Adaptive Management Systems (ALMS) are an evolving class of educational technology designed to deliver personalized, data-driven learning experiences. Traditional Learning Management Systems (LMS) typically provide static content, offering limited flexibility to adapt to individual learner needs. In contrast, ALMS utilize artificial intelligence (AI) and machine learning algorithms to analyse student data and dynamically adjust instructional materials, pacing, and assessments to better match each student’s unique strengths, weaknesses, and learning preferences. This paper explores the structure and function of ALMS, focusing on key components such as adaptive content delivery, real-time feedback, and predictive analytics. By analysing a range of student behaviours including response times, quiz scores, and engagement patterns ALMS can create a tailored learning path that maximizes retention and understanding. This approach aligns with principles of personalized learning, where educational materials and activities are continuously adapted based on individual progress, fostering a more efficient and supportive learning environment. Further, this paper investigates the pedagogical theories and cognitive models that drive adaptive learning, such as constructivist learning theory, zone of proximal development, and scaffolding.