Seeing what students learn: assessing cognitive representations and academic outcomes via EEG image reconstruction

Abstract Understanding how humans process visual information during learning remains a central challenge in cognitive neuroscience. Traditional approaches typically rely on behavioral observations or indirect neural measures, which limits direct insight into learners’ internal cognitive representations. Recent advances in brain–computer interfaces (BCIs) and deep learning have enabled image reconstruction from electroencephalography (EEG), providing a promising pathway to decode latent cognitive states. In this study, we investigate how EEG-based image reconstruction relates to students’ learning. While students watched educational videos, we recorded EEG signals and used an image reconstruction model to generate images reflecting their perceived visual representations. We assessed reconstruction quality using semantic similarity between reconstructed and original images. First, we examine whether semantic similarity is associated with overall academic performance and find a significant positive association in our sample. Second, we evaluate item-level effects and show that questions answered incorrectly correspond to significantly lower semantic similarity than correctly answered questions. Third, we analyze the temporal dynamics of reconstruction and demonstrate that temporal misalignment patterns—quantified through dispersion-related measures—are associated with learners’ performance. Overall, these exploratory results provide preliminary evidence that EEG-based image reconstruction is sensitive to both the strength and the temporal evolution of learners’ cognitive representations. If replicated in larger and more diverse samples, this approach may offer a promising direction for studying learning processes and for informing more personalized, data-driven educational interventions.