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.

The rapid development of artificial intelligence technology has propelled the automated, humanized, and personalized learning services to become a core topic in the transformation of education. Generative artificial intelligence (GenAI), represented by large language models (LLMs), has provided opportunities for reshaping the methods for setting personalized learning objectives, learning patterns, construction of learning resources, and evaluation systems. However, it still faces significant limitations in understanding the differences in individual static characteristics, dynamic learning processes, and students’ literacy goals, as well as in actively differentiating and adapting to these differences. The study has clarified the technical strategies and application services of GenAI-empowered personalized learning, and analyzed the challenges in areas such as the lag in theoretical foundations and lack of practical guidance, weak autonomy and controllability of key technologies, insufficient understanding of the learning process, lack of mechanisms for enhancing higher-order literacy, and deficiencies in safety and ethical regulations. It has proposed implementation paths around interdisciplinary theoretical innovation, development of LLMs, enhancement of personalized basic services, improvement of higher-order literacy, optimization of long-term evidence-based effects, and establishment of a safety and ethical value regulation system, aiming to promote the realization of safe, efficient, and sustainable personalized learning.

Generative artificial intelligence (GenAI) models, such as ChatGPT, have rapidly gained popularity. Despite this widespread usage, there is still a limited understanding of how this emerging technology impacts different stakeholders in higher education. While extensive research exists on the general opportunities and risks in education, there is often a lack of specificity regarding the target audience—namely, students, educators, and institutions—and concrete solution strategies and recommendations are typically absent. Our goal is to address the perspectives of students and educators separately and offer tailored solutions for each of these two stakeholder groups. This study employs a mixed-method approach that integrates a detailed online questionnaire of 188 students with a scenario analysis to examine potential benefits and drawbacks introduced by GenAI. The findings indicate that students utilize the technology for tasks such as assignment writing and exam preparation, seeing it as an effective tool for achieving academic goals. Subsequent the scenario analysis provided insights into possible future scenarios, highlighting both opportunities and challenges of integrating GenAI within higher education for students as well as educators. The primary aim is to offer a clear and precise understanding of the potential implications for students and educators separately while providing recommendations and solution strategies. The results suggest that irresponsible and excessive use of the technology could pose significant challenges. Therefore, educators need to establish clear policies, reevaluate learning objectives, enhance AI skills, update curricula, and reconsider examination methods.

Highspeed Internet, autonomous driving, the Internet of Things: data streams are proliferating at enormous speed. But classic radio technology is reaching its limits: the higher the data rate, the faster the signals need to be transmitted. Researchers at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) have now demonstrated (DOI: 10.1038/s42005-025-02273-0) that weak radio signals can be efficiently converted into significantly higher frequencies using this material that is just several tens of nanometers thick. And at room temperature, at that. The results open up prospects for future generations of mobile communications and high-resolution sensor technology.

A research team led by Dr. Jong-Woo Kim from the Nano Materials Research Division and Dr. Da-Seul Shin from the Materials Processing Research Division at the Korea Institute of Materials Science (KIMS) has successfully developed Korea’s first full-cycle magnetic cooling technology, encompassing materials, components, and modules. This breakthrough is expected to address the environmental issues associated with conventional gas-based refrigeration technologies and pave the way for eco-friendly, high-efficiency alternative cooling solutions to enter the market.

In a remarkable stride towards environmental sustainability, researchers at the Department of Biotechnology and Medical Engineering, National Institute of Technology Rourkela, India, have developed a novel approach to predict the adsorption capacity of biochar using machine learning. This breakthrough, detailed in their latest study titled "Machine Learning-Driven Prediction of Biochar Adsorption Capacity for Effective Removal of Congo Red Dye," offers a powerful solution to combat dye pollution.

The electrocatalytic CO₂ reduction reaction (CO₂RR) serves as an effective approach to convert CO₂ into high-value chemicals and facilitate carbon cycling. Among various products, ethylene (C₂H₄), a crucial industrial feedstock, demonstrates substantial market demand and economic significance. Copper (Cu)-based catalysts exhibit remarkable advantages in CO₂RR to C₂H₄ conversion due to their unique electronic structure and optimal *CO adsorption capacity.

Concurrently, the membrane electrode assembly (MEA) design featuring an electrolyte-free cathode effectively addresses mass transfer limitations, minimizes ohmic losses, and enhances interfacial efficiency, thereby significantly boosting current density and product selectivity. The integration of Cu-based catalysts with MEA technology thus emerges as a highly promising solution for industrial-scale CO₂RR to C₂H₄ production.

Chinese chemists boost lignin’s sun protection factor from 5 to 66 and cut colour darkness by one-third through controlled radical grafting and TiO₂ nano-loading, offering a biodegradable alternative to petrochemical filters.

Reporting in the Journal of Bioresources and Bioproducts, researchers show that atom-transfer radical polymerisation couples waste-pulp lignin with the commercial UV absorber MBBT, producing sub-micron spheres that remain stable after three hours of intense ultraviolet irradiation.