In recent years, trustworthiness has garnered increasing attention and exploration in the field of intelligent education, due to the inherent sensitivity of educational scenarios, such as involving minors and vulnerable groups, highly personalized learning data, and high-stakes educational outcomes. However, existing research either focuses on task-specific trustworthy methods without a holistic view of trustworthy intelligent education, or provides survey-level discussions that remain high-level and fragmented, lacking a clear and systematic categorization. To address these limitations, in this paper, we present a systematic and structured review of trustworthy intelligent education. Specifically, We first organize intelligent education into five representative task categories: learner ability assessment, learning resource recommendation, learning analytics, educational content understanding, and instructional assistance. Building on this task landscape, we review existing studies from five trustworthiness perspectives, including safety and privacy, robustness, fairness, explainability, and sustainability, and summarize and categorize the research methodologies and solution strategies therein. Finally, we summarize key challenges and discuss future research directions. This survey aims to provide a coherent reference framework and facilitate a clearer understanding of trustworthiness in intelligent education.

翻译：近年来，由于教育场景固有的敏感性——例如涉及未成年人及弱势群体、高度个性化的学习数据以及高利害关系的教育成果——可信性在智能教育领域获得了日益增长的关注与探索。然而，现有研究要么聚焦于特定任务的可信方法而缺乏对可信智能教育的整体观照，要么仅提供综述层面的讨论，这些讨论仍停留在高层次且较为零散，缺乏清晰、系统的分类。为应对这些局限，本文对可信智能教育进行了系统化、结构化的综述。具体而言，我们首先将智能教育组织为五个代表性任务类别：学习者能力评估、学习资源推荐、学习分析、教育内容理解以及教学辅助。基于此任务版图，我们从五个可信性视角——包括安全与隐私、鲁棒性、公平性、可解释性以及可持续性——回顾了现有研究，并对其中的研究方法和解决策略进行了总结与分类。最后，我们总结了关键挑战并探讨了未来的研究方向。本综述旨在提供一个连贯的参考框架，并促进对智能教育中可信性更清晰的理解。