This paper critically examines the validity of the learning styles hypothesis within the context of enhancing educational experiences through an adaptive machine learning extension for Learning Management Systems (LMS). In pursuit of identifying effective, evidence-based strategies for improving instructional delivery in digital learning environments, this study critically examines prevailing assumptions—particularly the claim that aligning instructional methods with individual learning styles significantly enhances student outcomes.

To ensure a robust and credible analysis, the study employs a comprehensive literature review methodology, systematically evaluating empirical research both in support of and against the learning styles framework. By exploring the theoretical foundations, methodological issues, and practical implications associated with learning styles, the paper offers a balanced and nuanced discussion of their potential role in educational design.

The findings of this research seek to clarify whether integrating learning styles into LMS features yields measurable benefits to the learning process. The study concludes with recommendations for future research, underscoring the importance of empirical validation in the development of innovative, learner-centered educational technologies. Overall, this work aims to contribute to the academic discourse on adaptive learning by promoting data-driven approaches that account for the diversity of learner needs in modern educational settings.

Keywords: Personalized education, Learning Management system, Learning style hypothesis validity, Digitalization, education.