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Interdisciplinary knowledge fusion and practice based on cloud computing technology
Haowen Liu;Bing Li;Fu Lin;Jiping Liu;Nowadays, cloud computing, big data, artificial intelligence, software engineering, and other technologies have developed rapidly. These technologies are widely used in all walks of life and are gradually developing towards integration. This paper introduces how to fuse the experiment cases related to big data, Dev Ops or artificial intelligence into the course of cloud computing platform and technology in Wuhan University, practice them with cloud computing, and then improve the course year by year, so as to improve the course and thereby promote the cultivation of high-end composite talents.
Preventing AI over-reliance: Integrating large language models in introduction to computing course
Pengfei Wang;Shangguang Wang;Against the backdrop of rapid advancements in generative artificial intelligence, the Introduction to Computing course faces risks of misuse such as proxy learning and proxy writing, particularly in software engineering project practices where students exhibit excessive reliance on AI. Concerning the AI over-reliance problem, this study proposes a three-stage teaching structure comprising computing fundamentals, modular design, and project practice, integrated with dual mechanisms for both leveraging and regulating large models. Specifically, in the early stage, students are guided to use AI for programming assistance to stimulate interest, while in the later stage, mechanisms for AI usage traceability and process monitoring are introduced to standardize AI application in projects. This approach shifts students from mastering syntax to understanding engineering principles, facilitating a transition from writing code to managing projects and collaborating effectively.
Unrestricted AI assistants hinder learning: A three-year longitudinal study in software engineering education
Botao Wang;Jing Luo;Xinnian Wang;The rapid integration of AI programming assistants(AIPAs) into education raises critical questions about their impact on student learning. This study conducts an exploratory three-year longitudinal quasi-experiment in an introductory software engineering course, comparing a cohort with unrestricted AI access(n = 21) against two control cohorts without AI(n = 24). Results show a significant performance decline in the AI-assisted group, with a mean final examination score(M = 58.8) over 20 points lower than the control groups' stable baseline(M = 79.2), a statistically significant difference(p < 0.001). This suggests that unguided AI use may encourage ªcognitive offloadingº, bypassing critical thinking and leading to superficial learning. The performance gap widens with diverse, high-order examination tasks, where AI-dependent students struggle in closedbook settings. The study concludes that the educational potential of AI requires careful integration with pedagogical guardrails, which serves as a cognitive scaffold rather than an answer machine, pending further validation.
Artificial intelligence empowering software engineering education: Opportunities, challenges, and future prospects
Chunwei Tian;Hengxin Qiu;Qi Zhu;Jianfang Hu;As a core field of information technology, the quality of software engineering education directly impacts the development of the future software industry. Current pedagogy, however, faces critical challenges including rapid knowledge obsolescence, inadequate practical skill development, limited personalization, and complex assessment. This paper systematically explores AI's transformative potential in this domain, proposing an application framework that addresses content innovation, skill cultivation, and assessment optimization. We critically analyze implementation opportunities while addressing technical constraints, pedagogical adaptations, and ethical considerations. The contribution of this paper lies in providing a macroscopic and forwardlooking theoretical analysis framework, which offers references for in-depth research and practice of AI in the field of software engineering education.
Economic value and management pathways of China-Europe software engineering education cooperation under the belt and road initiative
Xinyue Liu;Xiao Zheng;Zhiwei Ye;Amid the in-depth advancement of the Belt and Road Initiative and the rapid development of the global digital economy, cross-border cooperation in software engineering education between China and Europe has emerged as a pivotal strategy for enhancing regional digital economic competitiveness. This paper aims to systematically analyze the promotion mechanisms of such cooperation and its impact on regional digital economic development. It will also explore key management issues, including intellectual property protection and the mutual recognition of standards within cross-border educational collaborations. Furthermore, the paper will propose strategies for enhancing international industrial competitiveness through educational partnerships. The research seeks to provide both theoretical and practical insights to deepen China-Europe cooperation in software engineering education and unlock the full potential of the digital economy.
Research on the Construction of a software engineering course knowledge graph empowered by the Xhang AI teaching assistant
Xuefei Huang;Yiming Gai;Ying Li;Hao Sheng;Da Yang;Yingying Zhang;In view of the needs and challenges of digital transformation in the field of education under the background of ªnew engineeringº, this paper analyzes how to improve the teaching efficiency of software engineering course programming and realize personalized guidance through the large language models, and proposes to use the multi-channel feedback optimization technology of Xhang AI assistant to extract and construct the multimodal knowledge graph of the adaptive course, and realize the personalized fine-tuning of the model combined with the private data collaborative sharing platform, so as to enhance the accuracy and applicability of the knowledge graph of software engineering course. The automatic construction and updating of curriculum knowledge graph based on ªXhang AI assistantº can cover a wider range of educational courses and fields, and promote the development of intelligent and digital education.
