers published since 2017 to pinpoint the current state of research on using
LLMs to automate and support educational tasks. The findings revealed 53 use
cases for LLMs in automating education tasks, categorised into nine main
categories: profiling/labelling, detection, grading, teaching support,
prediction, knowledge representation, feedback, content generation, and
recommendation. Additionally, we also identified several practical and ethical
challenges, including low technological readiness, lack of replicability and
transparency, and insufficient privacy and beneficence considerations. The
findings were summarised into three recommendations for future studies,
including updating existing innovations with state-of-the-art models (e.g.,
GPT-3/4), embracing the initiative of open-sourcing models/systems, and
adopting a human-centred approach throughout the developmental process. As the
intersection of AI and education is continuously evolving, the findings of this
study can serve as an essential reference point for researchers, allowing them
to leverage the strengths, learn from the limitations, and uncover potential
research opportunities enabled by ChatGPT and other generative AI models.
