Human-Centric eXplainable AI in Education

TL;DR

This paper discusses the development of human-centric explainable AI systems in education, emphasizing trust, transparency, and user engagement through innovative frameworks and large language models.

Contribution

It introduces comprehensive frameworks for HCXAI in education, focusing on user understanding, engagement, and ethical considerations, especially with large language models.

Findings

Enhanced learning outcomes through explainable AI

Increased trust and transparency in AI-driven educational tools

Frameworks for developing user-centric HCXAI systems

Abstract

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?