Strengthening Teachers' Pedagogical Competence through an Integrated AI- and IoT-Based Smart Learning Management System

Abstract

Practical learning in many technical vocational programs remains fragmented and difficult to monitor systematically, limiting student competency development and teachers' data-driven pedagogical decisions. This study aimed to design and evaluate the validity, practicality, and effectiveness of a Smart Learning Management system assisted by Artificial Intelligence (AI)- and Internet of Things (IoT)-based EduKit for strengthening teachers' pedagogical competence and learning quality in Digital Electronics Engineering at SMK Negeri 2 Pekanbaru. Using a Research and Development approach structured around the Define, Design, Develop, and Disseminate (4D) model, the system was evaluated by media, material, and language experts and implemented with 38 students of class XI Industrial Electronics Engineering 2, using validation sheets, teacher and student response questionnaires, and a pretest-posttest test, analyzed through percentage and N-Gain techniques. The system achieved an average validity score of 96%, practicality scores of 95% from teachers and 96% from students, with teacher sub-indicators of 90% for pedagogical-competence support and 95% for learning-quality support, while student outcomes improved from a pretest average of 64.74 to a posttest average of 87.50, yielding a moderate N-Gain of 0.6418. These findings indicate that integrating AI and IoT into a unified learning-management ecosystem is valid, well accepted, and meaningfully effective for advancing data-driven vocational instruction, offering a replicable model for similar technical education settings.