Model Integrasi Feedback Digital Berbasis AI untuk Pembelajaran Berorientasi Kompetensi

Abstract

The use of artificial intelligence (AI) to provide digital feedback is growing and offers significant opportunities to improve the quality of competency-oriented learning. This article aims to analyze an AI-based digital feedback integration model through an in-depth literature review, examining the role of AI in generating automated, high-precision, adaptive, and data-driven feedback. The study results indicate that AI-based feedback systems can accelerate the formative assessment cycle, improve the accuracy of diagnosing learning difficulties, and facilitate mastery learning by providing personalized learning recommendations. Furthermore, AI contributes to the development of cognitive competencies through the analysis of conceptual understanding, affective competencies through monitoring motivation and engagement, and psychomotor competencies through gesture-based, video-based, or simulation-based performance analysis. This article also formulates an AI-based digital feedback integration model that combines components of learning analytics, competency gap detection, adaptive recommendations, and continuous feedback loops. This study confirms that integrating AI into feedback mechanisms can be a crucial pedagogical strategy for improving learning effectiveness and ensuring more comprehensive competency achievement.