AI与个性化学习：弥合与现代教育目标的差距
AI and personalized learning: bridging the gap with modern educational goals
AI总结：
本研究对照《OECD学习罗盘2030》目标分析AI驱动个性化学习与现代教育目标的差距，指出需融入学习者主体性等教育要素，还提出融合生成式AI与教师引导协作的混合个性化学习模型。
摘要：
个性化学习（PL）旨在为教育领域的“一刀切”模式提供替代方案。基于技术的个性化学习解决方案在提升学习表现方面已展现出显著成效。然而，它们与现代教育更广泛目标的契合度在不同技术和研究领域之间存在差异。本文中，我们依据《OECD学习罗盘2030》中概述的目标，考察了AI驱动的个性化学习解决方案的特征。我们的分析表明，现代教育目标与个性化学习的技术实现路径之间存在差距。我们明确了基于AI的个性化学习解决方案可吸纳当代教育核心要素的领域，例如培养学习者的主体性、认知参与度和综合能力。虽然仅聚焦于特定领域知识获取的个性化学习解决方案在辅助学习过程中发挥着作用，但教育专家所设想的个性化学习不止于简单的技术工具，还需要教育系统的整体性变革。最后，我们探讨了ChatGPT等生成式AI的潜力，并提出了一种混合模型，将人工智能与协作式、教师引导的个性化学习方法相融合。
英文摘要：
Personalized learning (PL) aspires to provide an alternative to the one-size-fits-all approach in education. Technology-based PL solutions have shown notable effectiveness in enhancing learning performance. However, their alignment with the broader goals of modern education is inconsistent across technologies and research areas. In this paper, we examine the characteristics of AI-driven PL solutions in light of the goals outlined in the OECD Learning Compass 2030. Our analysis indicates a gap between the objectives of modern education and the technological approach to PL. We identify areas where the AI-based PL solutions could embrace essential elements of contemporary education, such as fostering learner's agency, cognitive engagement, and general competencies. While the PL solutions that narrowly focus on domain-specific knowledge acquisition are instrumental in aiding learning processes, the PL envisioned by educational experts extends beyond simple technological tools and requires a holistic change in the educational system. Finally, we explore the potential of generative AI, such as ChatGPT, and propose a hybrid model that blends artificial intelligence with a collaborative, teacher-facilitated approach to personalized learning.
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。
