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关键词： Academic student writing   Generative AI   Large language models   Ethics  

摘要： The new version of ACM and IEEE-CS Computer Science Curricula envisages the preparation of a dozen white papers within more than a third of the Body of Knowledge courses. Lack of experience in conducting research, insufficient ability to articulate thoughts, non-observance of defined recommendations, as well as forgotten verification of what has been done are key challenges in the implementation of these recommendations. Large language models (LLMs) can significantly support the creation of anticipated white papers, encouraging initial directions and inspiration for research, as well as a focused presentation of key elements of the work. Unfortunately, they can also hinder the creation of expected academic texts, promoting the superficiality of research and the presentation of unverified information. In order to facilitate progress in acquiring the necessary writing skills without discriminating against those students who have already acquired them and prefer traditional writing, we have defined a series of strict rules and strategies for preparing the briefing reports with complete, partial, or no reliance on the LLM. They were embedded into the faculty learning management system, implemented, and evaluated twice with more than 150 students during the academic year 2023/24. This paper presents a new approach along with the lessons learned from the first attempt and the changes incorporated into the second. Based on these two carefully designed, conducted and analyzed studies, teachers and students gained a very positive opinion about the impact of LLM on the creation of quality student reports. The effectiveness of the new approach is a guarantee that optimization of the integration of AI-generated content can become a benchmark for the successful preparation of student reports that can be recommended to the wider academic community.

摘要： This paper explores the dynamics of international collaborations in Computer Science, focusing on the roles of the three Publishing Superpowers, those are China, the European Union, and the United States. Leveraging a comprehensive literature review, we investigate collaboration patterns, research impact, retraction rates, and the impact of the Development Index on research outcomes. Our analysis reveals that while China, the EU, and the US play central roles in driving global research endeavors, other regions might shorten the distance between them in terms of the number of publications. Notably, collaborations involving these key regions exhibit lower retraction rates, underscoring their adherence to rigorous scientific practices. Furthermore, our findings highlight the influence of a country’s Development Index on research impact, with Very High Developed countries contributing to higher citation rates and lower retraction rates. Overall, our study sheds light on the significance of international collaborations in shaping the global research landscape and underscores the importance of fostering inclusive and ethical research practices for advancing knowledge and innovation in Computer Science. © 2025, CC BY.

关键词： Semantics  

摘要： LLMs have been celebrated for their potential to help multilingual scientists publish their research. Rather than interpret LLMs as a solution, we hypothesize their adoption can be an indicator of existing linguistic exclusion in scientific writing. Using the case study of ICLR, an influential, international computer science conference, we examine how peer reviewers critique writing clarity. Analyzing almost 80,000 peer reviews, we find significant bias against authors associated with institutions in countries where English is less widely spoken. We see only a muted shift in the expression of this bias after the introduction of ChatGPT in late 2022. To investigate this unexpectedly minor change, we conduct interviews with 14 conference participants from across five continents. Peer reviewers describe associating certain features of writing with people of certain language backgrounds, and such groups in turn with the quality of scientific work. While ChatGPT masks some signs of language background, reviewers explain that they now use ChatGPT "style" and non-linguistic features as indicators of author demographics. Authors, aware of this development, described the ongoing need to remove features which could expose their "non-native" status to reviewers. Our findings offer insight into the role of ChatGPT in the reproduction of scholarly language ideologies which conflate producers of "good English" with producers of "good science." © 2025, CC BY-NC-SA.

