Humanising learning analytics in academic advising: how do we balance data and relational advising practice?

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.