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

Xiaoshan Yu, Shangshang Yang, Ziwen Wang, Haiping Ma, Xingyi Zhang

TL;DR

Trustworthy intelligent education addresses trust-related risks in high-stakes, data-rich educational contexts. The authors propose a two-level taxonomy that first delineates five representative tasks in the intelligent education pipeline and then analyzes these tasks through five trustworthiness dimensions: safety and privacy, robustness, fairness, explainability, and sustainability. By synthesizing methodologies across tasks, the paper exposes fragmentation, gaps, and interdependencies, and outlines challenge-driven directions and frontier topics such as multi-modal data, LLM-enabled education, and standardized trust benchmarks. The resulting framework provides a coherent reference and practical roadmap for researchers and practitioners to design scalable, fair, and pedagogically aligned intelligent educational systems.

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.

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

TL;DR

Trustworthy intelligent education addresses trust-related risks in high-stakes, data-rich educational contexts. The authors propose a two-level taxonomy that first delineates five representative tasks in the intelligent education pipeline and then analyzes these tasks through five trustworthiness dimensions: safety and privacy, robustness, fairness, explainability, and sustainability. By synthesizing methodologies across tasks, the paper exposes fragmentation, gaps, and interdependencies, and outlines challenge-driven directions and frontier topics such as multi-modal data, LLM-enabled education, and standardized trust benchmarks. The resulting framework provides a coherent reference and practical roadmap for researchers and practitioners to design scalable, fair, and pedagogically aligned intelligent educational systems.

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.
Paper Structure (41 sections, 3 figures)

This paper contains 41 sections, 3 figures.

Table of Contents

  1. Introduction
  2. Intelligent Education: Task Landscape
  3. Learner Ability Assessment
  4. Learning Resource Recommendation
  5. Learning Analytics
  6. Education Content Understanding
  7. Instructional Assistance
  8. Trustworthiness in Intelligent Education
  9. Safety & Privacy
  10. Federated Learning.
  11. Machine Unlearning.
  12. Privacy Attacks.
  13. Discussion
  14. Robustness
  15. Denoising-based Methods.
  16. ...and 26 more sections

Figures (3)

  • Figure 1: A Taxonomy of Trustworthy Intelligent Education.
  • Figure 2: An overview of intelligent education task categories. Five high-level task categories are considered, including learner ability assessment, learning resource recommendation, learning analytics, educational content understanding, and instructional assistance.
  • Figure 3: An overview of research categories for trustworthy intelligent education. The categories cover five trust-related aspects and are organized to reflect an desirable trajectory, from safety&privacy and robustness to explainability, fairness, and ultimately sustainability.