Purpose The purpose of this viewpoint is to highlight the critical role of relational academic advising in an era increasingly shaped by AI-driven learning analytics. It argues that while learning analytics can enhance advising practice, meaningful student support ultimately depends on humanised, dialogic relationships between advisors and students. Design/methodology/approach This is a viewpoint paper which adopts a conceptual and reflective approach, drawing on current literature in academic advising, learning analytics and AI in education. It critiques emerging practices and positions relational advising as a way of keeping data-driven systems grounded in human connection and meaning-making. Findings The paper highlights that learning analytics can strengthen advising by providing data-driven insights but cannot capture the complexity of students’ experiences, which underpin effective academic advising. It argues that advisors must resist becoming mere “humans-in-the-loop” of automated systems and instead be supported to interpret data critically and contextualise it through empathetic, relational dialogue. Originality/value This paper offers a timely contribution by positioning learning analytics not as a substitute for relational advising, but as a prompt for deeper, more humanised conversations with students. It challenges emerging narratives that present AI-driven analytics as inherently more efficient or equitable, and instead argues for a critically engaged and compassionate advising practice that keeps student meaning-making at its centre.
Carpenter et al. (Wed,) studied this question.