What OLM is
OLM is not a curriculum. It is a governed vocabulary and structural layer that any program can be expressed in — making the structure of learning inspectable alongside the content, without changing the content.
Every program expressed in OLM consists of the same kinds of building blocks: routines (what learners do), artifacts (what they produce), evidence (what is observed), constraints (what limits the work), and patterns (named clusters of blocks that recur). A program is a specific, versioned composition of these blocks.
The layer model
An OLM program packet has six layers. Each layer serves a distinct audience and is generated from the layers above it.
A hub buyer can inspect the Core Mapping. An educator reads the Educator Brief. A parent reads the Parent Brief. All three documents derive from the same structural source.
The canonical elements
OLM governs a registry of canonical elements — the only valid building blocks for any program mapping. No generated packet may reference an ID not in the registry. This is what makes OLM machine-readable and validator-enforced.
Reference programs
Four programs mapped end-to-end — selected to show that OLM handles real variety across domain, age range, format, and authorship type.
Popcorn Factory
Ages 7–17 · Week-long camp
Teams design, build, and pitch a popcorn product venture. The primary reference implementation — shows OLM from the ground up across a full week-long program.
pattern.project_planning
pattern.prototype_cycle
pattern.capstone_pitch
Homemade Pizza
All ages · 60-minute workshop
Learners explore the history of pizza, then make one. Demonstrates OLM at its simplest — a single-session program with one perishable artifact and a clean inquiry-to-application arc.
routine.new_material
routine.hands_on_activity
artifact.physical_product
CAD Missions
Ages 7+ · Self-paced series
Mission-based 3D modeling using Onshape. Demonstrates OLM on an externally-authored program: one Core Mapping covers the full catalog. Surface-area verification produces the strongest evidence signal in the library.
routine.apply_feature_with_parameters
routine.verify_with_measurement
evidence.numeric_match
OpenSciEd Unit 6.1 — Light & Matter
Ages 11–12 · 6-week unit
OLM as a translation layer over a widely-used NSF-funded curriculum. OpenSciEd's five "routines" become OLM patterns — naming the difference is what makes the translation possible.
pattern.phenomenon_anchor
pattern.investigation_cycle
pattern.consensus_model
What a translation looks like
OpenSciEd uses "routine" to mean "named instructional move." OLM uses "pattern" for the same idea. Naming the difference is what makes the translation possible.
| OpenSciEd construct | OLM translation |
|---|---|
| Anchoring Phenomenon Routine | pattern.phenomenon_anchor |
| Navigation Routine | Ritual — Storyline Navigation (no evidence produced) |
| Investigation Routine | pattern.investigation_cycle |
| Putting the Pieces Together Routine | pattern.consensus_model |
| Driving Question Board | artifact.question_set (existing canonical ID) |
OLM and AI systems
OLM is designed to be legible to both humans and AI systems. Most educational frameworks are too narrative or loosely defined to constrain an LLM. OLM's governed vocabulary and explicit layer rules eliminate the most common failure modes in AI-generated educational content: activities masquerading as programs, skills asserted without derivation, evidence claims without artifacts.
When a model operates within OLM, it cannot hallucinate structural relationships. The constraints become guardrails. This makes OLM a shared semantic layer between humans and AI tools working on the same content.
Contribute
OLM grows through practitioners mapping the programs they actually run. The most valuable contribution is a Core Mapping — a structural description of a real program expressed in canonical OLM vocabulary.
Paste your brief below to build a complete prompt — the canonical registry, layer rules, and your brief — ready to paste into Claude, ChatGPT, or any capable LLM. Generate a Core Mapping for the learning architecture, or a Playbook for the delivery model. Your AI tool, your output, framework-compliant.
If your program uses a routine, artifact, or evidence type with no existing canonical equivalent, propose it. Requires framework fluency and a justification for reusability.
Open a GitHub issue. Lowest barrier, still valuable. Structural feedback helps the registry stay accurate.
Build a prompt from your brief
Paste a program brief and get a complete prompt ready to drop into Claude, ChatGPT, Gemini, or any capable LLM. Choose OLM Mapping for a YAML Core Mapping (the learning architecture), or OLM Playbook for a YAML Playbook (the delivery model). Both are valid contributions to the OLM library. Nothing leaves your browser.
Community
OLM educators and hub operators discuss programs and framework questions in the Meta Humans community forum under the OLM category. Technical contributors work directly through the GitHub repository.