didactical.ai

Didactical · makers of LORE

A course explains each thing once. LORE explains it again, a different way, until it lands.

Every training course ever written picks one explanation per idea and hopes. The learners it doesn’t reach get the same words again, slower. LORE was built the other way round: it carries several routes to the same fact, and when someone misses a question it teaches that point again by a different route — narrated, in a voice that sounds like a person, in the moment.

Then it records which explanation finally worked — for that person, and across the whole cohort.

1 doc
Is all a course needs to start
5
Different routes to the same fact
576
Course corrections sized for one 16-lesson build
~18 min
Of GPU to narrate them, from measured speed

What we do

From the document you have to a record of what your people actually understood.

The didactic method — one instructor, one lecture, one pace for everyone — does not survive contact with a workforce. LORE keeps the instruction and drops the single pace.

  1. 1

    Bring the document

    A manual, a guidance document, a standard operating procedure. PDF or text, up to 40 MB.

  2. 2

    LORE builds the course

    Lessons in order, with every question tied to the exact passage of the source it came from.

  3. 3

    Your instructors decide

    Your staff read the draft before it goes out — or choose to let a course publish on its own.

  4. 4

    Learners listen

    Each lesson narrated, in the browser on any phone or on Android (iPhone app coming soon), with every word highlighted as it’s spoken.

  5. 5

    A miss says something specific

    Every wrong answer is a named misconception, written from the passage that warns against it.

  6. 6

    LORE teaches it again, differently

    A new explanation on that exact point plays next — and which one worked is the thing worth knowing.

When it doesn’t stick

Re-explaining the same way, slower, is what a bad teacher does.

Each route is a different cognitive path to the same fact — not a restyled version of the same sentence. However an explanation is produced, it is checked against the approved script before it reaches a learner; anything the lesson does not say is flagged or refused.

Plain words
The rule again, plainly, in the lesson’s own terms.
Side by side
What they chose next to what the procedure asks for — “you chose consent; the procedure asks for containment.”
A worked example
The rule applied to one concrete case, start to finish.
Step by step
The concrete steps to take, in that moment, at that bench.
A story
A short scene from the lesson’s own material, where getting it wrong has a cost.

Why the voice has to be this good

A course that keeps finding new words needs a narrator who can say them.

Hundreds of alternative explanations only exist as a product if narrating one costs nothing but machine time. Thirteen corrections rendered in twenty-three seconds — six times faster than real time — which is why a whole course’s adaptive layer is minutes of GPU rather than a studio calendar. And a voice a learner will hear that often has to be one they can stand for an hour.

  Hosted voice API LORE
Who the voice is A stock voice from a shared library, or a clone you upload. Your learners may have heard it selling software last week. Craig and Randi. Professional broadcast announcers, named, who agreed to teach this material in their voices.
What it learned from Largely read-aloud corpora — audiobooks and studio reads. The model learns what reading sounds like. Broadcast voice-tracking: working announcers performing to an audience. The model learns what talking sounds like.
Inflection and emphasis Tuned per request, sentence by sentence. Stress lands where the model guesses, usually mid-sentence. Trained on long segments, so phrasing carries across a paragraph — the emphasis falls on the word the sentence is about.
How the audio gets made Chunk the script, call the API, stitch, listen for the seams. A human narrator means a booked session and an edit. Approved text renders straight through on the first pass. No session to book, no second take, no punch-ins.
Where the narration is made On the vendor’s servers. Your lesson scripts go with it. On a GPU we control, against our own model weights. Approved scripts are narrated here, and finished audio stays on infrastructure we run.

Listen

A full lesson, start to finish, unedited.

Randi reading section 1.7.3 of Applied Engineering Principles — shipboard ungrounded electrical systems — exactly as LORE built it. Two and a half minutes off the renderer in one pass; nothing re-recorded, punched in or cut. Listen for where she leans on a word.

Randi Section 1.7.3

Shipboard Ungrounded Electrical Systems

0:002:26
“Your ship’s electrical system is ungrounded. So, no path to ground, right? … It sounds like solid logic. It is a deadly mistake.” Lesson script · drafted from the source, approved by a human

The same material, talked through

And the episode that hands off to it.

