FLoRA: An Advanced AI-Powered Engine to Facilitate Hybrid Human-AI Regulated Learning

Abstract: 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. Nevertheless, existing digital tools frequently fall short, lacking adaptability, focusing narrowly on isolated SRL phases, and insufficiently support meaningful human-AI interactions. In response, this paper introduces the enhanced \flora Engine, which incorporates advanced Generative Artificial Intelligence (GenAI) features and state-of-the-art learning analytics, explicitly grounded in SRL and HHAIRL theories. The \flora Engine offers instrumentation tools such as collaborative writing, multi-agents chatbot, and detailed learning trace logging to support dynamic, adaptive scaffolding 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 instrumentation tools can be utilized in real-world educational and experimental contexts. These studies demonstrate the effectiveness of \flora Engine in fostering SRL and HHAIRL, providing both theoretical insights and practical solutions for the future of AI-enhanced learning context.