Educational technology innovations that have been developed based on 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 literature 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 practical and ethical challenges of LLMs-based innovations were also identified by assessing their technological readiness, model performance, replicability, system transparency, privacy, equality, and beneficence. The findings were summarised into three recommendations for future studies, including updating existing innovations with state-of-the-art models (e.g., GPT-3), embracing the initiative of open-sourcing models/systems, and adopting a human-centred approach throughout the developmental process. These recommendations could support future research to develop practical and ethical innovations for supporting diverse educational tasks and benefiting students, teachers, and institutions.

翻译：基于大型语言模型（LLMs）开发的教育技术创新已展现出自动化文本内容生成与分析等繁琐过程的潜力。尽管已开发出多种创新技术来自动化一系列教育任务（如试题生成、反馈提供和论文评分），但这些技术的实践性和伦理性仍存在隐忧。此类隐忧可能阻碍未来研究及LLMs创新在真实教育场景中的落地应用。为此，我们对2017年以来发表的118篇同行评审论文进行了系统性文献综述，以厘清利用LLMs自动化支持教育任务的研究现状。通过评估其技术成熟度、模型性能、可复现性、系统透明度、隐私保护、公平性和有益性，我们识别出LLMs创新面临的实践与伦理挑战。研究结果归纳为三项未来研究建议：采用最新模型（如GPT-3）更新现有创新、推动模型/系统开源倡议、以及在开发全过程中采用人本主义方法。这些建议将支持未来研究开发兼具实践性与伦理性的创新方案，服务于多样化教育任务，惠及学生、教师及教育机构。