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Related Concept Videos

Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Methods of Documentation II: POMR01:26

Methods of Documentation II: POMR

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A Single-Component System01:24

A Single-Component System

In the field of chemistry, the terms "component" and "phase" hold significant importance. A component refers to a chemically distinct substance in a system that has specific properties. It is chemically homogeneous, meaning it has the same properties throughout. For example, in a mixture of salt and water, both salt and water are considered separate components because they have different chemical properties.On the other hand, a phase is a form of matter that has a consistent chemical...
Methods of Documentation VII: EMR01:30

Methods of Documentation VII: EMR

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Impact of Schemas01:30

Impact of Schemas

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Related Experiment Videos

A multi-agent RAG system for generating SCORM courses from enterprise documents.

Gulshat Amirkhanova1, Bauyrzhan Amirkhanov1, Alikhan Amirkhanov1

  • 1Department of Artificial Intelligence and Big Data, Faculty of Information Technology, Al-Farabi Kazakh National University, Almaty, Kazakhstan.

Frontiers in Artificial Intelligence
|June 17, 2026
PubMed
Summary

This study introduces an AI pipeline for generating e-learning courses from company documents. It automates instructional design for corporate training, ensuring factual accuracy and reducing errors.

Keywords:
SCORMautomated course generationcorporate onboardingenterprise learningretrieval-augmented generation

Related Experiment Videos

Area of Science:

  • Artificial Intelligence
  • Educational Technology
  • Knowledge Management

Background:

  • Existing AI course generation systems lack focus on enterprise-specific knowledge.
  • Scalability and consistency in corporate training are hindered by the gap in AI applications for internal documents.
  • Regulated industries require high factual accuracy in training, which current systems do not adequately support.

Purpose of the Study:

  • To develop an automated system for generating SCORM-compliant e-learning courses from heterogeneous enterprise documents.
  • To address the limitations of current AI course generation systems in the corporate knowledge domain.
  • To ensure factual accuracy and traceability of training content to source materials.

Main Methods:

  • A multi-agent pipeline was designed, integrating semantic document ingestion, structure-aware chunking, and embedding.
  • An autonomous ReAct-based architect agent was employed for course design.
  • A content generation pipeline with multi-query retrieval and neural reranking was utilized, followed by SCORM packaging.

Main Results:

  • The system successfully generated a complete, multi-module e-learning course from occupational safety documents in minutes.
  • End-to-end automation of instructional design was demonstrated, grounded solely in the provided source materials.
  • The generated content exhibited traceability to the original organizational knowledge.

Conclusions:

  • The developed multi-agent pipeline effectively automates the creation of SCORM-compliant e-learning courses from enterprise documents.
  • This approach enhances the scalability and consistency of corporate training, particularly in regulated environments.
  • By ensuring content traceability, the system mitigates the risk of factual inaccuracies or hallucinations in training materials.