教育领域中以人为本的可解释人工智能
Human-Centric eXplainable AI in Education
AI总结：
本文探讨教育场景中的HCXAI，分析可解释AI实施挑战，提出以用户理解与参与为核心的框架，并面向教育者、开发者和政策制定者给出建议。
摘要：
随着人工智能（AI）日益融入教育环境，我们如何确保这些系统既易于理解又值得信赖？对AI系统可解释性日益增长的需求是一个关键的关注领域。本文探讨教育领域中的以人为本的可解释人工智能（HCXAI），强调其在提升学习成果、培养用户信任以及确保AI驱动工具透明度方面的作用，尤其是通过对大语言模型（LLM）的创新使用。在教育情境中实施可解释AI会出现哪些挑战？本文分析了这些挑战，探讨AI模型的复杂性和用户的多样化需求。文中概述了用于开发HCXAI系统的综合框架，这些框架优先考虑用户理解和参与，确保教育工作者和学生能够有效地与这些技术互动。此外，教育工作者、开发者和政策制定者可以采取哪些措施，以在教育中创建更有效、更具包容性且合乎伦理责任的AI解决方案？本文针对这一问题提供了有针对性的建议，强调了优先考虑可解释性的必要性。通过这样做，我们如何利用AI的变革潜力，促进公平且富有吸引力的教育体验，以支持多样化的学习者？
英文摘要：
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?
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