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Reform and exploration of programming courses for postgraduates
Yi Ma;Yixian Liu;Dongming Chen;Programming-related courses are undoubtedly an extremely crucial and important part of the postgraduate training plan. For postgraduate students to carry out research work and promote related projects, their importance is self-evident and plays a vital role. In the specific implementation process of actual teaching activities, programming courses have exposed some deficiencies in many aspects such as the speed of content update and the cultivation of practical ability for research projects. These problems all need to be further improved and optimized. Precisely because of this, curriculum reform has become an inevitable trend and is imperative. Under such circumstances, this article deeply analyses the main problems presented by the current programming courses, comprehensively and systematically explores the effective ways to solve these problems, and further puts forward a brand-new teaching model and evaluation method.
Exploring and practicing an intelligent computational thinking cultivation model for new liberal arts students
Zan Wang;Weijing Wang;Hanmo You;Against the backdrop of digital transformation and rapid development of artificial intelligence, the development of New Liberal Arts requires the deep integration of humanities and social sciences with modern information technology to cultivate compound talents with digital literacy. However, current technical training for liberal arts students still lags behind industry demands, manifested in three key issues:(1) lack of problem-driven instructional design,(2) lack of progressive teaching examples, and(3) lack of tiered instructional support. To address these challenges, we propose an intelligent computational thinking cultivation model based on ªdisciplinary context anchoring, cognitive ladder construction, and dynamic framework adjustmentº. First, through the content reconstruction based on discipline-specific context, technical concepts are embedded in liberal arts research cases to resolve the dilemma of ªwhat to learnº. Then, the cognition-based step-by-step teaching pathway gradually introduces technical concepts and solves complex problems in stages, solving the challenge of ªhow to learnº. Finally, the gradual dynamic adjustment supporting framework provides incremental instructional support, resolving the issue of ªhow to learn wellº. To validate the effectiveness, we implemented it in Tianjin University's Fundamentals of New Media Programming course, integrating Python with real-world journalism and communication problems. This systematic solution offers an effective pathway for liberal arts students to overcome the difficulties of technical learning, develop intelligent computational thinking, and meet the demand for interdisciplinary talent cultivation in the digital era.
Educational practice in software engineering empowered by generative AI and virtual humans
Yingying Zhang;Runze Liu;Hao Sheng;Xudong Li;Ying Li;Haogang Zhu;Da Yang;Xuefei Huang;The rapid advancement of information technology catalyzes the digital transformation of education, urging the education sector to adapt to technological changes actively. Focusing on the dual-engine drive of large models and digital human technologies, we innovate across multiple dimensions to reconstruct the software engineering curriculum system. We build multimodal teaching frameworks and personalized learning paths to break through traditional limitations, establish a digital and intelligent course resource supermarket for precise service supply, integrate smart classrooms with blended learning to reshape teaching spaces, and create an all-process digital and intelligent evaluation system to revolutionize assessment and feedback mechanisms. By deeply integrating intelligent technologies into the entire education process, we enhance software engineering course quality and offer new ideas and practical pathways for educational reform.
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PCER: A dynamic feedback model for optimizing graduate student training in software engineering
Bangchao Wang;Yuyang Dai;Hongyan Wan;Xinrong Hu;Xiong Wei;Under the national innovation-driven development strategy and the emerging need for new engineering education, the cultivation of high-quality software engineering talent faces increasingly stringent requirements. To address critical challenges in the current graduate training model at regional universities, including fragmented undergraduate-to-graduate transition, ambiguous competency development pathways, and structural deficiencies in teaching resources, this paper proposes a novel PCER(Plan-Carry Out-Evaluate-Refine) dynamic feedback model for software engineering graduate education. The PCER framework establishes phased training objectives, develops an adaptive multi-level resource repository, and implements a multidimensional evaluation mechanism. Empirical results demonstrate that the model significantly enhances students' academic innovation capabilities and engineering practice competencies, providing a replicable reform path for the cultivation of software engineering talents. The PCER approach offers a transferable solution for engineering education reform, particularly for institutions facing similar resource constraints and quality improvement challenges.
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