Reconstruction Path and Practical Framework of AI Agent Empowered In-house Training in Mining Enterprises

DOI

10.2991/978-94-6239-766-8_35How to use a DOI?

Keywords

Mining enterprises; In-house training; AI agent

Abstract

In-house training in mining enterprises has long been constrained by the tension between work and learning, the disconnection between training content and operational scenarios, insufficient personalization, and the difficulty of quantitatively evaluating training outcomes. Drawing on educational theories, this study systematically examines the underlying deficiencies of mining training and proposes an “AI-agent-as-hub” training empowerment framework comprising four core modules: adaptive learning path generation, immersive scenario simulation, multi-agent collaborative coaching, and intelligent data-feedback loop. A 12-week quasi-experimental study was conducted across three coal mining enterprises using a pretest–posttest control group design, with 120 miners assessed across five competency dimensions. Results indicate that the experimental group significantly outperformed the control group across all five dimensions—safety regulation knowledge mastery, fault diagnosis accuracy, emergency decision-making soundness, equipment operation standardization, and safety behavior compliance (p<0.01)—with a composite competency improvement 3.0 times that of the control group. The findings confirm that AI agents restructure the training ecosystem through a human–AI collaboration paradigm, shifting mining training from “standardized inculcate” to “precision empowerment.”

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Deng Jieru
AU  - Zhou Mengzhou
AU  - Xiao Canjun
AU  - Zhou Xiying
PY  - 2026
DA  - 2026/09/04
TI  - Reconstruction Path and Practical Framework of AI Agent Empowered In-house Training in Mining Enterprises
BT  - Proceedings of the 2026 6th International Conference on Education, Information Management and Service Science  (EIMSS 2026)
PB  - Atlantis Press
SP  - 342
EP  - 350
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6239-766-8_35
DO  - 10.2991/978-94-6239-766-8_35
ID  - Jieru2026
ER  -