The neurocognitive mechanisms underlying prevalent learning disorders, such as dyslexia, dyscalculia, and their comorbidity, remain unclear and are the subject of ongoing debate. The core-deficit hypothesis has long been the dominant theory explaining these mechanisms across various learning disorders. However, this hypothesis faces criticism for its limited explanatory power and reliance on a categorical framework. In contrast, a dimensional framework has been proposed as a more robust approach, offering deeper theoretical insights and addressing some of the methodological limitations inherent in the categorical model. In this opinion paper, we also suggest leveraging advanced technologies, such as specific machine learning techniques and simulated data (e.g. digital twins), to further refine our understanding within a dimensional framework. This work contributes to the ongoing discourse on human potential by exploring how neurocognitive diversity can be better understood and supported through advanced methodologies. By shifting towards a dimensional perspective and integrating emerging technologies, we aim to enhance interventions that unlock learning potential across diverse learner profiles.