Date
Publisher
arXiv
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.
Study at a glance
- Question: How are trustworthiness dimensions addressed across educational tasks, and what challenges remain?
- Setting and data: Not stated in the paper
- Method: Systematic review and conceptual taxonomy development
Why it matters
Untrustworthy AI in education risks privacy leakage, biased decisions, and unstable predictions for vulnerable learners. However, existing research remains fragmented across isolated tasks and lacks a structured framework.
Key findings
- Intelligent education can be categorized into five core tasks: ability assessment, resource recommendation, learning analytics, content understanding, and instructional assistance.
- Techniques addressing safety, robustness, fairness, explainability, and sustainability remain largely fragmented within isolated tasks rather than across entire educational pipelines.
- Current methods often rely on idealized data assumptions, short-term fairness definitions, or single-modal data, leaving long-term maintainability and multi-modal trustworthiness unaddressed.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
What is the application?
Who age?
Why use AI?
Study design
