IGNinf
Learning intelligence architecture

학습 전 과정을 하나의 지능 레이어로 연결합니다.

과제, 피드백, 학생 신호, 교사의 판단을 연결해 다음 학습이 더 정밀해지는 구조를 만듭니다.

교육기관 파일럿 요청
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.

교육기관 파일럿 요청