The era of AI requires new educational taxonomies that prepare AI natives for personal and professional success. In a new breakthrough, a researcher from the Educational University of Hong Kong has proposed, in TAO, the novel SCALE taxonomy that integrates scientific literacy, computational thinking, AI literacy, learning to learn/metacognition, and engineering and design thinking. It equips AI natives to engage in meaningful experiences and promotes “authentic learning through creating” in the age of AI.

A new study published in Frontiers of Engineering Management presents a novel blockchain-enabled Open BIM data exchange paradigm based on a traceable semantic differential transaction (tSDT) approach. The tSDT approach minimizes data redundancy and enables semantic-level traceability of BIM changes. A Blockchain 3.0 application named openBIMdisk implements the tSDT approach and provides an intuitive and user-friendly interface for BIM practitioners.

This study evaluates virtual 3D scanned prosections in gross anatomy education. Twenty-nine medical students were divided into physical or virtual teaching groups. Both groups showed significant post-test score improvements, with no significant difference between them. While students found 3D scans effective for learning and exam preparation, they preferred dissection for lab experience. Results indicate virtual 3D scans are a valuable supplementary tool but not a replacement for traditional dissection in medical education. 

Teacher emotion recognition (TER) is crucial for classroom dynamics, yet limited by scarce multimodal data and feature modeling. We present a multimodal TER dataset with 102 lessons and 2,170 video segments, annotated with interaction-based emotional tags. We propose an Emotion Dual-Space Network (EDSN) with a Commonality Space (ECSC) using central moment differences to align modalities, and a Discrimination Space (EDSC) using gradient reversal and orthogonal projection to extract unique features. EDSN achieves 77.0% accuracy and 0.769 F1-score on the dataset, outperforming state-of-the-art models, demonstrating its effectiveness in educational emotion recognition. 

This study determined cryo-EM structures of midnolin-proteasome complexes, providing mechanistic insights to midnolin-catalyzed substrate degradation. Based on structural and mechanistic understanding, authors have engineered the midnolin system for targeted degradation of desired substrates, and provide the first proof-of-principle for degrading a non-native therapeutically high-profile target that is otherwise undruggable.