Zhu Guangqian's Psychology of Literature and Art is a key text in the construction of modern aesthetics in China. This book systematically analyzes the aesthetic experience and its manifestations in literary and artistic activities from the perspective of psychology, which embodies a distinct research orientation of "proceeding from empirical facts" in methodology, and theoretically completes the selective absorption of modern western aesthetics and the integration of China's traditional aesthetic experience. Because the text itself spans many fields of psychology, aesthetics and literature and art, its theoretical structure is not nonlinear, and beginners are prone to break their understanding and confuse their concepts during reading. This paper does not attempt to make a comprehensive review of Psychology of Literature and Art, but focuses on "how to learn this classic", sorting out a relatively clear and operable learning path from four aspects: academic context, theoretical core, research method and practice transformation, in order to provide reference for the systematic study of graduate students.

2025 marks a pivotal preparatory phase preceding the explosive growth of artificial intelligence(AI)technology.Its integration into education is driving systemic transformation from"standardized instruction"to"personalized enhancement".This paper examines key annual advancements in AI-powered education,focusing on three core domainsintelligent teaching agents, edge-based educational AI hardware,and AI governance frameworks.Research findings reveal a global educational AI ecosystem characterized by both competition and collaboration.The study concludes that engineering implementation and compliant applications remain current priorities.Future efforts should emphasize educational ethics and multimodal teaching integration,propelling AI evolution from educational auxiliary tools to"human-machine collaborative enhanced educational partners".

With the rapid development of artificial intelligence, large language models (LL Ms) are increasingly applied in education. Ideological and political theory courses in universities, which are crucial for moral education, face challenges such as outdated cases, weak interaction, and uniform content. This study explores the intelligent generation of cases and interactive teaching empowered by LL Ms, proposing a “triad” paradigm: intelligent generation, multi-dimensional interaction, and value guidance. An ELM-based case-generation system and an interactive platform were constructed to realize real-time content updates, precise case matching, enhanced classroom interaction, and data-driven assessment. Results show that LL Ms significantly improve the attractiveness and pertinence of ideological education, providing technical support for the goal of “educating students in accordance with the times and trends”. Finally, strategies for addressing ethical, security, and teacher-role issues are discussed.

To address the increasing demand for personalized instruction in education and ameliorate the time-consuming and inflexible nature of traditional lesson planning, this paper presents the development and evaluation of an intelligent, AI-based lesson preparation tool. The system integrates Large Language Models (LLMs), including the Langchain-Chatchat framework, ChatGLM3, and ERNIE-3.5-8K, with the objective of significantly enhancing both the efficiency of teacher preparation and the quality of instruction. The tool implements four core functionalities: rapid question-answering based on a local knowledge base, one-click intelligent generation of instructional images and PowerPoint presentations (PPTs), and automated assignment generation and grading. Results from functional testing and user feedback evaluations indicate that the tool operates stably, effectively reduces educators' preparation time, enriches the dimensions of teaching content, and increases student engagement. The successful implementation of this research provides a valid paradigm for the deep application of artificial intelligence technology in the education sector, demonstrating its substantial potential in advancing personalized learning and automating pedagogical tasks.

In response to the urgent demand for high-calibre, multidisciplinary software engineering professionals arising from the development of new engineering disciplines, and to address critical bottlenecks in traditional software engineering education—such as the disconnect between theory and practice and insufficient student agency—this research endeavours to establish a systematic, quantifiable blended teaching quality enhancement strategy. Guided by the logic of Outcome-Based Education (OBE), this paper undertakes a comprehensive and systematic backward redesign of the software engineering programme's talent development objectives, curriculum design, teaching implementation, and assessment framework. The core innovation lies in proposing and implementing a tripartite, deeply integrated teaching model centred on Project-Based Learning (PBL), encompassing online, offline, and practical components, alongside a fully integrated quality monitoring and continuous feedback mechanism. Empirical application and data analysis demonstrate that this approach significantly enhances the actual attainment of students' Course Learning Outcomes (CLOs). Particularly in core engineering competency metrics—engineering practice, complex problem-solving, and team collaboration—student performance shows substantial and systematic improvement, with teaching quality assessment indicators effectively enhanced. Research confirms that the blended teaching model guided by OBE principles represents an effective, actionable, and quantifiable pathway for enhancing the quality of software engineering talent cultivation. It provides crucial theoretical underpinnings and practical reference value for curriculum reform and implementation within comparable engineering education domains.

