With rapid technological advancements and increasing environmental challenges, educational systems face demands for innovative teaching methods. This study integrates Virtual Reality (VR) technology into vocational high school art curricula, examining its impact on public art creation and environmental literacy. Based on the Creative Problem Solving (CPS) model and scaffolding theory, the course includes units on public art, aesthetic composition, and environmental issues. Participants were second-year students from a vocational high school in Taiwan, with 29 in the VR-assisted experimental group and 35 in the control group using traditional teaching methods. Data were collected via pre- and post-tests, teacher evaluations, and self-assessments. Results showed that VR significantly improved students’ engagement, exploration, and creation, effectively integrating art and environmental knowledge. The experimental group excelled in spatial expression, showing better understanding and interaction with spatial concepts. The CPS model enhanced problem-solving skills, innovation, teamwork, and feedback. This study confirms that combining VR with the CPS model fosters creativity and problem-solving in art education.

Exploring the potential of text-to-image generation technology in Taiwanese vocational high school art courses, this study employs a conceptual framework of technology integration, creative thinking, and metacognitive abilities, focusing on its effects on teaching strategies as well as students’ digital art creation skills and cognitive and creative development. The study was conducted through a multi-methodological approach that includes a systematic literature review plus participatory action research and qualitative analysis. The results showed that integrating text-to-image technology with education boosted students’ interest in activities such as prompt design and project creation and suited themes like landscapes and conceptual art. Testing AI tools enhanced technical proficiency (average of 3.95/5), while pedagogy shifted to project-based learning, increasing engagement. Students’ digital art skills improved from 3.26 to 3.78 (16% growth), with creativity and originality (3.82/5), style diversity, visual complexity, and divergent thinking notably advanced. The technology also fostered metacognitive skills and critical thinking, proving to be an effective teaching aid beyond a mere digital tool. This discovery provides a fresh theoretical viewpoint and instructional procedures for high school art education curricula, anchored in technology, and highlights the importance of nurturing students’ innovativeness and adaptability within the contemporary digital age.

This study explores the application of generative artificial intelligence (AI) in financial risk forecasting, aiming to assess its potential in enhancing both the accuracy and interpretability of predictive models. Traditional methods often struggle with the complexity and nonlinearity of financial data, whereas generative AI—such as large language models and generative adversarial networks (GANs)—offers novel solutions to these challenges. The study begins with a comprehensive review of current research on generative AI in financial risk prediction, with a focus on its roles in data augmentation and feature extraction. It then investigates techniques such as Generative Adversarial Explanation (GAX) to evaluate their effectiveness in improving model interpretability. Case studies demonstrate the practical value of generative AI in real-world financial forecasting and quantify its contribution to predictive accuracy. Furthermore, the study identifies key challenges—including data quality, model training costs, and regulatory compliance—and proposes corresponding mitigation strategies. The findings suggest that generative AI can significantly improve the accuracy and interpretability of financial risk models, though its adoption must be carefully managed to address associated risks. This study offers insights and guidance for future research in applying generative AI to financial risk forecasting.

This study addresses the increasingly complex challenge of multiple-fault diagnosis in modern intelligent automotive electronic systems by proposing an innovative deep learning-based solution. The research integrates Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and the Transformer architecture to construct a multi-modal fault diagnosis model. By collecting real-world operational data from vehicle electronic systems, including fault samples from key modules such as the Engine Control Unit (ECU), Body Control Module (BCM), and safety systems, a comprehensive dataset comprising 12 major fault types was established. Experimental results demonstrate that the proposed hybrid deep learning model achieves a multiple-fault identification accuracy of 96.8%, representing a 23% performance improvement over traditional diagnostic methods. The integration of Explainable AI (XAI) techniques provides the diagnostic results with visual interpretability, aiding maintenance technicians in understanding the model’s diagnostic logic. The findings of this research can be applied in smart factories, automotive service centers, and on-board diagnostic (OBD) systems, offering significant practical value in enhancing vehicle safety and reducing maintenance costs.

