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.

翻译：近年来，基于大型语言模型（LLMs）的教育技术创新在自动化文本生成与分析这一繁重任务中展现出巨大潜力。尽管已开发出多种创新方案以实现教育任务的自动化（如试题生成、反馈提供与论文评分），但这类技术的实用性与伦理合规性仍面临诸多质疑。这些顾虑可能阻碍未来研究进展及基于LLMs的创新成果在真实教育场景中的实际应用。为此，本研究对2017年以来发表的118篇同行评审论文进行了系统范围综述，旨在明确当前利用LLMs自动化支持教育任务的研究现状。结果显示，LLMs在教育任务自动化领域存在53种应用场景，可归纳为九大类别：用户画像/标注、检测、评分、教学支持、预测、知识表征、反馈、内容生成及推荐系统。同时，本研究识别出若干实践与伦理挑战，包括技术成熟度不足、可复现性与透明性缺失、隐私保护与善行原则考量欠缺等。基于研究发现，我们为未来研究提出三项建议：采用前沿模型（如GPT-3/4）更新现有创新成果、推动模型/系统开源倡议、在开发全流程贯彻以人为本原则。鉴于人工智能与教育的交叉研究领域持续演进，本研究成果可为研究者提供关键参考基准，使其既能借鉴现有优势、汲取经验教训，亦能发掘ChatGPT及其他生成式AI模型所开创的潜在研究机遇。