Teaching Design of Course Building Decoration Materials Based on Generative Artificial Intelligence

With the digital transformation of global education and China's emphasis on education digital,generative AI technology has been widely used in the field of higher education.In this paper,the development of generative AI technology and its potential in personalized learning,interactive content creation and adaptive assessment in education were introduced firstly.Then,the application case of generative AI tools in teaching content creation,scenario-based teaching content development,visual teaching content development,complex concept deconstruction and analogy,student-led application practice and other aspects in the teaching of Building Decoration Materials was discussed.Through the teaching experiment and effect evaluation,the positive influence of generative AI technology on the improvement of students'learning effect and teaching efficiency was verified.Finally,some thoughts and inspirations on the combination of educational theory and generative AI technology,the integration of teaching design and generative AI technology,and the practice cases and effect evaluation were put forward,and the importance of teacher role transformation and personalized learning path design was emphasized to provide theoretical and practical support for the innovative development of higher education.

Cultivation of Critical Thinking Skills: Exploring the Impact of Generative Artificial Intelligence- Enabled Instruction in English Essay Writing

This study explores the impact of generative artificial intelligence(AI)-enabled instruction on critical thinking in English essay writing among 1,050 first-year English majors across four colleges.Pedagogical strategies,including facilitating critical responses and emphasizing real-world application,are identified to enhance generative AI’s impact.Both qualitative and quantitative analyses reveal significant post-intervention improvements in critical thinking skills.This research contributes to understanding how generative AI can effectively foster critical thinking in educational settings.

Revisiting Educational Issues in the Age of Generative Artificial Intelligence

The emergence of generative artificial intelligence(AI)has had a huge impact on all areas of life,including the field of education.AI can assist teachers in cultivating talents and promoting personalized learning and teaching,but it also prevents individuals from thinking independently and creatively.In the era of generative AI,the rapid development of technology and its significant impact on the field of education are inevitable.There are many educational issues related to it,such as teaching methods,student training goals,teaching philosophy and purposes,and other educational issues,that require re-conceptualization and review.

Exploring Pedagogical Ideologies and Strategies for College English Writing Instruction from a Generative Artificial Intelligence Perspective

This study,drawing on the commonalities between generative artificial intelligence and foreign language writing instruction,outlines the core ideology of digital humanities-based college English writing instruction,including auxiliary use of generative artificial intelligence tools,primary focus on humanistic education,and the re-production of knowledge,aiming to foster students’critical thinking,collaborative skills,and creativity.Building on this foundation,the study delves into generative artificial intelligence tools applicable to different stages of process-genre writing and their strategic applications.The use of generative artificial intelligence tools is beneficial for students to present,discuss,and share writing content,encouraging them to enhance their writing,collaboration,critical thinking,and creative abilities through deep interaction with model essays and creative discourses.

Towards data-efficient mechanical design of bicontinuous composites usinggenerative AI

The distribution of material phases is crucial to determine the composite’s mechanical property.While the full structure-mechanics relationship of highly ordered material distributions can be studied with finite number of cases,this relationship is difficult to be revealed for complex irregular distributions,preventing design of such material structures to meet certain mechanical requirements.The noticeable developments of artificial intelligence(AI)algorithms in material design enables to detect the hidden structure-mechanics correlations which is essential for designing composite of complex structures.It is intriguing how these tools can assist composite design.Here,we focus on the rapid generation of bicontinuous composite structures together with the stress distribution in loading.We find that generative AI,enabled through fine-tuned Low Rank Adaptation models,can be trained with a few inputs to generate both synthetic composite structures and the corresponding von Mises stress distribution.The results show that this technique is convenient in generating massive composites designs with useful mechanical information that dictate stiffness,fracture and robustness of the material with one model,and such has to be done by several different experimental or simulation tests.This research offers valuable insights for the improvement of composite design with the goal of expanding the design space and automatic screening of composite designs for improved mechanical functions.

