Metadata is becoming increasingly important in the future learning ecosystem. Organizations across the commercial sector, government, and academia are recognizing the immediate and long-term benefits of metadata implementation. One of the most exciting rewards of metadata implementation is one whose limits have yet to be discovered: unlocking the use of machine learning (ML) and the broader range of other artificial intelligence (AI) capabilities for the education and training domain. These rapidly advancing disciplines promise to enhance operational readiness by making full use of the bounteous data sets continuously produced by modern technology, which are far too vast for humans to interpret. Some of the advancements already enabled by AI include real-time content difficulty adjustment, learning path optimization, and competence estimation. Ongoing research continues to expand the realm of possibility.
Several general-purpose metadata standards currently exist, including the Learning Resource Metadata Initiative (LRMI), Schema.org, the Dublin Core Metadata Initiative (DCMI), and Learning Object Metadata (LOM). Most legacy standards, while they still play an important role, cannot accommodate today’s broad range of learning experiences. They often cover only basic attributes and fail to distinguish between various types of learning objects. As the education and training community looks to the future of distributed learning, newer standards must be designed to enable more precise data collection and incorporate non-traditional learning modalities, such as simulations, virtual reality, and mobile content.
This paper summarizes the LRMI Task Group’s primary objectives in its efforts to update and combine LRMI and LOM. Because these efforts have led to major revisions, it explains the key upgrades and how they address significant gaps in older standards. Finally, it details the group’s technical and strategic recommendations, including the updated standard itself along with implementation guidance to facilitate adoption and transition.