Augmenting academic advising in the era of smart cognitive computing technologies creates new dynamics of engagement that can help academic advising evolve toward a learning-centered paradigm. We examined the self-directed learning readiness of students selected through demographic variables. The results indicate that differences in employment experience, length of employment, and grade point average (GPA) are statistically significant with regard to self-directed learning readiness scores. Preparing learners for self-directed processes can augment student development when purposefully positioned in a learning-centered approach with continuity across mobile, cloud, and cognitive technology-based learning platforms and in components of academic advising processes.