Capture-Calibrate-Coach: A Graph-Based Framework for Knowledge Monitoring Estimation and Adaptive Feedback
arXiv:2605.25419v1 Announce Type: new Abstract: Effective learning support requires understanding not only what learners know but also how accurately they perceive their own understanding. This metacognitive dimension, known as knowledge monitoring, fundamentally influences self-regulated learning, yet this dimension remains underexplored in current systems. This paper introduces the Capture-Calibrate-Coach (3C) framework for adaptive learning support. The Capture phase extracts learners' perceived knowledge states from open-ended self-reports to construct a heterogeneous graph linking learner
This research is emerging now due to the increasing sophistication of AI in understanding complex human cognition and the growing demand for more effective, personalized learning systems.
A strategic reader should care because improving knowledge monitoring in AI-driven education can significantly enhance human capital development and the efficacy of workforce training, directly impacting productivity and innovation.
The ability of AI to not only assess learned knowledge but also a learner's self-perception of that knowledge opens new avenues for adaptive education and more robust human-AI collaboration in learning contexts.
- · EdTech companies
- · AI developers
- · Educational institutions
- · Individual learners
- · Traditional static learning platforms
- · Ineffective assessment methods
Adaptive learning systems will become more personalized and effective by explicitly integrating metacognitive feedback.
This could lead to a significant acceleration in skill acquisition and reskilling efforts across various industries.
The enhanced human-AI interaction in learning could foster greater trust and collaboration between humans and AI in other complex tasks.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG