This review systematizes contemporary approaches to monitoring and adaptively supporting students’ cognitive states through the integration of Brain–Computer Interfaces (BCIs) and Artificial Intelligence (AI). In response to increasing informational and cognitive demands in modern educational environments, particular attention is given to real-time assessment of attention, cognitive load, and fatigue. The analysis synthesizes recent hardware configurations and algorithmic paradigms, including deep learning models, Transformer-based architectures, and latent representation methods for neurophysiological signal processing. Through a cross-level comparison of prior studies, the review highlights a recurring portability–precision trade-off, wherein the requirement for rapid, unobtrusive classroom deployment conflicts with the spatial and temporal resolution needed for accurate neural inference. Drawing on benchmarks reported in the literature, effective educational BCI systems are shown to require end-to-end processing latencies on the order of hundreds of milliseconds to support meaningful cognitive adaptation. In addition, a comparative examination of open-access datasets–COG-BCI, EEGMAT, CLARE, and UNIVERSE–motivates a hybrid development strategy that combines high-fidelity laboratory data for feature discovery with naturalistic datasets for robustness and generalization testing. Collectively, these insights provide a grounded conceptual basis for the near-term development of neuro-adaptive educational systems that responsively align instructional dynamics with learners’ cognitive states.