APPLICATION OF ARTIFICIAL INTELLIGENCE IN ADAPTIVE PROGRAMMING EDUCATION SYSTEMS

Abstract

This conference paper examines the application of artificial intelligence (AI) in adaptive programming education systems and proposes a pedagogically grounded model for enhancing programming competence in higher education. The study is structured according to the IMRAD format and focuses on the integration of student modelling, learning analytics, recommendation systems, adaptive feedback and teacher dashboards into programming courses. The proposed model is intended to support differentiated instruction, timely remediation, progressive task sequencing and evidence-based assessment. A quasi-experimental design is outlined with a control group taught through conventional methods and an experimental group taught through an AI-driven adaptive environment. Model-based experimental results indicate that adaptive programming education can improve theoretical knowledge, algorithmic thinking, code-writing accuracy, debugging ability and reflective self-regulation. The paper also discusses implementation constraints such as data privacy, algorithmic transparency, infrastructure readiness and teacher digital competence.