Artificial intelligence (AI) is increasingly taking over decision-making processes in our daily lives. First studies have revealed differences in the perception and evaluation of algorithmic decision-making (ADM) versus human decision-making (HDM) in select contexts. This highlights the importance of comparative assessments of ADM and HDM in the corresponding context prior to the implementation of corresponding AI-based decision-making processes. Here, we investigated the influence of the decision-making mode on justice perception, trust, and emotional responses (satisfaction and outrage) in students who were allocated their university course topics for presentations and projects based on ADM or HDM. Importantly, all topics were allocated according to the sample principles, but lecturers in the HDM condition pretended to decide themselves based on the information of a corresponding preference assessment system, whereas lecturers in the ADM condition explicitly stated that the AI decided. Furthermore, we varied whether the corresponding decision-making process was explicitly communicated as fair or whether no comment was made on the process’ fairness. Against our expectations, Bayesian evidence indicated that whether a human or an AI allocated course topics had no impact on the perceived justice, trust, or emotional responses regarding topic allocation. Conversely, outcome favourability strongly determined students’ evaluations of the allocation process. Although the preregistered stopping rule for data collection has not yet been fulfilled for all effects, the data thus far imply that students perceive ADM the same as HDM. Given that outcome favourability is central, ADMs that can optimize outcomes better than humans might therefore be preferable.