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Learning-context personalization engine: mastery, spacing, and help-seeking signals guide when and how AI assistance is delivered

Design · All designs
Evidence · no claims cited

Description

The article's learning-context modeling approach maintains student-level learning state to tailor support and sequencing. The engine 'aggregates signals such as mastery by concept, attempt history, time-on-task, spacing intervals, and help-seeking behavior', and on that basis determines when to recommend review, when to escalate to teacher attention, and what scaffolding type fits the student's current context. This operationalizes feedback, self-regulated help-seeking, and spaced-practice mechanism families from the learning sciences inside the LMS.

Design Implications

Context

Requirements

  • Item-level assessment so performance can be interpreted at the level of specific concepts and skills
  • Logging of attempts, revisions, hint usage, and AI interaction metadata

Constraints

Target Learners

  • Middle school students

Learning Goals

  • Skill mastery and retention
  • Self-regulated learning and effective help-seeking

Claims

Examples

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Key Sources

  • Misan Paul Etchie, Olutosin Taiwo. (2026). AI-Integrated Learning Management System for Middle School: A Longitudinal Study of Learning Outcomes Through High School and Beyond. https://arxiv.org/abs/2606.07544
Page metadata
Field Value
type design
id learning-context-engine-mastery-spacing-helpseeking
title Learning-context personalization engine: mastery, spacing, and help-seeking signals guide when and how AI assistance is delivered
description The article's learning-context modeling approach maintains student-level learning state to tailor support and sequencing.
status review
generated by process:settle-candidates on 2026-10-10
trust unverified — no one has confirmed this page's content
sources 1 — misan-paul-etchie-2026

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