Potential and limitations of ChatGPT and generative artificial intelligence in medical safety education

The primary objectives of medical safety education are to provide the public with essential knowledge about medications and to foster a scientific approach to drug usage.The era of using artificial intelligence to revolutionize medical safety education has already dawned,and ChatGPT and other generative artificial intelligence models have immense potential in this domain.Notably,they offer a wealth of knowledge,anonymity,continuous availability,and personalized services.However,the practical implementation of generative artificial intelligence models such as ChatGPT in medical safety education still faces several challenges,including concerns about the accuracy of information,legal responsibilities,and ethical obligations.Moving forward,it is crucial to intelligently upgrade ChatGPT by leveraging the strengths of existing medical practices.This task involves further integrating the model with real-life scenarios and proactively addressing ethical and security issues with the ultimate goal of providing the public with comprehensive,convenient,efficient,and personalized medical services.

A Discussion of Artificial Intelligence in Visual Art Education

Since ChatGPT emerged on November 30, 2022, Artificial Intelligence (AI) has been increasingly discussed as a radical force that will change our world. People have become used to AI in which such ubiquitous technologies as Siri, Google, and Netflix deploy AI algorithms to answer questions, impart information, and provide recommendations. However, many individuals including originators and backers of AI have recently expressed grave concerns. In this paper, the authors will assess what is occurring with AI in Visual Arts Education, outline positives and negatives, and provide recommendations addressed specifically for teachers working in the field regarding emerging AI usage from kindergarten to grade twelve levels as well as in higher education.

Digital Generative Multimedia Tool Theory (DGMTT): A Theoretical Postulation

The development of digital technology has brought about a substantial evolution in the multimedia field.The use of generative technologies to produce digital multimedia material is one of the newer developments in this field.The“Digital Generative Multimedia Tool Theory”(DGMTT)is therefore presented in this theoretical postulation by Timothy Ekeledirichukwu Onyejelem and Eric Msughter Aondover.It discusses and describes the principles behind the development and deployment of generative tools in multimedia creation.The DGMTT offers an all-encompassing structure for comprehending and evaluating the fundamentals and consequences of generative tools in the production of multimedia content.It provides information about the creation and use of these instruments,thereby promoting developments in the digital media industry.These tools create dynamic and interactive multimedia content by utilizing machine learning,artificial intelligence,and algorithms.This theory emphasizes how crucial it is to comprehend the fundamental ideas and principles of generative tools in order to use them efficiently when creating digital media content.A wide range of industries,including journalism,advertising,entertainment,education,and the arts,can benefit from the practical use of DGMTT.It gives artists the ability to use generative technologies to create unique and customized multimedia content for its viewers.

基于AI智能生成工具Generative Fill的PS课程教学改革

随着人工智能技术的不断发展,AI智能创作技术得到了广泛应用。Photoshop Beta 2023 AI智能生成工具Generative Fill(生成式填充),不仅能根据关键词的要求,把原图直接扩展,还可以更换画面背景元素等。AI智能工具如今已经在软件操作上带来了巨大的优化更新,给传统的PS教学方式带来了历史性变革。文章通过对AI智能生成工具Generative Fill的分析和研究,探索PS课程教学改革,为设计类专业提供教学参考。

Toward Artificial General Intelligence: Deep Reinforcement Learning Method to AI in Medicine

Artificial general intelligence (AGI) is the ability of an artificial intelligence (AI) agent to solve somewhat-arbitrary tasks in somewhat-arbitrary environments. Despite being a long-standing goal in the field of AI, achieving AGI remains elusive. In this study, we empirically assessed the generalizability of AI agents by applying a deep reinforcement learning (DRL) approach to the medical domain. Our investigation involved examining how modifying the agent’s structure, task, and environment impacts its generality. Sample: An NIH chest X-ray dataset with 112,120 images and 15 medical conditions. We evaluated the agent’s performance on binary and multiclass classification tasks through a baseline model, a convolutional neural network model, a deep Q network model, and a proximal policy optimization model. Results: Our results suggest that DRL agents with the algorithmic flexibility to autonomously vary their macro/microstructures can generalize better across given tasks and environments.

