This article seeks to explore different combinations of human and Artificial Intelligence (AI) decision-making in the context of distributed learning. Distributed learning institutions face specific challenges such as high levels of student attrition and ensuring quality, cost-effective student support at scale using a range of technologies, such as AI. While there is an expanding body of research on AI in education (AIEd), this conceptual article proposes that combinations of human-algorithmic decision-making systems need careful and critical consideration, not only for their potential, but also for their appropriateness and ethical considerations. We operationalize a framework designed to consider robot autonomy at four key events in students' learning journeys, namely (1) admission and registration; (2) student advising and support; (3) augmenting pedagogy; and (4) formative and summative assessment. We conclude the article by providing pointers for operationalizing options in human-algorithmic decision-making in distributed learning contexts.