This paper takes the undergraduate graduation design of the Computer Science and Technology major as the research object, and fully records the entire process of the project "Design and Implementation of a Rapid Goods Recognition System for Cabinets Based on Deep Learning" from initiation to implementation. It focuses on analyzing the key actions and core gains in the stages of topic selection and decision-making, technical preparation, engineering practice, and reflection and summary. In the topic selection stage, it breaks through the limitations of traditional development directions and finally selects the uncontacted deep learning field among small program development, conventional system design, and deep learning applications. In the technical preparation stage, it builds a theoretical framework for target detection and system development based on pre-graduate study during the winter vacation and literature review. In the engineering practice stage, aiming at the problem of insufficient computing power of personal equipment, it systematically explores server rental and selection schemes, and overcomes a series of technical difficulties in environment setup, code reproduction, and function integration. Finally, a cabinet goods recognition system with image detection, video recognition, and result export functions is completed. The research confirms that this graduation design not only realizes the cross-field integration of professional knowledge but also cultivates the abilities of independent learning, problem diagnosis, and engineering implementation. It provides a reusable graduation design practice paradigm for computer major students and helps them efficiently complete the core tasks in the final stage of their studies.

To address the theory-practice gap in ethics instruction within artificial intelligence (AI) general education, this action research study developed a three-layer pedagogical model—"Problem Orientation, Implementation, and Value Shaping"—enhanced by AI technology. Centered on the core case study, "My Professional Assistant," and supported by complementary cases, the model was implemented over one semester. A mixed-methods approach, comprising pre- and post-test questionnaires, content analysis of student deliverables, and in-depth interviews, was employed for evaluation. Results demonstrated significant improvements in students' sensitivity to technological ethics (p < 0.001) and their knowledge of responsible AI practices (p < 0.001). Qualitative findings revealed that students transitioned from being mere tool users to responsible supervisors, integrating ethical considerations deeply into their professional practice. Furthermore, several student projects were adopted by external partners, generating impact beyond the classroom. This research provides an actionable framework and empirical support for the systematic cultivation of professional ethics literacy in general AI education.

Aiming at the problems existing in the traditional teaching of "Principles and Applications of Database Systems" for software engineering majors in private application-oriented undergraduate universities—such as overemphasis on theory while neglecting practice, significant differences in students' foundational knowledge, and a single evaluation method—this study integrated the CDIO (Conceive-Design-Implement-Operate) engineering education model with the OBE (Outcome-Based Education) concept, and implemented a systematic teaching reform using the "Student Performance Management System" as the core teaching case. Following the CDIO process of "Conceive-Design-Implement-Operate" and relying on SQL Server as the database tool, student-centered project-based teaching was carried out. Adhering to the logic of "reverse design and forward implementation", a three-dimensional objective system covering knowledge, competence, and quality was reconstructed. Progressive project-based teaching was adopted, along with a differentiated strategy of "stratified grouping + flexible tasks", and a multi-dimensional assessment system consisting of "process evaluation (60%) + summative evaluation (40%)" was established. Teaching practice shows that this model effectively stimulates students' learning initiative, and significantly enhances their engineering practice and teamwork abilities. The CDIO-OBE integrated model can effectively improve the teaching quality of application-oriented courses in private universities, providing a referable path for the reform of similar courses.

Gamma-ray burst (GRB) detection is crucial for triggering rapid follow-up observations and enabling subsequent multi-wavelength studies, yet traditional methods are limited by their difficulty in modeling long-range temporal dependencies in noisy, non-stationary data. In this work, we propose GRBNet, a self-attention–based neural network for GRB detection from same-source multi-detector time-tagged event (TTE) sequences. GRBNet leverages multi-head self-attention to adaptively aggregate discriminative evidence over the full observation window, explicitly capturing long-term dependencies and multi-pulse structures, and integrates complementary responses from multiple detectors under a unified tokenization scheme to improve robustness against background drift and low SNR in individual instruments. Experiments on real Fermi/GBM observations show strong detection performance (recall=1.00, precision=1.00) under a stringent triggering threshold 0.99, indicating reliable performance for weak and morphologically complex GRB events.

To address the issues of uneven texture distribution, background mis-migration, and training imbalance in traditional CycleGAN for style transfer of non-paired horse and zebra images, this study proposes an improved model integrating dynamic attention mechanisms, semantic segmentation constraints, and adaptive training strategies. By embedding lightweight space-channel hybrid attention modules in generator residual blocks, the model enhances feature extraction in target regions. A lightweight semantic segmentation network is introduced to enforce local style transfer constraints, preventing redundant background migration. A two-stage adaptive loss weight adjustment strategy is designed to improve training stability. Experiments on the horse2zebra dataset using the PyTorch platform demonstrate that the improved model generates zebra stripes that conform to the subject structure, with non-target region pixel changes below 15.8%. Compared to the original CycleGAN, the model achieves an 8.3% SSIM improvement and 1.5dB PSNR enhancement. This model effectively resolves core limitations of traditional methods, providing a superior solution for style transfer of non-paired animal images.