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A Multi-Agent Framework for Democratizing XR Content Creation in K-12 Classrooms

Yuan Chang, Zhu Li, Jiaming Qu

Abstract

Generative AI (GenAI) combined with Extended Reality (XR) offers potential for K-12 education, yet classroom adoption remains limited by the high technical barrier of XR content authoring. Moreover, the probabilistic nature of GenAI introduces risks of hallucination that may cause severe consequences in K-12 education settings. In this work, we present a multi-agent XR authoring framework. Our prototype system coordinates four specialized agents: a Pedagogical Agent outlining grade-appropriate content specifications with learning objectives; an Execution Agent assembling 3D assets and XR contents; a Safeguard Agent validating generated content against five safety criteria; and a Tutor Agent embedding educational notes and quiz questions within the scene. Our teacher-facing system combines pedagogical intent, safety validation, and educational enrichment. It does not require technical expertise and targets commodity devices.

A Multi-Agent Framework for Democratizing XR Content Creation in K-12 Classrooms

Abstract

Generative AI (GenAI) combined with Extended Reality (XR) offers potential for K-12 education, yet classroom adoption remains limited by the high technical barrier of XR content authoring. Moreover, the probabilistic nature of GenAI introduces risks of hallucination that may cause severe consequences in K-12 education settings. In this work, we present a multi-agent XR authoring framework. Our prototype system coordinates four specialized agents: a Pedagogical Agent outlining grade-appropriate content specifications with learning objectives; an Execution Agent assembling 3D assets and XR contents; a Safeguard Agent validating generated content against five safety criteria; and a Tutor Agent embedding educational notes and quiz questions within the scene. Our teacher-facing system combines pedagogical intent, safety validation, and educational enrichment. It does not require technical expertise and targets commodity devices.

Paper Structure

This paper contains 8 sections, 2 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methods
  4. Design Process
  5. System Architecture Overview
  6. Implementation of Agents
  7. Illustrative Walkthrough
  8. Discussion

Figures (2)

  • Figure 1: System architecture overview.
  • Figure 2: System interface. (a) The user can configure education level, subject, and topic of interest, which will be passed to the pedagogical agent. (b) The generated 3D model generated and checked by the execution and safeguard agents. (c) Teaching materials (e.g., introduction and quiz questions) generated by the tutor agent.