关键词： Programming learning   Major differences   Sequence mining   Latent class analysis   Self-organizing maps  

摘要： BackgroundWith the increasing interdisciplinarity between computer science (CS) and other fields, a growing number of non-CS students are embracing programming. However, there is a gap in research concerning differences in programming learning between CS and non-CS students. Previous studies predominantly relied on outcome-based assessments, focusing on summative evaluations and surveys while providing little insight into the real learning process and differences therein. This study aims to provide a process-oriented comparison of programming learning between two novice student groups, CS and Math, under uniform instructional conditions, focusing on their semester-long scores, engagement, and code *** research involves 75 novice students enrolled in a compulsory introductory programming course designed for a mixed class, comprising 35 Math and 40 CS. Through Latent Class Analysis and Self-Organizing Maps, we identify distinct learning states throughout the semester and employ sequence mining to explore the differences in learning trajectories and state transitions between the two cohorts. Our results reveal that the association between engagement and scores diverges across different majors as the course progresses, deviating from the widely discussed positive correlation. In the semester-long code metrics analysis of students exhibiting over-engineering state, the two cohorts display opposing trends. Moreover, CS students demonstrate significant alignment between formative and summative scores, whereas Math peers exhibit phenomena of cold-start and learning *** study underscores the importance of understanding distinct learning trajectories to improve instructional design for diverse learner groups. Our findings indicate that CS students follow increasingly efficient learning patterns with decreasing code complexity over time, while Math students need strategies to overcome phenomena of cold-start and learning avoidance. Code met

关键词： Society   Software   Computer science   Public health   Psychology  

摘要： Studies suggest benefits as well as harms from digital companion apps — but scientists worry about long-term dependency.

关键词： Animal behaviour   Computer science   Ecology   Society  

摘要： Removal of experienced fish results in younger members not knowing migration routes — plus, how AI companion apps may affect our mental health.

关键词： 人工智能+   计算机专业   创新型人才培养  

摘要： 在“人工智能+”赋能教育的时代背景下,计算机专业创新型人才培养急需突破传统教育理念束缚,重新构建教育生态系统,培养满足企业需求的计算机专业创新型人才培养。本文首先收集了影响计算机专业创新型人才培养的各种现象,然后聚焦人工智能与计算机科学的深度融合,分析造成这些现象的原因,最后,构建“以赛促创”的生态化培养机制。通过学科竞赛的“问题导向-过程迭代-成果转化”三维驱动,全力挖掘学生的创新潜能,最大程度激发其创新热情,深度强化大学生的实践操作能力,致力于培育兼具创新思维的高素质创新型计算机专业人才,以满足软件行业对计算机专业创新型人才的迫切需求。

关键词： 场景理论   数字游戏   《黑神话:悟空》   文化传播  

摘要： 本文探讨了场景理论视角下,数字游戏《黑神话:悟空》如何通过精心设计的场景实现传统文化的创新传播。论文分析了游戏场景中的空间布局、自然景观、传统艺术形式、场景与角色互动的运用等文化元素,并阐述了这些元素如何增强玩家的文化体验。同时,论文总结了《黑神话:悟空》对数字游戏产业在文化融合与创新、技术手段应用、产业发展与文化传播协同方面的启示。

关键词： 应用型本科人才培养   趣味导向   实践强基   竞赛赋能  

摘要： 随着全球信息产业的快速发展,对应用型计算机专业人才的需求日益迫切。针对当前计算机专业教育中存在的理论与实践衔接不足、学生思维能力弱化等问题,本文提出了一种以“趣味导向、实践强基、竞赛赋能”的三阶融合应用型计算机专业人才培养模式。以趣味化教学重构知识体系,通过产教协同强化实践能力,依托竞赛驱动提升创新思维。实施结果表明,三阶融合实践教学体系、建立层级递进式竞赛体系显著提高了人才培养质量,促进了教学条件建设并提升了教学研究水平。With the rapid development of the global information industry, there is an increasingly urgent demand for application-oriented computer professionals. To address current issues in computer education, such as the disconnect between theory and practice and the weakening of students’ critical thinking, this paper proposes a three-stage integrated talent cultivation model based on the principles of “Interest Orientation, Practice Reinforcement, and Competition Empowerment”. The implementation of interest-driven pedagogical reconstruction to reshape the knowledge system, coupled with industry-education collaboration to enhance practical competencies, and a competition-oriented mechanism to foster innovative thinking. Results demonstrate that the three-stage integrated practical teaching framework and the establishment of a hierarchical, progressive competition system have markedly improved talent cultivation quality, advanced the development of teaching infrastructure, and elevated pedagogical research standards.