Research program
Precision Education treats the person, their context, and the evidence as the unit of learning rather than the course. The right knowledge, to the right person, in the right context, with measurable results. It covers contextual micro-content, delivery tied to workflow tasks, observed consumption and comprehension, proof of application in real work artifacts, and closed-loop recommendation driven by durable learner state.
Does delivering the right knowledge to the right person in the right context, and checking it in their real work, produce measurable results that a course does not?
Program
Training is treated as a broadcast: publish the course, record the completion, hope for the change. We treat human knowledge as a control loop instead, where every step from delivery to outcome produces evidence and the evidence decides what the person gets next.
The reference implementation of the closed loop: make a knowledge-sharing request explicit, observable, and answerable, so delivery, consumption, comprehension, application, and outcome stay separate claims with separate evidence.
An online course platform could watch how each learner is doing and send a short explanation, an example or a practice item at the point where that learner struggles, instead of sending everyone through the same lessons.
A company could give its staff an assistant that reads where they are and what they are doing (the job, the screen, the place) and offers a short instruction for that step, so people learn while they work instead of in a separate course.
A school or an online learning company could give students a chatbot that, instead of writing a general answer, finds the short part of the course material that fits the question and the assignment the student is working on, and sends that.
12 records across the register and the writing.