Teacher emotion recognition (TER) has a significant impact on student engagement, classroom atmosphere, and teaching quality, which is a research hotspot in the smart education area. However, existing studies lack high-quality multimodal datasets and neglect common and discriminative features of multimodal data in emotion expression. To address these challenges, this research constructs a multimodal TER dataset suitable for real classroom teaching scenarios. TER dataset contains a total of 102 lessons and 2,170 video segments from multiple educational stages and subjects, innovatively labelled with emotional tags that characterize teacher‒student interactions, such as satisfaction and questions. To explore the characteristics of multimodal data in emotion expression, this research proposes an emotion dual-space network (EDSN) that establishes an emotion commonality space construction (ECSC) module and an emotion discrimination space construction (EDSC) module. Specifically, the EDSN utilizes central moment differences to measure the similarity to assess the correlation between multiple modalities within the emotion commonality space. On this basis, the gradient reversal layer and orthogonal projection are further utilized to construct the EDSC to extract unique emotional information and remove redundant information from each modality. Experimental results demonstrate that the EDSN achieves an accuracy of 0.770 and a weighted F1 score of 0.769 on the TER dataset, outperforming other comparative models.

Open-source large language models (LLMs) research has made significant progress, but most studies predominantly focus on general-purpose English data, which poses challenges for LLM research in Chinese education. To address this, this research first reviewed and synthesized the core technologies of representative open-source LLMs, and designed an advanced 1.5B-parameter LLM tailored for the Chinese education field. Chinese education large language model (CELLM) is trained from scratch, involving two stages, namely, pre-training and instruction fine-tuning. In the pre-training phase, an open-source dataset is utilized for the Chinese education domain. During the instruction fine-tuning stage, the Chinese instruction dataset is developed and open-sourced, comprising over 258,000 data entries. Finally, the results and analysis of CELLM across multiple evaluation datasets are presented, which provides a reference baseline performance for future research. All of the models, data, and codes are open-source to foster community research on LLMs in the Chinese education domain.

As a data-driven analysis and decision-making tool, student portraits have gained significant attention in education management and personalized instruction. This research systematically explores the construction process of student portraits by integrating knowledge graph technology with advanced data analytics, including clustering, predictive modelling, and natural language processing. It then examines the portraits’ applications in personalized learning, such as student-centric adaptation of content and paths, and personalized teaching, especially the educator-driven instructional adjustments. Through case studies and quantitative analysis of multimodal datasets, including structured academic records, unstructured behavioural logs, and socio-emotional assessments, the research demonstrates how student portraits enable academic early warnings, adaptive learning path design, and equitable resource allocation. The findings provide actionable insights and technical frameworks for implementing precision education.

When incorporating new technology into medical curricula, it is essential to evaluate student success and resource preference. Our team created a database of virtual 3D scanned prosections for students to use while studying Gross Anatomy. Incoming first year medical students were recruited to take part in a study examining the effectiveness and preference for this resource. The study was conducted in four parts: first, a pre-test using physical prosections and images of virtual 3D scans of prosections; second, a teaching session using physical prosections or virtual 3D scans of prosections; third, a post-test identical to the pre-test; forth, a post-test survey. Twenty-nine students participated in this study (physical prosection teaching group [physical]= 15; virtual 3D scans teaching group [virtual] = 14). Exam scores significantly increased in both groups regardless of past anatomy experience with no significance found between groups (physical = 42.6% ± 17.9%; virtual = 44.3% ± 24.0%; P < 0.01). Students taught using the virtual 3D scans were more likely to agree that they “would be able to sufficiently learn anatomy using 3D scans” (physical = 3.0 ± 0.8; virtual = 4.1 ± 1.1; P < 0.01). Regardless of teaching group, students disagreed that they “would have a similar lab experience if they learned 3D scans instead of dissection” (physical = 2.1 ± 0.6; virtual = 2.5 ± 0.8), but agreed that they would use the virtual 3D scans to prepare for the dissection lab and practical/written exam (physical = 4.5 ± 0.8; virtual = 4.9 ± 0.3). This study demonstrates that virtual 3D scans are comparable to physical prosections in anatomy learning, but students do not support this resource replacing the dissection process.

With the development of the Internet and intelligent education systems, the significance of cognitive diagnosis has become increasingly acknowledged. Cognitive diagnosis models (CDMs) aim to characterize learners’ cognitive states based on their responses to a series of exercises. However, conventional CDMs often struggle with less frequently observed learners and items, primarily due to limited prior knowledge. Recent advancements in large language models (LLMs) offer a promising avenue for infusing rich domain information into CDMs. However, integrating LLMs directly into CDMs poses significant challenges. While LLMs excel in semantic comprehension, they are less adept at capturing the fine-grained and interactive behaviours central to cognitive diagnosis. Moreover, the inherent difference between LLMs’ semantic representations and CDMs’ behavioural feature spaces hinders their seamless integration. To address these issues, this research proposes a model-agnostic framework to enhance the knowledge of CDMs through LLMs extensive knowledge. It enhances various CDM architectures by leveraging LLM-derived domain knowledge and the structure of observed learning outcomes taxonomy. It operates in two stages: first, LLM diagnosis, which simultaneously assesses learners via educational techniques to establish a richer and a more comprehensive knowledge representation; second, cognitive level alignment, which reconciles the LLM’s semantic space with the CDM’s behavioural domain through contrastive learning and mask-reconstruction learning. Empirical evaluations on multiple real-world datasets demonstrate that the proposed framework significantly improves diagnostic accuracy and underscoring the value of integrating LLM-driven semantic knowledge into traditional cognitive diagnosis paradigms.

With the rapid development of online education, cognitive diagnosis has become a key task in intelligent education, particularly for student ability assessments and resource recommendations. However, existing cognitive diagnosis models face the diagnostic system cold-start problem, whereby there are no response logs in new domains, making accurate student diagnosis challenging. This research defines this task as zero-shot cross-domain cognitive diagnosis (ZCCD), which aims to diagnose students’ cognitive abilities in the target domain using only the response logs from the source domain without prior interaction data. To address this, a novel paradigm, large language model (LLM)-guided cognitive state transfer (LCST) is proposed, which leverages the powerful capabilities of LLMs to bridge the gap between the source and target domains. By modelling cognitive states as natural language tasks, LLMs act as intermediaries to transfer students’ cognitive states across domains. The research uses advanced LLMs to analyze the relationships between knowledge concepts and diagnose students’ mastery of the target domain. The experimental results on real-world datasets shows that the LCST significantly improves cognitive diagnostic performance, which highlights the potential of LLMs as educational experts in this context. This approach provides a promising direction for solving the ZCCD challenge and advancing the application of LLMs in intelligent education.

Large language models (LLMs) have transformed natural language processing with their improved performance compared with previous methods and have shown great potential to be adopted in other fields. The sequential nature of genomics data, such as deoxyribonucleic acid (DNA), ribonucleic acid (RNA), and proteins, makes it akin to human natural language, supporting the application of LLMs. Currently, LLMs have only been applied to genomic research for about four years but have already achieved significant advances in many challenging and important problems. This review summarizes the recent progress of applying LLMs in genomic research, including developing biological foundation models for protein, DNA, and RNA, as well as specialized models for interaction prediction, single-cell analysis, and structure prediction. The review discusses the challenges and potentials of adopting new advancements in LLMs for genomic applications and proposes several practical projects for integrating LLMs into genomics teaching and learning.