# Human-Centric eXplainable AI in Education

arXiv: https://arxiv.org/abs/2410.19822 · HTML: https://mlsanity.com/paper/2410.19822/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 59 references that are in the map.
- First posted on arXiv 18 Oct 2024.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://mlsanity.com/paper/1806.00069/) (2018)
- [Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond](https://mlsanity.com/paper/2304.13712/) (2023)
- [Questioning the AI: Informing Design Practices for Explainable AI User Experiences](https://mlsanity.com/paper/2001.02478/) (2020)
- [Expanding Explainability: Towards Social Transparency in AI systems](https://mlsanity.com/paper/2101.04719/) (2021)
- [Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation](https://mlsanity.com/paper/2305.02231/) (2023)
- [Enhancing LLM-Based Feedback: Insights from Intelligent Tutoring Systems and the Learning Sciences](https://mlsanity.com/paper/2405.04645/) (2024)
- [Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAI](https://mlsanity.com/paper/2302.00799/) (2023)
- [The Impact of Imperfect XAI on Human-AI Decision-Making](https://mlsanity.com/paper/2307.13566/) (2023)
- [XAI for All: Can Large Language Models Simplify Explainable AI?](https://mlsanity.com/paper/2401.13110/) (2024)
- [Seamful XAI: Operationalizing Seamful Design in Explainable AI](https://mlsanity.com/paper/2211.06753/) (2022)
- [A Novel Multi-Stage Prompting Approach for Language Agnostic MCQ Generation using GPT](https://mlsanity.com/paper/2401.07098/) (2024)
- [Personalized Education in the AI Era: What to Expect Next?](https://mlsanity.com/paper/2101.10074/) (2021)
- [Exploring the Capabilities of Prompted Large Language Models in Educational and Assessment Applications](https://mlsanity.com/paper/2405.11579/) (2024)
- [How Ready Are Generative Pre-trained Large Language Models for Explaining Bengali Grammatical Errors?](https://mlsanity.com/paper/2406.00039/) (2024)
- [How Effective is GPT-4 Turbo in Generating School-Level Questions from Textbooks Based on Bloom's Revised Taxonomy?](https://mlsanity.com/paper/2406.15211/) (2024)
- [Who's Thinking? A Push for Human-Centered Evaluation of LLMs using the XAI Playbook](https://mlsanity.com/paper/2303.06223/) (2023)

## Built on by (most cited first, 3 of 3)

- [Beyond the Single-Best Model: Rashomon Partial Dependence Profile for Trustworthy Explanations in AutoML](https://mlsanity.com/paper/2507.14744/) (2025)
- [Interpretability Framework for LLMs in Undergraduate Calculus](https://mlsanity.com/paper/2510.17910/) (2025)
- [A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata](https://mlsanity.com/paper/2510.22266/) (2025)

## 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?