“生成式人工智能”(AIGC)及其哲学意蕴

从“生成式的AI”或“生成式的人工智能”(AIGC)的角度看,需要关注“自然生成”“人工智能生成”“社会生成”或“伦理生成”之间的关联和区分。人工智能具有确定性与生成性,它从一个侧面体现了being与becoming的沟通和统一。从传统意义上的人禽之辨到人机之辨,体现了重要的转换,但二者在本质上都关乎何为人的追问。人工智能所体现的理性具有非原创的意义,人所具有的理性推论能力作为人的本质,则具有原创性。相对于人而言,AI作为一种机器,归根到底还是属于“器”,只具有工具意义,不具有独立的人格,也难以获得伦理主体的地位。此外,人工智能主要表现为人的创造的一种结果,真正的原创意义上的智能只有人才具有。从这一意义上说,只有人才是原始的创造者,把人工智能看作比人更高级的动物,并不合乎事实。人工智能发展可以取代很多人的工作,这在本质上如同在近代工业的发展过程中,机器不断取代手工操作,二者情形相近,原理也一致。从更深远的意义上说,这也是人不断地走向人性化的社会、达到真正自由存在形态的一个环节或前提。科技的发展可以使我们对世界的细节、对某些方面越来越深化,但是总体上对世界的把握离不开哲学。另一方面,科学发展具有自主之性或惯性,后者需要哲学为其提供价值引导。

如何走出AIGC的“科林格里奇困境”:全流程动态数据合规治理

[目的/意义]AIGC的快速发展带来了安全风险与规制问题,遵循AIGC的发展趋势构建全流程动态数据合规治理机制,能够帮助AIGC走出“科林格里奇困境”。[研究设计/方法]通过梳理AIGC与数据治理之间的赋能与保障关系,结合AIGC规制的国际实践经验进行“科林格里奇困境”规制博弈分析,探析AIGC创新发展与规制的路径。[结论/发现]无论是欧盟的强硬规制、中国的谨慎监管还是美国的行业自律,都在不同维度显现出规制的局限性,可以通过全流程动态化规制手段适配AIGC的动态化发展趋势,构建基于数据合规治理的AIGC全流程动态规制机制,帮助AIGC走出困境。[创新/价值]基于数据合规治理的AIGC动态规制机制能够不断改进规制体制和工具,为AIGC相关技术落地具体应用领域提供思路。

组态视角下AIGC应用平台用户中辍行为影响因素研究

[目的/意义]从组态视角对AIGC应用平台用户的中辍行为影响因素进行研究,明确人工智能技术服务的痛点与边界,更好地优化服务,以促进技术赋能。[方法/过程]使用问卷收集219份有效数据,采用扎根编码与定性比较分析相结合的方法,对影响AIGC应用平台用户中辍行为的因素进行识别,并构建相应的理论模型,揭示引致AIGC应用平台用户中辍行为发生的条件组合。[结果/结论]将AIGC应用平台用户中辍行为归纳为潜在威胁感知型和技术因素主导型两种模式,发现感知威胁、期望失验、系统质量、内容质量在AIGC应用平台用户中辍行为中发挥着重要作用。基于上述研究结论,对后续的研究视角提出了相关建议。

AIGC可版权性认定的一般规则构建

以ChatGPT-4为代表的新一代生成式人工智能迅速普及,其“作品”的可版权性也备受争议。我国近期的AI文生图著作权侵权第一案,肯定了涉案AIGC“春风送来了温柔”的作品属性,无疑鼓舞了生成式人工智能的新业态。但个案裁判与规则化的认定标准,在效力上仍然相距甚远,不应因此便盲目乐观。从制度上构建AIGC作品认定的一般规则,仍是新业态发展亟待解决的问题。在AIGC超强“智能性”旨在深度理解“人类指令”,并非缔造独立创作主体的逻辑前提下,还原生成式人工智能在新型“人机合作”中的“工具地位”。以“提示工程”代表“自然人创作”的法律定性为逻辑起点,准确开展AIGC可版权性一般规则的构建,确立AIGC专门作品类型,避免出现提示词输入等于画家创作的常识性悖论。

