Probabilistic Machine Learning Approaches for Adaptive User Systems in Developing Economies

Adaptive user systems are becoming increasingly important in modern digital environments because they improve personalization and user interaction. This paper explores probabilistic machine learning approaches for adaptive systems in developing economies. The study focuses on Bayesian-inspired learning systems capable of adjusting applications according to user behavior, educational background, and technological literacy levels. The paper further discusses the relevance of adaptive systems within African digital transformation initiatives.