IGNinf
Learning intelligence architecture

Une couche d’intelligence sur tout le parcours d’apprentissage.

Missions, feedback, signaux apprenants et jugement des éducateurs deviennent un système connecté.

Demander un pilote institutionnel
Assignment Context01
AI Feedback02
Learning Graph03
Coach Exception Queue04
Next-Best Task05
ARCHITECTURE

Five layers. One compounding learning system.

Every new submission can improve the learner model, the educator decision and the next assignment—without turning the student into a passive recipient.

01

Assignment Context

Goals, rubric, exam, school, subject and workload enter before the AI evaluates a response.

02

Feedback Engine

Reasoning, task fit, expression, scoring risk and self-correction are separated instead of collapsed into one generic comment.

03

Learning Graph

Recurring errors and strengths become longitudinal learning signals—not disposable reports.

04

Human Exception Layer

Educators see ambiguity, repeated failure, important moments and emotional context—not every machine-readable detail.

05

Next-Best Task

The system uses prior learning evidence to make the next task smaller, sharper and more useful.

PRINCIPLE

Human agency stays intact.

AI should increase a learner’s ability to think, explain and self-correct—not create dependence on answers.

NEXT STEP

Start with one real learner or one real workflow.

HAOLLA is designed to earn trust through measured use—not through exaggerated claims.

Demander un pilote institutionnel