Effectiveness of AI-based microlearning in improving student absorption

Abstract

In the digital era, universities face the challenge of adapting teaching methods to the dynamic needs of students. Microlearning, as a small segment-based approach, is increasingly relevant in improving learning effectiveness, especially with the support of artificial intelligence (AI) that enables personalization of the learning experience. This research uses a qualitative approach with a case study method in several universities that have adopted AI-based microlearning. Data were collected through in-depth interviews, observation of platform usage, and document analysis, and analyzed using thematic techniques. The results show that AI-based microlearning increases student engagement, accelerates concept understanding, and provides flexibility of access to learning. AI also enables personalization of learning materials which has a positive impact on information retention. However, challenges such as lecturer readiness, infrastructure, and ethical use of AI remain a concern. The implications of this study highlight the need for policies and training to optimize the integration of AI in microlearning, in order to create an adaptive, inclusive, and efficient learning environment in higher education.