Key Features of the Learning Graph

• Decentralized Connections:

  • Learners, educators, and institutions can establish direct relationships without relying on a centralized intermediary.

  • These connections are stored on-chain, ensuring portability and interoperability across platforms within the DLP ecosystem.

• Personalized Educational Feeds:

  • Learners can follow educators to stay updated on their latest courses, research, and announcements.

  • Similarly, educators can build a network of followers, showcasing their content and expertise to a global audience.

• Collaborative Learning:

  • The Learning Graph facilitates peer-to-peer collaboration, enabling learners to form study groups, join projects, or co-create content.

  • Educators can also collaborate with peers or institutions to design interdisciplinary courses or conduct research.

• Skill-Based Recommendations:

  • Using data from the Learning Graph, AI agents can recommend relevant courses, collaborators, or study groups based on a user’s profile, skills, and learning goals.

  • For example, a learner interested in AI ethics may be connected to a renowned educator specializing in the field.

• Portability Across Platforms:

  • The Learning Graph ensures that users retain their networks and relationships even when transitioning between platforms.

  • For instance, a learner who moves from one decentralized platform to another can carry their entire educational network and progress without starting over.

• Reputation Building:

  • As part of the Learning Graph, learners, educators, and institutions can earn reputation tokens based on feedback, ratings, and achievements.

  • A high reputation score increases visibility and trust within the ecosystem.