Human-Centric eXplainable AI in Education
It addresses the need for explainable AI in educational contexts to foster trust and inclusivity among educators and students, but it appears incremental as it builds on existing explainability concepts without introducing new methods or data.
This paper tackles the problem of making AI systems in education understandable and trustworthy by exploring Human-Centric eXplainable AI (HCXAI), analyzing implementation challenges and providing frameworks and recommendations to enhance learning outcomes and transparency.
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?