Brain–computer interfaces (BCIs) are emerging technologies that enable direct communication between the human brain and external devices. As BCIs transition from laboratory research to educational practice, they offer new possibilities for advancing STEM learning. This study presents a systematic literature review of research on electroencephalography (EEG)-based BCIs applied in educational contexts, following the PRISMA 2020 methodology. A total of 683 records were identified from Scopus and Web of Science, resulting in a final selection of 29 empirical and conceptual studies published between 2020 and 2025. The analysis reveals that BCI applications in education concentrate on four primary domains: (1) attention and engagement monitoring, (2) neurofeedback for inclusion and special education, (3) robotics and engineering practice, and (4) emotion recognition and affective learning. Across these domains, the most frequently explored processes are attention, engagement, motivation, and memory, with consumer EEG devices such as Muse, Emotiv, and NeuroSky enabling real-time monitoring of students’ cognitive states. Studies also integrated machine learning and affective computing to classify emotional states and deliver adaptive feedback in real time. Despite current limitations such as signal quality, electrode calibration, and ethical concerns, the findings suggest that EEG-based BCIs have strong potential to foster personalized, inclusive, and interdisciplinary STEM education, positioning them as transformative technology for future neuroadaptive learning environments.