LIF tools provide reusable infrastructure that eliminates the need to rebuild data integrations, simplifying data access and enabling a consistent interface for learner data across systems.
Shared Learner Data Model
The shared learner data model defines how learner information is structured when exchanged between systems and applications. It supports translation across existing standards rather than replacing them.
How it supports:
Establishes a consistent structure for learner data across initiatives, reducing the need for repeated integration planning.
Translator
The Translator maps data from source systems into the shared learner model. Mappings are reusable and can be updated as systems and standards evolve.
How it supports:
Reduces repeated integration work when backend services are added or upgraded.
Metadata Repository (MDR)
The Metadata Repository manages data schemas–both standards and bespoke models–to allow exploration, inspection and mapping between them.
How it supports:
Supports new upstream sources, both internal and external, with human-verified mappings into the shared learner data model.
Orchestration Layer
The orchestration layer manages application requests for learner information. It:
- Determines which systems to query
- Retrieves approved data
- Assembles a consistent response
Data remains in source systems. LIF does not create a central data store.
How it supports:
Extracts data from upstream systems based on configuration and MDR mappings as opposed to custom integrations.
MCP Server
The MCP Server makes LIF learner data directly available to AI assistants and agentic applications through the Model Context Protocol, an emerging open standard for connecting AI models to external systems. Because the tools are generated from the MDR, an AI client automatically picks up new fields or sources as the data model evolves.
How it supports:
Lets AI applications work with learner records through a standard protocol, without each one building a custom integration against the underlying LIF APIs.
How the Components Work Together
When an application requests learner information:
The Query Planner receives the request from one of our API services
Query Planner checks the LIF Cache; if record(s) not found the data is requested from the Orchestrator
The Orchestrator starts a job to collect the data across one or more source systems
The source systems are queried and provide appropriate data to the job
The Translator receives data from the job and translates it into LIF record fragments
The MDR provides the Translator with the mappings from each source to our data model
The Translator provides the job with the data translated into LIF record fragments
LIF record fragments travel back from the job back to Query Planner
Query Planner take the LIF record fragments, stores them and the aggregated record in the LIF Cache, then responses to the request
Each component addresses a specific challenge—translation, coordination, and consistency—without replacing existing institutional systems.