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

2025-07-09

AI Summary

Existing digital tools inadequately support hybrid human–AI regulated learning (HHAIRL), exhibiting weak adaptivity, fragmented phase support, and superficial interaction. To address this, we propose FLoRA—a theoretically grounded, integrative engine combining generative AI, multi-agent dialogue systems, collaborative writing tools, and fine-grained learning trajectory analytics—to deliver dynamic, just-in-time, and precise scaffolding across the entire self-regulated learning (SRL) process. Its core innovation lies in centering learner agency and reflective capacity, thereby overcoming dual limitations of conventional tools in adaptivity and interaction depth. Empirical evaluations—including controlled experiments and classroom-based implementations—demonstrate that FLoRA significantly enhances SRL competency development and effectively facilitates high-quality, synergistic human–AI learning interactions in authentic educational settings.

Technology Category

• Humans and AI: Human-in-the-loop Machine Learning
• Multiagent Systems: Multiagent Learning
• Machine Learning: Life-Long and Continual Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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 Artificial Intelligence (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.

Problem

• Enhancing Hybrid Human-AI Regulated Learning (HHAIRL) with adaptive AI scaffolding
• Addressing limitations of current digital tools in supporting SRL phases
• Integrating GenAI and learning analytics to foster self-regulated learning (SRL)

Innovation

• Advanced GenAI features for adaptive scaffolding
• Multi-agents chatbot for real-time support
• Learning trace logging for personalized feedback