ARTIFICIAL INTELLIGENCE BASED EDUCATION SOFTWARE FOR PERSONALIZED LEARNING AND STUDENT PROGRESS PREDICTION

Abstract

Artificial intelligence-based education software supports personalized learning and student progress prediction by analyzing learner behaviour, assessment results, attendance, engagement, and course activity. The system adapts lessons, practice exercises, difficulty levels, and recommendations according to individual learning needs. Machine learning models help educators identify knowledge gaps, predict academic performance, and detect students who may require additional support. Real-time dashboards provide insights into participation, assignment completion, subject mastery, and progress trends. Automated feedback and intelligent tutoring tools improve learner guidance while reducing routine instructional workload. Integration with learning management systems, digital content platforms, and student databases improves data consistency and academic coordination. Overall, the software can strengthen student engagement, improve learning outcomes, support early intervention, and enable more effective personalized education.