VERACREDENTIALS FOUNDATION

Learning Context Model™ (LCM)

The foundation that makes AI assessment consistent, explainable, and defensible.

VeraLearning's Learning Context Model (LCM) encodes the competencies, expectations, and standards an organization already uses and provides the shared context an AI system needs to apply them consistently across learners, interactions, and time.

LCM provides a persistent structure that guides how AI conducts learning interactions, interprets learner behavior, and accumulates evidence in alignment with established skill maps and competency definitions.

This allows VeraCredentials to produce assessments that are consistent, transparent, and defensible within real organizational and regulatory contexts.

LCM provides a stable learning context by explicitly modeling:

competencies and skill boundaries

performance expectations and criteria

acceptable evidence types

evaluation logic

This enables assessments that are consistent across learners, explainable to stakeholders, and defensible in institutional settings.

Without vs with LCM

Without the Learning Context Model

AI relies on prompts and retrieved text

Evaluation varies by interaction

Decisions are difficult to explain or audit

Evidence is fragmented

With LCM

AI reasons within defined expectations

Evaluation is consistent across learners

Decisions trace back to criteria

Evidence accumulates coherently

In short

Without context, AI guesses.
With LCM, AI reasons.

What LCM Enables and Produces

With LCM in place, VeraCredentials:

conducts adaptive, competency-aligned interviews

evaluates mastery using consistent, explicit criteria

generates evidence suitable for review and validation

supports pilots and early adoption without sacrificing rigor

This produces

Decision-ready artifacts you can review and share:

Structured assessment snapshots

Explainable mastery decisions

Shareable evidence trails

Verifiable credentials (when applicable)

How LCM is different

The core reasons LCM enables trustworthy assessment.

Models learning, not documents

Captures instructional intent and expectations, not just source text.

Built for judgment, not retrieval

Defines how evidence is interpreted against standards and competencies.

Persists across interactions

Maintains context across turns so evidence accumulates coherently.

Makes decisions transparent

Every decision traces back to defined criteria for auditability.

Model-agnostic by design

Works across AI providers, avoiding vendor lock-in.