Design and Evaluation of an AI-Based E-Learning System for Graduate Education
TL;DR
Findings indicate that AI-based e-learning enhances personalization, engagement, and data-driven learning in higher education.
Abstract
. The advancement of information and communication technology has driven innovation in higher education, particularly in intelligent learning systems. This study develops an Artificial Intelligence (AI) – based e-learning system to improve learning in the Educational Management Graduate Program at Universitas Negeri Medan. Using a Research and Development (R&D) approach with the ADDIE model, data were collected through interviews, surveys, and curriculum analysis involving lecturers and graduate students. The system integrates adaptive learning recommendations, automated feedback, learning analytics, and an NLP-based chatbot. Results show significant improvements, including increased learning efficiency (58%), faster feedback response time (65%), and high user satisfaction (89%). Interactivity and student motivation also improved by 54% and 61%, respectively. Expert validation through Focus Group Discussion (FGD) confirmed strong system feasibility (92%) and pedagogical relevance (85%). These findings indicate that AI-based e-learning enhances personalization, engagement, and data-driven learning in higher education.