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Probabilistic Machine Learning Approaches for Adaptive User Systems in Developing Economies

Year: 2026

Type: preprint

Abstract: 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.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.20284696

Author: KAJASICHE, LAWRENCE

Language: English

Topic: Intelligent Tutoring Systems and Adaptive Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

OpenAlex ID: W7161734929

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