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. 

In a paper published in SCIENCE CHINA Earth Sciences, a team of researchers conducted a comprehensive review of direct measurement techniques for optical and chemical properties of atmospheric aerosols, as well as the impacts of aerosols on climate and environment, and health risks associated with exposure to high concentrations of ultrafine particles. It serves as a valuable reference for advancing future research and instrumentation development in the field of aerosol science.

A new paper published in SCIENCE CHINA Earth Sciences has identified the topographic heating of the Tianshan Mountains as a key driver of a distinctive dipole precipitation pattern in Arid central Asia. The research, focusing on the mid-Holocene warm period, indicated that the dipole pattern is closely linked to high-resolution model's effective representation of the Tianshan thermal effects. It provides crucial insights for refining climate models to more accurately project future hydrological changes in this vulnerable region.

A research team from the Institute of Geology and Geophysics, Chinese Academy of Sciences (IGGCAS) has produced the first continent-wide map of tree-ring oxygen isotopes (δ¹⁸O_TR) in Asia. Published in National Science Review, the study provides a comprehensive new perspective of how atmospheric circulation, rainfall isotopes, and topography shape the isotopic fingerprints of trees—offering new opportunities for understanding past climate changes and tracing wood origins.

Researchers from China have developed a solar-driven multi-field synergistic strategy to simultaneously harvest freshwater and boron from seawater. The innovative (MXene-MgO)@sodium alginate (SA) composite gel (MMS) achieves high evaporation rates and boron adsorption capacities, offering a sustainable solution to global water and food scarcity.