Academic advising brings numerous benefits to the mission of Higher Education Institutions. One of the main actors are the advisors who support students in defining appropriate academic roadmaps. One central and challenging duty of academic advisors is course recommendation for term planning. This task requires both knowledge of the study programs and a thorough analysis of the students' unique circumstances. If we add limited time and a large student population, the task becomes overwhelming. As a result, an important body of research has sought to expedite term planning via data-oriented decision-support tools. While the impact of such tools on students has been extensively studied, the advisors' perspective remains largely unexplored. We contribute to fill this gap by studying how a grade prediction tool shapes advisors' course recommendation strategies. Our observations suggest that while the advisors' usual strategies tend to prevail, their recommendations are mostly affected by the advisee's historical performance. CCS Concepts: • Human-centered computing → Empirical studies in visualization.