Keyword: learning orientations

ABSTRACT: Learning styles have long been widely used in the educational literature to explain individual differences and to support the personalization of instruction. However, these approaches have been subject to significant empirical and theoretical criticism in recent years, largely due to the assumption that learning styles represent stable and instructionally dominant characteristics. At the same time, the rapid proliferation of artificial intelligence (AI)-supported learning environments has necessitated a reconsideration of learning as an adaptive, contextual, and process-oriented phenomenon. In this context, how AI structures learning experiences and what this implies for the concept of learning styles remain conceptually underexplored. The aim of this study is to critically reexamine the learning styles literature in the age of AI and to discuss how AI-mediated learning environments transform this concept. The study reviews classical approaches to learning styles and the major critiques directed at them, and then examines the underlying logic of personalization in AI-based adaptive learning systems. In the critical discussion, it is argued that the widespread claim that AI “changes” learning styles is based on assumptions that treat learning styles as fixed traits. Instead, learning in AI-supported environments is conceptualized as a dynamic, context-sensitive, and temporally evolving process. The paper proposes that learning styles should be reconceptualized not as stable individual traits but as dynamic learning orientations that emerge within AI-mediated learning processes. It is suggested that this conceptual shift provides a more explanatory framework for understanding learning in contemporary educational contexts and offers important implications for instructional design and future empirical research.