About this prototype
This site is a prototype learning-engineering product under active development. Its content is cloned from the lebok.wiki of record — the Learning Engineering Body of Knowledge published by INFERable (a Public Benefit Corporation) under copyleft terms — and then re-presented here as an experiment in transparent, source-grounded authorship.
It is not authoritative. Please treat it accordingly:
- The lebok.wiki of record is the canonical source. Where this prototype and the of-record wiki disagree, the of-record wiki wins. This deployment may lag, diverge, or contain errors introduced during processing.
- Much of the prose is machine-drafted. Pages may carry one of three provenance banners — Draft (assembled from cited community-corpus items), AI-synthesized (AI-written prose grounded in corpus metadata), or Authored from the Learning Engineering Toolkit (written from the source text of Goodell & Kolodner's Learning Engineering Toolkit, 2023, with per-claim citations).
- "Pending expert review" means what it says. Grounded and cited is not the same as verified. No claim here should be cited as authoritative until a qualified reviewer has signed off. Every page shows its confidence level and its sources so you can judge for yourself.
- This is a demonstration, not an official publication of the LEBOK, ICICLE/IEEE, INFERable, or the Learning Engineering Toolkit's authors and publisher. Names and works are cited for scholarly attribution only.
If you spot an error, use the Edit button on any page to propose a correction — that is exactly what the prototype is for.
Pedagogy
This prototype is itself a small piece of learning engineering, so it tries to practice what the body of knowledge preaches. A few pedagogical commitments shape how the material is presented:
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Epistemic transparency over false authority. The most important thing a reference can teach a learner is how much to trust it. Rather than hide uncertainty behind a confident tone, every page surfaces its provenance, its confidence level, and its sources. Learning to weigh evidence is part of the curriculum, not a distraction from it.
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Source-grounded claims. Substantive assertions are tied to specific, checkable sources — landmark papers, the community corpus, or a named chapter of the Learning Engineering Toolkit — with inline citations that resolve to a reference list on the page. This models the field's own commitment to evidence-based, data-informed practice and lets a reader trace any claim back to where it came from.
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Worked structure and scaffolding. Content is organized into the twelve Knowledge Areas and broken into small, focused pages so a newcomer can build a schema incrementally rather than face an undifferentiated wall of text. Foundational concepts precede the tools that apply them, and cross-references connect related ideas so knowledge is encountered as a connected network.
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Retrieval and reinforcement. Site-wide search, a glossary, and consolidated references support looking things up and re-encountering key terms in multiple contexts — a nod to the retrieval practice and spaced, varied exposure that the learning sciences show strengthen durable memory.
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Human-in-the-loop by design. The Edit button, the "pending expert review" flags, and the visible corpus links treat readers as contributors. The intended learning loop is not "read and accept" but "read, question, verify, and improve" — the same iterative, feedback-driven cycle that defines the learning-engineering process the wiki describes.
In short: the pedagogy is to teach the content and model the discipline at the same time — to show, in how the site behaves, the evidence-based and iterative habits that learning engineering asks of its practitioners.