<p>Predicting students at risk of dropping out and intervening on them has become an important challenge, in which Artificial Intelligence techniques have shown promise. However, many teachers and other stakeholders are not experts in those techniques and face notable technical obstacles accessing them. To make Artificial Intelligence more accessible for the prediction of and the intervention on at-risk students, we propose the integration of Automated Machine Learning and Explainable Artificial Intelligence techniques into a Visual Interactive Dashboard to automate the entire process. Our objective is to allow non-expert users to easily obtain predictions from data that will help make decisions to avoid student dropout. Our dashboard tailored the interface providing two views (denoted basic and advanced) intended to be used for beginners and intermediate users respectively. In this paper we describe a case study in which a group of 49 users ran the Dashboard with a public dataset with the assignment to take action about student dropout. The Dashboard demonstrated promising performance. All the advanced interface users and 87.5% of the basic interface users completed the required task successfully in 4 to 6&#xa0;min. A system usability evaluation produced scores of 78.7 and 74.5 out of 100 for the advanced and basic interfaces respectively. Finally, there were no significant differences between the two groups in terms of task success, time on task, or usability. These results demonstrate a contribution of the presented approach in the accessibility process of non-expert users for addressing the problem of student’s academic dropout.</p>