With the rapid advancement of virtual reality technology, its application in the judicial field has become increasingly widespread. However, the determination of the legal validity of VR evidence still faces numerous challenges, including issues related to technical reliability, evidentiary authenticity, and legal adaptability. This paper systematically examines the criteria for determining the legal validity of VR evidence in criminal proceedings, the rules for its evaluation, and practical dilemmas. It aims to address theoretical gaps in the current legal framework and provide actionable guidelines for judicial practice. The research not only facilitates the deep integration of technology and law but also offers valuable insights for refining evidence rules in the digital era and safeguarding judicial fairness. Therefore, the publication of this paper holds significant academic and practical value.

Operationalizing the Green Principle in civil law is hindered by difficulties in proving causality for indirect environmental torts. This study proposes a computational knowledge management framework integrating a Neural Network optimized by Genetic Algorithms to bridge this gap. By transforming macro-environmental data into a dynamic Environmental Pollution Index, the model quantifies pollution diffusion and identifies urbanization and energy consumption as key drivers with cross-regional spillover effects. The results demonstrate that this approach converts complex scientific data into explicit legal knowledge, supporting causal presumption and the inversion of the burden of proof. Ultimately, the framework facilitates a shift from post-event compensation to data-driven preventive governance, offering a robust technical foundation for modernizing environmental rule of law through the deep integration of legal theory and computational intelligence.

This study aims to address three core issues in children's literature recommendation systems for ages 0–6: mismatched content with children's cognitive development, lack of integrated evaluation for educational value and safety, and algorithm “black box” undermining parental/educator trust. To resolve these, it proposes a big data-driven intelligent system integrating multi-modal data, age-aware dynamic modeling, a hybrid recommendation algorithm, an educational evaluation framework, multi-level safety filtering, and explainable AI for transparency. A controlled experiment showed the experimental group had 26.9–33.3% higher recommendation accuracy, 22.9–34.9% better educational goal achievement, a 26.7-percentage-point lower content safety error rate, and significant long-term engagement growth. This system overcomes traditional limitations, providing a scalable framework for integrating big data and AI into early childhood literacy knowledge management, and insights for personalized, safe, education-aligned recommendations in educational knowledge systems.

Global supply chains have been repeatedly disrupted by pandemics, climate shocks, and geopolitical tensions, exposing vulnerabilities in multinational corporations (MNCs). Traditional risk models rely on static assumptions and separate prediction from decision-making. This study develops a big data–driven knowledge framework that integrates risk prediction and performance optimization for MNC supply chains. Using a simulated panel of 1,000 subsidiaries from 50 multinational firms (2020–2024), risk factors such as demand volatility, lead time, supplier reliability, logistics costs, and climate disruptions are modeled alongside moderating variables including ESG score, digitalization, and cross-border diversity. Machine-learning and deep learning models (Random Forest, XGBoost, LSTM, and Federated GCN) generate disruption probabilities, which are translated into resilience strategies through optimization and digital-twin simulations. Regression and structural equation modeling link risks and strategies to ROI, ROE, cost efficiency, and resilience.

The built environment has a significant impact on knowledge workers' productivity. Work environments should be designed to address the needs of the workers who inhabit the space to ensure optimal effectiveness in executing their work tasks, thereby increasing their productivity. This paper proposes a user-centered process for workspace design that would allow organizations to improve the capabilities of their work environment continually and address the needs of knowledge workers. A structured literature review was conducted to identify principles for effective workplace design as well as dimensions for testing workplace usability. A focus group was facilitated to enrich the findings emanating from the literature review, where the love and breakup letter method (LBM) was implemented to elicit the needs of office workers. The findings determined that the most significant negative impact on knowledge worker productivity was the implementation of a hot-desk system, and suggested a move towards activity-based workspaces.

Social structure transformation demands higher capabilities of social work professionals, but traditional teaching suffers from low participation and theory-practice disconnection. Based on social constructivism and immersive learning theory, this study developed an enhanced “preparation-participation-reflection-reconstruction” cyclic model. A quasi-experiment with 160 social work students used multi-source data triangulation. Results showed the model significantly improved students' participation, reflective depth, critical thinking, and academic self-efficacy. Reflective depth fully mediated the path from interaction frequency to professional competence, strengthened by the “collaboration-feedback loop”. With only six extra minutes of teacher preparation per class, the model is low-burden and operable, showing stable effects across genders and stronger effects among sophomores. It provides a feasible framework for cultivating practice-oriented social work professionals.

This study explores how digital gamification can enhance the internalization of value-based knowledge in university ideological and political education. Grounded in knowledge management principles, a dual-loop incentive model is proposed, integrating extrinsic rewards (e.g., points, levels) and intrinsic motivation (e.g., role-playing, narrative) to drive cognitive engagement and behavioral commitment. A digital learning system is developed to support real-time feedback, knowledge tracking, and adaptive intervention—functioning as a lightweight knowledge management system in an educational context. Results show improved student participation, value identification, and critical thinking, demonstrating the model's effectiveness in facilitating both explicit and tacit knowledge transfer. The findings offer practical insights for designing incentive-driven knowledge systems in educational organizations.

This study proposes an AI-enhanced design thinking framework to improve knowledge creation and transfer in digital media education. As generative AI transforms creative practices, traditional instruction lacks adaptability and personalization. By integrating AI tools like large language models into design thinking, the framework enables dynamic knowledge construction, iterative prototyping, and intelligent feedback. It supports adaptive knowledge management systems in education, enhancing curriculum flexibility and teaching efficiency. Empirical results show improved knowledge sharing, reuse of learning artifacts, and higher learner satisfaction. Aligning AI with human-centered design fosters cognitive engagement and collaboration. Findings highlight AI's role as a cognitive and collaborative enabler in pedagogy. This research contributes to knowledge management by demonstrating how AI-driven methods can structure knowledge capture, transfer, and application in higher education. The framework offers a model for AI-integrated, learner-centered curriculum innovation.

Predicting academic performance is vital in education, yet early identification of at-risk students and timely interventions remain challenging. The authors present a decision support system using a Bayesian network to dynamically estimate course failure risk and integrate predictive analytics with institutional knowledge management. The model, built with the Bayesian Information Criterion and domain expertise, handles incomplete data and enables continuous, near-real-time monitoring. Applied to data from 8,897 undergraduates at a Southeastern U.S. university, it achieves over 85% accuracy at semester start and over 90% by midterm. The system enables prompt, individualized interventions and enhances institutional knowledge flows, supporting informed decisions and organizational learning. This scalable framework strengthens student retention and academic outcomes, bridging predictive modeling and knowledge-based strategies in higher education.

Traditional physical education often lacks personalization and fails to optimize training efficiency. To address this, this study proposes a three-layer VR-based framework integrating data acquisition, algorithmic analysis, and immersive content delivery to support personalized and adaptive sports training. The framework enables real-time feedback and dynamic course adjustment based on individual performance and interest. Empirical results show that the VR-enhanced training improves basketball shooting efficiency by 1.79 times, reduces fatigue through a 23% improvement in blood lactate threshold, and achieves high movement consistency across scenarios (ρ=0.92). These findings demonstrate the framework's effectiveness in enhancing both skill acquisition and transfer. This research provides a replicable model for intelligent physical education systems, contributing to the integration of knowledge management and experiential learning in sports training.