Self-Regulated Learning (SRL), defined as learners' ability to systematically plan, monitor, and regulate their learning activities, is crucial for sustained academic achievement and lifelong learning competencies. Emerging AI developments profoundly influence SRL interactions by potentially either diminishing or strengthening learners' opportunities to exercise their own regulatory skills. Recent literature emphasizes a balanced approach termed Hybrid Human-AI Regulated Learning (HHAIRL), in which AI provides targeted, timely scaffolding while preserving the learners' role as active decision-makers and reflective monitors of their learning process. Central to HHAIRL is the integration of adaptive and personalized learning systems; by modelling each learner's knowledge and self-regulation patterns, AI can deliver contextually relevant scaffolds that support learners during all phases of the SRL process. Nevertheless, existing digital tools frequently fall short, lacking adaptability and personalisation, focusing narrowly on isolated SRL phases, and insufficiently supporting meaningful human-AI interactions. In response, this paper introduces the enhanced FLoRA Engine, which incorporates advanced generative AI features and state-of-the-art learning analytics, and grounds in solid educational theories. The FLoRA Engine offers tools such as collaborative writing, multi-agent chatbots, and detailed learning trace logging to support dynamic, adaptive scaffolding of self-regulation tailored to individual needs in real time. We further present a summary of several research studies that provide the validations for and illustrate how these tools can be utilized in real-world educational and experimental contexts. These studies demonstrate the effectiveness of FLoRA Engine in fostering SRL, providing both theoretical insights and practical solutions for the future of AI-enhanced learning contexts.

翻译：自我调节学习（Self-Regulated Learning, SRL）被定义为学习者系统规划、监控和调节其学习活动的能力，对于持续的学业成就和终身学习能力至关重要。新兴的人工智能发展深刻影响了SRL的互动过程，既可能削弱也可能增强学习者运用自身调节技能的机会。近期文献强调一种称为混合人机协同调节学习（Hybrid Human-AI Regulated Learning, HHAIRL）的平衡方法，其中人工智能提供针对性、及时的支架支持，同时保留学习者作为学习过程的主动决策者和反思监控者的角色。HHAIRL的核心在于整合自适应与个性化学习系统；通过建模每位学习者的知识与自我调节模式，人工智能能够提供情境相关的支架，在学习者SRL过程的各个阶段给予支持。然而，现有的数字工具往往存在不足，缺乏适应性与个性化，仅狭隘地关注孤立的SRL阶段，且未能充分支持有意义的人机交互。为此，本文介绍了增强型FLoRA引擎，该引擎融合了先进的生成式人工智能功能与前沿学习分析技术，并基于坚实的教育理论。FLoRA引擎提供协作写作、多智能体聊天机器人及详细学习轨迹记录等工具，以支持针对个体需求实时动态调整的自适应自我调节支架。我们进一步综述了多项实证研究，这些研究验证了该引擎的有效性，并展示了这些工具在真实教育及实验场景中的应用方式。研究表明，FLoRA引擎在促进SRL方面具有显著效果，为未来人工智能增强的学习环境提供了理论洞见与实践解决方案。