Personalized Mathematics Instruction through Artificial Intelligence: A New Pedagogical Approach
Year: 2026
Type: article
Abstract: This repository contains the camera‑ready research package for a study on AI‑driven personalized mathematics instruction in secondary education. The package includes the manuscript (JOIV/IEEE style), figures (conceptual framework, related‑work synthesis, methodology pipeline, discussion map), and tables (descriptive, inferential framework, derived improvement metrics), together with analysis templates for reporting ANCOVA/mixed‑effects, Cohen’s d, partial η², GLMM outcomes, and robustness checks. The methodology integrates a data pipeline (collection → preprocessing → supervised modelling → teacher‑centred recommendations) and a pre‑registered statistical analysis plan (ANCOVA with pre‑test covariate; mixed‑effects with class random intercept; nonparametric checks; effect‑size reporting). The ethics‑by‑design section summarises consent, pseudonymisation and storage controls. Figures are provided in high‑resolution PNG (print‑friendly variants available). Tables are included in DOCX and CSV templates to ease journal submission. This record is designed to support reproducibility, transparency, and classroom transfer.
Author: AITS
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W7125615159
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