用于大规模个性化、经济且高质量学习的 AI 驱动数字框架
AI-powered Digital Framework for Personalized Economical Quality Learning at Scale
AI总结：
针对全球优质教育获取不均及低成本规模化需求，论文提出基于深度学习理论的 AI 数字学习框架，以 OLM、活动建议和双向 AI 支持实现个性化协作学习。
摘要：
无论是发达国家与发展中国家之间，还是各国内部，无论其经济地位如何，获得优质教育方面的差距都十分显著。社会经济障碍以及就业市场的快速变化进一步加剧了这一问题，凸显出对能够以低成本大规模提供优质教育的创新解决方案的需求。本文通过提出一个以深度学习（DL）理论为基础的 AI 驱动数字学习框架来应对这些挑战。DL 理论强调学习者的能动性，并将教师角色重新定义为促进者，因此特别适合可扩展的教育环境。我们概述了源自学习科学与 AI 的八项关键原则，这些原则对于实施基于 DL 的数字学习环境（DLEs）至关重要。所提出的框架利用 AI 进行基于开放学习者建模（OLM）的学习者建模、活动建议，并为学习者和促进者提供 AI 辅助支持，从而培养协作式且富有参与感的学习体验。我们的框架为全球范围内可扩展的高质量教育提供了有前景的方向，并为教育中一些与 AI 相关的挑战提供了实用解决方案。
英文摘要：
The disparity in access to quality education is significant, both between developed and developing countries and within nations, regardless of their economic status. Socioeconomic barriers and rapid changes in the job market further intensify this issue, highlighting the need for innovative solutions that can deliver quality education at scale and low cost. This paper addresses these challenges by proposing an AI-powered digital learning framework grounded in Deep Learning (DL) theory. The DL theory emphasizes learner agency and redefines the role of teachers as facilitators, making it particularly suitable for scalable educational environments. We outline eight key principles derived from learning science and AI that are essential for implementing DL-based Digital Learning Environments (DLEs). Our proposed framework leverages AI for learner modelling based on Open Learner Modeling (OLM), activity suggestions, and AI-assisted support for both learners and facilitators, fostering collaborative and engaging learning experiences. Our framework provides a promising direction for scalable, high-quality education globally, offering practical solutions to some of the AI-related challenges in education.
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。
