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

TL;DR

The paper presents FLoRA, an advanced AI-enhanced engine designed to support Hybrid Human-AI Regulated Learning by instrumenting, tracing, and scaffolding Self-Regulated Learning in real time. It integrates GenAI with learning analytics to deliver personalized, adaptive scaffolds while preserving learner agency, and validates the approach through ESL writing, clinical reasoning, and analytics-based scaffolding case studies. Findings indicate that personalized GenAI scaffolding can promote SRL behaviors and engagement, though risks such as metacognitive disengagement and AI hallucinations warrant careful design and training. The work highlights practical implications for education, including dashboards, transparency, and educator preparation, and argues for balanced, learner-centered AI integration to advance SRL and co-regulated learning in diverse contexts.

Abstract

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.

Figures (16)

• Figure 1: Overview of the FLoRA User Interface
• Figure 2: GenAI-based scaffolding tool
• Figure 3: GenAI-based scaffolding trigger mechanism
• Figure 4: GenAI Prompt Example
• Figure 5: Writing analytics tool - basic writing