MRGEN: A Conceptual Framework for LLM-Powered Mixed Reality Authoring Tools for Education
Summary: arXiv:2604.15341v1 Announce Type: cross
Abstract
Mixed Reality (MR) offers immersive and multimodal opportunities for education but remains difficult for teachers to author without technical expertise. We propose MRGEN, a conceptual framework for LLM-powered authoring tools to support teachers in creating MR learning activities that work on mobile devices (tablets and smartphones). MRGEN articulates three axes: Learning Objectives, MR Modality, and GAI Assistance.
Introduction
The integration of Mixed Reality (MR) in educational settings has the potential to revolutionize how instructors deliver content and engage students. However, the complexity of creating MR experiences often discourages teachers, especially those without a strong technical background. To address this challenge, we introduce MRGEN, a framework designed to empower educators with the tools they need to create effective MR learning activities.
Framework Overview
MRGEN is structured around three primary axes:
- Learning Objectives: This axis focuses on aligning MR activities with specific educational goals, ensuring that the content is not only engaging but also pedagogically sound.
- MR Modality: This dimension explores the various modalities available in MR, such as augmented reality (AR) and virtual reality (VR), allowing teachers to choose the most suitable format for their lessons.
- GAI Assistance: Generative AI (GAI) plays a crucial role in aiding teachers throughout the authoring process, offering suggestions and automating certain tasks to streamline content creation.
Prototype Implementation
To validate the MRGEN framework, we developed a prototype using the open-source MIXAP authoring platform. Our prototype enables teachers to easily create MR learning activities tailored to their specific needs, utilizing the three axes of our framework.
User Study
We conducted a user study involving 24 participants, comprising educators with varying levels of experience in MR technology. The objective was to assess the effectiveness of the LLM-powered authoring tool in reducing the time spent on task completion.
- Results: The findings revealed that the integration of LLM assistance reduced task duration by an average of 36%.
- User Feedback: Over 90% of participants reported that the AI support was helpful for brainstorming, structuring, and aligning content with their learning goals.
Conclusion
The promising results from our study indicate that LLM-powered authoring tools, such as those envisioned in the MRGEN framework, can significantly enhance the ability of teachers to create immersive MR learning experiences. As educational technology continues to evolve, the potential for AI-assisted authoring tools in engaging learners and facilitating effective teaching methods will only grow.