The rest of section 1.7 — what current does to a body, and the rules for changing a fuse — as a two-host episode. It closes by pointing at the lesson above: “Next episode: the myth about ungrounded systems that has gotten people killed.” Same manual, same approved script, second register. Three and a quarter minutes, unedited.

Craig & Randi Section 1.7

Ep. 8 — Electrical Safety

0:003:17

RandiAnd do not cut the wire.

CraigNever. The flash and the whipping ends make you the second victim.

Episode script · drafted from the source, approved by a human

Where the read comes from

The same voices, doing the job they trained on.

Before it narrated a lesson, this voice read live radio — retail copy, forecasts, breaks. That is the performance the model was built from, and it is why the instruction above doesn’t sound read. Three reads, unedited.

Randi Retail spot

Hometown Auto Group

0:000:27
“Doors open Saturday at nine a.m. sharp — and the first twenty-five buyers get a five-hundred-dollar gift card.” Broadcast read · one pass, no edit
Randi Forecast

Hometown Forecast

0:000:13
“We’ll top out at eighty-four this afternoon, down to sixty-one overnight.” Broadcast read · one pass, no edit
Randi Break

Coming Up After the Break

0:000:06
“Brand new music, plus your chance to win tickets. Stick around.” Broadcast read · one pass, no edit

Sources: Applied Engineering Principles, chapter 1 · broadcast copy · professional voice talent · rendered on our own GPU

What it learns

Which explanation worked is a more useful fact than who got it wrong.

Because the same idea is taught several ways, LORE can record not just that someone missed a point but which route finally reached them. Roll that up across a cohort and a raw pass rate separates into four very different situations that look identical from the outside.

01

A broken question

The students who did well overall got this one wrong more often than the students who did badly. A miskeyed or ambiguous item can carry a perfectly unremarkable pass rate and be invisible to any amount of reading — and obvious to the statistic.

02

Something the lesson never taught

Failed first time, passed after the correction. The retry is carrying the module, which means the script has a defect with a specific address — this is the signal that turns an adaptive loop into a course-improvement loop.

03

A genuinely hard concept

Low pass rate, and the retry is no better. Nothing is broken here — the topic simply deserves more of the course than it is getting.

04

A question doing no work

Everybody passes it. It distinguishes nobody, and it is taking up a slot that could be telling you something.

Two things we will not do with this data. We do not hide the sample size: item statistics are noisy below thirty responses and meaningless below ten, so every figure carries its n and anything under the floor is reported as provisional. A course change made on six answers is a guess with a decimal point on it. And we do not sort people into learning styles — the visual/auditory/kinaesthetic typology does not survive testing. What gets recorded is observed behaviour: which explanation this person responded to, on this concept.

Straight about the state of it

What runs today, and what we are still building.

Working now

  • Document to published courseLessons and questions drafted from the source, held at a human review gate, narrated and published.
  • Narration on our own hardwareWhole courses rendered in one pass in Craig’s or Randi’s voice, verified lesson by lesson before anything attaches.
  • Listening, checks and correctionsLecture and podcast playback, with short checks after each lesson and a corrective lesson on a missed point.
  • Grounding enforced in codeNo question is published unless it quotes the approved script verbatim, and a correction is refused if it brings in a number, unit or named term the script does not contain.
  • Fast narration of correctionsThirteen rendered in twenty-three seconds; at that speed about eighteen minutes of GPU would cover a sixteen-lesson course’s full set.
  • Corrections written on the spotWhen a learner has had every prepared correction and it still has not landed, LORE writes a new one for exactly what they missed, in a different way, checked before they hear it and shown to their teacher.
  • Recording which route landedEvery correction a learner gets, and whether it stuck, is recorded; the next one is chosen by what has worked for them and their class.
  • Two-host podcast for every lessonCourse building writes a two-host episode for each lesson from the approved script; an episode that fails the check leaves that lesson a lecture.

In development

  • Cohort item analysis in the consoleThe statistics exist and are verified against a planted defect; surfacing them to instructors does not yet.
  • Lesson videoBuilt and rendering in testing; not yet switched on for learners.
  • Conversational examinerA spoken back-and-forth that checks understanding, not recall.
  • Running fully on customer hardwareNarration already runs on GPUs we control; course writing still uses an outside AI model.

Bring us the course people keep failing.

Send the manual you’d start with. A pilot is one document, a small group of learners, and a few weeks.