mlsanityData to 13 Oct 2026

Human-Centric eXplainable AI in Education

arXiv 2410.19822 ↗First posted 18 Oct 2024cs.CY cs.AI cs.HCcited by 3

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Abstract (arXiv)

As artificial intelligence (AI) becomes more integrated into educational environments, how can we ensure that these systems are both understandable and trustworthy? The growing demand for explainability in AI systems is a critical area of focus. This paper explores Human-Centric eXplainable AI (HCXAI) in the educational landscape, emphasizing its role in enhancing learning outcomes, fostering trust among users, and ensuring transparency in AI-driven tools, particularly through the innovative use of large language models (LLMs). What challenges arise in the implementation of explainable AI in educational contexts? This paper analyzes these challenges, addressing the complexities of AI models and the diverse needs of users. It outlines comprehensive frameworks for developing HCXAI systems that prioritize user understanding and engagement, ensuring that educators and students can effectively interact with these technologies. Furthermore, what steps can educators, developers, and policymakers take to create more effective, inclusive, and ethically responsible AI solutions in education? The paper provides targeted recommendations to address this question, highlighting the necessity of prioritizing explainability. By doing so, how can we leverage AI's transformative potential to foster equitable and engaging educational experiences that support diverse learners?

Key facts. Cited by 3 papers in the map (data through 13 Oct 2026). Builds on 20 of its 59 references that are in the map. First posted on arXiv 18 Oct 2024.

Builds on

20 of 59 references.

References · 20

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8 more
  1. 2024XAI for All: Can Large Language Models Simplify Explainable AI?cited by 21
  2. 2022Seamful XAI: Operationalizing Seamful Design in Explainable AIcited by 20
  3. 2024A Novel Multi-Stage Prompting Approach for Language Agnostic MCQ Generation using GPTcited by 19
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Citing papers per year: 2025: 3.

3 papers.

  1. 2025Beyond the Single-Best Model: Rashomon Partial Dependence Profile for Trustworthy Explanations in AutoMLcited by 6
  2. 2025Interpretability Framework for LLMs in Undergraduate Calculuscited by 0
  3. 2025A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdatacited by 0