Pengembangan Responsive Agentic AI-Enhanced Learning Framework untuk Meningkatkan Daya Saing Startup Edtech

Abstract

Advances in artificial intelligence in education open up opportunities for more adaptive and personalized learning. However, EdTech startups still face challenges in the form of high customer churn rates and low user loyalty, which impact business competitiveness. This study proposes the Responsive Agentic AI-Enhanced Learning Framework as a conceptual artifact that integrates Agentic AI capabilities with adaptive learning. The method used is Design Science Research (DSR), which includes problem identification, solution goal setting, design and development, demonstration, logical evaluation, and communication. The objective of this research is to identify the essential elements of Agentic AI in the development of adaptive learning models for EdTech startups. The research results identified six main components, Agentic AI Capability, Adaptive Learning, Personalized Learning Experience, User Engagement, User Loyalty, and Competitive Advantage. Logical evaluation showed that the resulting framework possesses good relevance, consistency, completeness, and usefulness. The study concluded that the integration of Agentic AI has the potential to increase user loyalty while supporting the sustainable competitiveness of EdTech startups.