The ability to measure students’ engagement in an educational setting may facilitate timely intervention in both the learning and the teaching process in a variety of classroom settings. In this paper, a real-time automatic student engagement measure is proposed through investigating two of the main components of engagement: the behavioral engagement and the emotional engagement. A biometric sensor network (BSN) consisting of web cameras, a wall-mounted camera and a high-performance computing machine was designed to capture students’ head poses, eye gaze, body movements, and facial emotions. These low-level features are used to train an AI-based model to estimate the behavioral and emotional engagement in the class environment. A set of experiments was conducted to compare the proposed technology with the state-of-the-art frameworks. The proposed framework shows better accuracy in estimating both behavioral and emotional engagement. In addition, it offers superior flexibility to work in any educational environment. Further, this approach allows a quantitative comparison of teaching methods.

When an action is familiar, we are able to anticipate how it will change the state of the world. These expectations can result from retrieval of action-outcome associations in the hippocampus and the reinstatement of anticipated outcomes in visual cortex. How does this role for the hippocampus in action-based prediction change over time? We use high-resolution fMRI and a dual-training behavioral paradigm to examine how the hippocampus interacts with visual cortex during predictive and nonpredictive actions learned either three days earlier or immediately before the scan. Just-learned associations led to comparable background connectivity between the hippocampus and V1/V2, regardless of whether actions predicted outcomes. However, three-day-old associations led to stronger background connectivity and greater differentiation between neural patterns for predictive vs. nonpredictive actions. Hippocampal prediction may initially reflect indiscriminate binding of co-occurring events, with action information pruning weaker associations and leading to more selective and accurate predictions over time.

Advances in brain imaging have led to a paradigm shift in neuroscience research, moving from focusing on individual brain structures to investigating neural networks and connections. However, neuroanatomy education still tends to concentrate on discrete brain regions. Two separate experiments in undergraduate neuroscience courses investigated whether incorporating neural connectivity into neuroanatomy education would enhance learning. Students in each experiment learned to identify brain structures through computer‐based training sessions that provided text‐based narrative feedback about neural connections, followed by final memory tests after a 1‐month delay. The first experiment included 30 students and demonstrated a long‐term memory benefit associated with described neural connections, showing a medium effect size (p = 0.01, d = 0.54) comparable to the established retrieval practice effect for enhancing long‐term memory (p = 0.03, d = 0.47). The second experiment replicated the benefits of described neural connections with a small effect size (p = 0.005, d = 0.28) in a larger sample of 122 students across classrooms at two universities. Furthermore, students remembered the functional outcomes of neural connections from training (p < 0.001, d = 0.46), and this generalized to clinical applications (p = 0.009, d = 0.27). In contrast, categorizing brain areas without describing neural connections (as is commonly done in introductory neuroscience textbook chapters) did not benefit either memory or generalization. Findings demonstrate that leveraging the connectivity paradigm shift in neuroscience research can enhance neuroanatomy education. Emphasizing neural connections and their functional outcomes helps simplify neuroanatomy and improve understanding and retention.

Show Me the Brain!! (SMtB) is a digital system for interactive graphics that is designed to support instruction in neuroanatomy and neuroscience. It will soon be made open‐source and freely available. SMtB bridges medical and traditional neuroanatomy instruction with the computational systems and representational conventions common in contemporary human neuroimaging research, including an open‐source programming environment, MRI‐based atlases, standardized coordinate systems, and white matter pathways. The graphical component of SMtB brings together several forms of high‐resolution imagery of the human brain, including 3D models of brain structures, color photography of Visible Human 2.0 cryosections, and structural MRI. These are co‐registered in a single representation, and they are explored in an intuitive 3D environment through direct interaction and through links in embedded HTML documents. A key innovation in the system is the SMtB Scripting Language (SMtB‐SL). This is a formal representation of the graphical display and user interaction that can be generated automatically through simple authoring tools. The result is a high‐level authoring of scenes, assemblies, narratives, and tests that can be used to present material to users and implement learning strategies. SMtB supports multiple use cases: as a tool for self‐directed learning, a classroom instructional tool, a research‐grade neuroimaging data viewer, and a platform for experimental studies in learning and education. Its core digital design supports multiple forms of expansion and future development. We describe the system's architecture, the pedagogical principles behind its design, and applications of the system in neuroscience education and research.

Memory must balance preserving episode-specific details with extracting generalizable structure. Here, we test whether spontaneous hippocampal reactivation during postencoding rest actively transforms memories by strengthening semantic gist at the expense of episodic detail. Twenty-four participants encoded Deese–Roediger–McDermott word lists presented in male or female voices, creating orthogonal semantic (list theme) and episodic (voice source) information. Using high-resolution fMRI, we tracked list-specific reactivation in hippocampal subfields during a 90-sec delay period. Most consistently in CA1, reactivation frequency showed opposing relationships with subsequent memory: Greater reactivation predicted increased false recognition of semantic lures (gist extraction; dz = 0.52) and decreased accuracy for voice discrimination (source memory; dz = −0.65), yielding a large, bidirectional effect within CA1 (gist minus source, paired dz = 1.00). This bidirectional pattern was most prominent in anterior-to-mid hippocampus, consistent with its role in schematic processing. These findings reveal that hippocampal reactivation implements a computational trade-off, actively constructing abstract and generalizable representations while competitively weakening episode-specific details. This mechanism could explain how the brain rapidly extracts meaning from experience, with false memories emerging as a natural consequence of adaptive memory transformation rather than retrieval failure.