Mathematical reasoning is a fundamental aspect of intelligence, encompassing a spectrum from basic arithmetic to intricate problem-solving. Recent investigations into the mathematical abilities of large language models (LLMs) have yielded inconsistent and incomplete assessments. In response, we introduce MathEval, a comprehensive benchmark designed to methodically evaluate the mathematical problem-solving proficiency of LLMs in various contexts, adaptation strategies, and evaluation metrics. MathEval consolidates 22 distinct datasets, encompassing a broad spectrum of mathematical disciplines, languages (including English and Chinese), and problem categories (ranging from arithmetic and competitive mathematics to higher mathematics), with varying degrees of difficulty from elementary to advanced. To address the complexity of mathematical reasoning outputs and adapt to diverse models and prompts, we employ GPT-4 as an automated pipeline for answer extraction and comparison. Additionally, we trained a publicly available DeepSeek-LLM-7B-Base model using GPT-4 results, enabling precise answer validation without requiring GPT-4 access. To mitigate potential test data contamination and truly gauge progress, MathEval incorporates an annually refreshed set of problems from the latest Chinese National College Entrance Examination (Gaokao-2023, Gaokao-2024), thereby benchmarking genuine advancements in mathematical problem solving skills.

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.

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.

This study examines the impact of corporate digital mergers and acquisitions (M&As) on the development of New Quality Productive Forces (NQPF). Using a multi-period difference-in-differences (DID) methodology with data from Chinese listed firms (2011-2021), we demonstrate that digital M&As significantly enhance NQPF. We identify two key mechanisms driving this effect: enhanced firm innovation capability and accelerated data asset accumulation. Furthermore, our findings reveal that external factors including advanced industrial structure, higher urban human capital, and lower economic policy uncertainty positively moderate this relationship. This research introduces a novel NQPF measurement index and provides actionable insights for firms and policymakers seeking to leverage digital transformation for high-quality economic development.

An Osaka Metropolitan University-led research team studied 5-HT3A receptor deletion in mice to assess behavioral persistence.

A groundbreaking study from New Zealand demonstrates that central bank "forward guidance" significantly strengthens the transmission of monetary policy. Analyzing New Zealand's banking data, the research finds that providing clear communication about the future path of interest rates enhances the pass-through from the official policy rate to bank deposit and lending rates. The results show improved long-term pass-through, especially for time deposits and fixed mortgages, alongside a slight acceleration in short-term adjustments. These findings offer critical evidence for central banks worldwide on the power of communication as a policy tool.

Aqueous zinc-ion batteries (AZIBs) are emerging as a promising option for next-generation energy storage due to their abundant resources, affordability, eco-friendliness, and high safety levels. Manganese-based cathode materials, in particular, have garnered significant attention because of their high theoretical capacity and cost-effectiveness. However, they still face substantial challenges related to rate performance and cycling stability. To address these issues, researchers have developed various strategies. This review focuses on the key advancements in manganese-based cathode materials for AZIBs in recent years. It begins with a detailed analysis of the energy storage mechanisms in manganese-based cathodes. Next, it introduces a variety of manganese-based oxides, highlighting their distinct crystal structures and morphologies. It also outlines optimization strategies, such as ion doping (both monovalent ions and multivalent ions), the preparation of Mn-based metal-organic frameworks (MOFs), carbon materials coatings, and electrolyte optimization. These strategies have significantly improved the electrochemical performance of manganese-based oxide cathodes. By systematically analyzing these advancements, it aims to provide guidance for the development of high-performance manganese-based cathodes. Finally, it discusses prospective research directions for manganese-based cathodes in AZIBs.