From Learning-Style Neuromyths to AI-Enabled Personalisation for Teacher Preparation

Abstract

Teacher-education programmes increasingly turn to AI to personalise learning, yet many designs still inherit assumptions from learning-styles (LS) models. This review synthesises 63 empirical studies from Scopus and Web of Science using a PRISMA-guided process to clarify what LS research in teacher education shows and how these insights should redirect AI-enabled personalisation. We find widespread declared support for LS (77.4% of studies) alongside limited comparative testing against alternative frameworks (51.6%), and substantial heterogeneity of instruments and methods that undermines comparability. Notably, the intersection of “personalised learning” and LS is comparatively small, suggesting a weak empirical basis for LS-driven personalisation. Concerns about LS as a neuromyth, and calls to bridge neuroscience and educational practice, recur across the corpus; the literature also points to conceptual-change strategies that can reduce erroneous beliefs among pre- and in-service teachers. Taken together, the evidence does not support style-matched instruction. We therefore argue for AI-enabled personalisation anchored in observable performance, strategy use, and self-regulation, with transparent evidence pipelines and attention to workload and equity. This contribution reframes personalisation for teacher preparation beyond fixed style labels and outlines a realistic route for school improvement in an AI-intensive educational landscape.