让AI教育工具更透明易懂，提升师生信任与学习效果
Human-Centric eXplainable AI in Education
- 以用户为中心设计可解释AI，聚焦师生理解与参与
- 提出整合大语言模型的HCXAI框架，增强教育AI透明度
- 适合教育AI开发者、教师及政策制定者参考
随着人工智能在教育环境中的深入融合，如何确保这些系统既可理解又值得信赖成为关键问题。本文探讨了以人为本的可解释人工智能（HCXAI）在教育领域的应用，强调其在提升学习成效、建立用户信任以及保障AI驱动工具透明性方面的作用，尤其通过创新使用大语言模型（LLMs）实现。论文分析了教育场景中可解释AI实施所面临的挑战，包括模型复杂性与用户需求多样性，并提出了兼顾用户理解与互动的综合框架。此外，针对教育工作者、开发者与政策制定者，本文提供了具体建议，强调解释性优先的重要性，旨在充分发挥AI的变革潜力，构建公平且具吸引力的教育体验，支持多样化学习者。
原文摘要 · Abstract (English)
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?
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