用AI构建可规模化个性化学习框架，解决教育不平等难题。
AI-powered Digital Framework for Personalized Economical Quality Learning at Scale
- 基于深度学习理论设计自适应学习系统，强化学习者自主性
- 提出8项核心原则，融合学习科学与AI技术实现精准支持
- 适合教育科技开发者及政策制定者参考落地
优质教育资源的获取存在显著差距，无论在发达国家与发展中国家之间，还是在同一国家内部，这一问题均因社会经济壁垒和就业市场快速变化而加剧。本文提出一种基于深度学习（DL）理论的AI驱动数字学习框架，强调学习者主体性，将教师角色重塑为引导者，适用于大规模教育场景。我们从学习科学与AI中提炼出八项关键原则，用于构建基于开放学习者建模（OLM）的智能学习环境（DLE）。该框架通过AI进行学习者画像、活动推荐及双端（学习者与引导者）辅助支持，促进协作式、沉浸式学习体验。本研究为全球范围内实现高质量、低成本规模化教育提供了可行路径，并应对了教育中部分AI应用挑战。
原文摘要 · Abstract (English)
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.
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