Document Type

Article

Publication Date

2025

DOI

10.3390/educsci15081004

Publication Title

Education Sciences

Volume

15

Issue

8

Pages

1004 (1-26)

Abstract

Current learning and development approaches often struggle to capture dynamic individual capabilities, particularly the skills they acquire informally every day on the job. This dynamic creates a significant gap between what traditional models think people know and their actual performance, leading to an incomplete and often outdated understanding of how ready the workforce truly is, which can hinder organizational adaptability in rapidly evolving environments. This paper proposes a novel dynamic learner-state ecosystem—an AI-driven solution designed to bridge this gap. Our approach leverages specialized AI agents, orchestrated via the Model Context Protocol (MCP), to continuously track and evolve an individual’s multi-dimensional state (e.g., mastery, confidence, context, and decay). The seamless integration of in-workflow performance data will transform daily work activities into granular and actionable data points through AI-powered dynamic xAPI generation into Learning Record Stores (LRSs). This system enables continuous, authentic performance-based assessment, precise skill gap identification, and highly personalized interventions. The significance of this ecosystem lies in its ability to provide a real-time understanding of everyone’s capabilities, enabling more accurate workforce planning for the future and cultivating a workforce that is continuously learning and adapting. It ultimately helps to transform learning from a disconnected, occasional event into an integrated and responsive part of everyday work.

Rights

© 2025 by the authors.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Data Availability

Article states: "No new data were created or analyzed in this study."

ORCID

0009-0003-1982-830X (Lovett)

Original Publication Citation

Yang, M., Lovett, N., Li, B., & Hou, Z. (2025). Towards dynamic learner state: Orchestrating AI agents and workplace performance via the Model Context Protocol. Education Sciences, 15(8), 1-26, Article 1004. https://doi.org/10.3390/educsci15081004

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