This study develops and validates an artificial intelligence (AI)-assisted internship learning platform for automotive electronics based on the Llama 3 large language model, aiming to enhance pedagogical effectiveness within vocational training contexts. Addressing critical issues such as the persistent theory–practice gap and limited innovation capability prevalent in existing curricula, we leverage the natural language processing (NLP) capabilities of Llama 3 through fine-tuning based on transfer learning to establish a specialized knowledge base encompassing fundamental circuit principles and fault diagnosis protocols. The implementation employs the Hugging Face Transformers library with optimized hyperparameters, including a learning rate of 5 × 10⁻⁵ across five training epochs. Post-training evaluations revealed an accuracy of 89.7% on validation tasks (representing a 12.4% improvement over the baseline model), a semantic comprehension precision of 92.3% in technical question-and-answer assessments, a mathematical computation accuracy of 78.4% (highlighting this as a current limitation), and a latency of 6.3 s under peak operational workloads (indicating a system bottleneck). Although direct trials involving students were deliberately avoided, the platform’s technical feasibility was validated through multidimensional benchmarking against established models (BERT-base and GPT-2), confirming superior domain adaptability (F1 = 0.87) and enhanced error tolerance (σ² = 1.2). Notable limitations emerged in numerical reasoning tasks (Cohen’s d = 1.15 compared to human experts) and in real-time responsiveness deterioration when exceeding 50 concurrent users. The study concludes that Llama 3 demonstrates considerable promise for automotive electronics skills development. Proposed future enhancements include integrating symbolic AI modules to improve computational reliability, implementing Kubernetes-based load balancing to ensure latency below 2 s at scale, and conducting longitudinal pedagogical validation studies with trainees. This research provides a robust technical foundation for AI-driven vocational education, especially suited to mechatronics fields that require close integration between theoretical knowledge and practical troubleshooting skills.

The pervasive integration of artificial intelligence across e-commerce ecosystems has fundamentally transformed the competitive landscape, rendering systematic and reproducible platform evaluation frameworks an operational necessity rather than an academic exercise. Conventional multi-criteria decision analysis approaches for e-commerce evaluation remain structurally constrained by their dependency on human expert panels, which introduce recruitment costs, cognitive biases, limited reproducibility, and the practical infeasibility of assembling genuinely multidisciplinary panels spanning e-commerce strategy, machine learning engineering, and financial technology simultaneously. This study proposes a novel decision support framework that integrates Large Language Model (LLM) multi-agent simulation with the Best–Worst Method (BWM) to derive reproducible priority weights for AI-driven e-commerce platform evaluation within a rigorous business intelligence architecture. Twelve domain-differentiated LLM agents—organized into three expertise groups representing e-commerce management, AI and machine learning technology, and digital payment systems—were instantiated with structured system prompts encoding professional domain knowledge and deployed across three independent simulation rounds to perform BWM pairwise comparisons across a comprehensive six-dimensional, 30-sub-criterion evaluation hierarchy. Inter-agent consensus was synthesized through geometric mean aggregation, with consistency verification conducted via BWM’s xi* indicator and inter-round stability assessed through coefficient of variation analysis. Results reveal that Transaction Security and Trust achieves the highest dimension-level weight (w = 0.248), followed by AI Recommendation Effectiveness (w = 0.213), with Personal Data Protection (G = 0.0750), Recommendation Accuracy (G = 0.0607), and Transaction Transparency (G = 0.0549) emerging as the three highest globally ranked sub-criteria. The aggregated consistency indicator xi* = 0.062 confirms logical coherence of the multi-agent judgment consensus, and all dimension weights exhibit CV values below 2.8%, demonstrating exceptional inter-round stability. Spearman rank correlations among the three domain-expertise groups exceed 0.92, confirming strong inter-group convergence. Sensitivity analysis under perturbations of ±10% and ±20% demonstrates that the top-five priority indicators are structurally stable. This study establishes LLM multi-agent BWM simulation as a methodologically rigorous, institutionally accessible, and computationally reproducible alternative to traditional expert elicitation for complex platform evaluation tasks.