Trustworthy Intelligent Education: A Systematic Perspective on Progress, Challenges, and Future Directions

2026-01-29

AI Summary

This study addresses the lack of a systematic understanding of trustworthiness—encompassing safety, privacy, fairness, explainability, and related aspects—in current intelligent education research, which often remains confined to specific tasks or fragmented reviews. To bridge this gap, the paper proposes the first comprehensive framework for analyzing trustworthiness in intelligent education, integrating five core educational tasks with five key trustworthiness dimensions: safety and privacy, robustness, fairness, explainability, and sustainability. Through a systematic literature review, the authors structurally synthesize existing technical approaches, establish a clear reference framework, identify critical challenges, and outline future research directions. This work fills a significant void in holistic scholarship on the topic and provides foundational support for both theoretical advancement and practical implementation of trustworthy intelligent education systems.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Intelligent Robots: Other Foundations and Applications

Application Category

• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data
• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

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.

Problem

• trustworthy intelligent education
• systematic review
• educational AI
• trustworthiness
• intelligent education

Innovation

• trustworthy AI
• intelligent education
• systematic review
• educational AI
• AI ethics