生成式AI时代大学生智能学习助手:框架、挑战与应对

生成式AI时代人工智能技术的快速发展,为大学生智能学习助手的产生和发展提供了坚实的技术支撑。研究以联通主义学习教学交互与认知参与模型为指导,针对生成式AI时代大学生智能学习助手的设计框架、现实挑战与建设进路展开。研究发现,生成式AI时代大学生智能学习助手的设计框架包括三大功能:作为知识获取的智慧平台,为学习者提供友好的学习交互体验的基础功能;作为知识交流的智能座舱,支持学习者的深度学习和研究活动的进阶功能;作为知识生产的创新引擎,促进学生全面持续性发展的高级功能。生成式AI时代大学生智能学习助手的现实挑战包括技术内容可靠性困境、互动与创生困难以及数据的伦理难题。为此,研究提出了大学生智能学习助手的建设进路:一是整合多元资源,提升智能学习助手的问答准确性;二是提升智能素养,促进师生与智能学习助手良性互动;三是完善政策标准,保障智能学习助手的顺利运行。

基于AIGC协同工业设计流程的气道廓清辅具设计研究与实践

目的设计领域引入蓬勃发展的AIGC技术以期提高效率,在此基础上结合大数据技术和双钻模型,以提高AIGC应用于复杂产品工业设计的质量。方法提出了一种AIGC协同的“1+2”钻工业设计流程模型,通过三次发散、三次收敛来提高AIGC协同工业设计的流程效率与方案质量。首先,在ChatGPT和Python大数据爬取辅助下发散需求问题,在Python高频词分析、竞品分析、专家评审下收敛设计目标;其次,运用Midjourney的Stop参数模糊生图进行创意发散,通过对项目人员问卷调查收敛设计方向;最后,联合使用DALL·E、StableDiffusion和Midjourney发散设计细节,通过焦点小组收敛以确定最终方案并建模渲染。结果实现全面、专业、准确的长期卧床老人气道廓清的需求分析,且辅具设计方案的在用户友好程度、造型美观度、技术可行性和功能可用性方面获得了设计与工程双方专业人员的认可。结论AIGC、大数据与传统工业设计方法相结合的“1+2”钻设计流程,能够通过人工控制AIGC的参考内容、处理AIGC的生成内容,以及阶段性专家评估有效地提升AIGC协同的工业设计方案质量,为规范化的AIGC协同工业设计流程提供思路。

生成式AI对个人信息保护的挑战与风险规制

生成式AI的技术特性使其对《个人信息保护法》建构的个人信息保护体系产生了挑战,主要包括:生成式AI虽然解决了让语言模型使用超大体量无人工标注数据进行预训练的难题,但其采用的技术路线也使大语言模型成为完全的“技术黑箱”,进而使开发者难以遵守个人信息处理的知情同意规则;无论是依据“目的限定原则”还是“场景理论”,生成式AI的技术特性都使其难以满足在“合理范围”内处理已公开个人信息的法定要求;生成式AI的技术特性使大语言模型的输入端和输出端都存在对信息主体的敏感个人信息权益和个体隐私权的侵害风险。我们应该基于“包容审慎”的基本风险规制理念,通过调整生成式AI领域知情同意规则的适用方式、重塑生成式AI领域已公开个人信息的处理规则、设立生成式AI领域个人信息中人格权保护的行政规制措施等途径,实现创新技术发展和个人信息保护之间的平衡。

论复杂人工智能生成物在著作权法的定性--兼评“AI文生图著作权案”

对于AI生成物的可版性认定,我国司法实践采取了“简单—复杂”场景二分的裁判思路。然而,囿于对独创性概念的有限阐释,司法机关并未对该问题形成逻辑融贯的论证,进而引发争议。对此,有必要以复杂生成场景为定位,并沿袭独创性概念的规范阐释脉络,从客观、主观和行为三个维度来回应争议:第一,著作权法并仅不限于保护“大师”作品,生成式AI“排列组合式”的创作也能满足客观面向的“一定美感”;第二,复杂AI生成物具有高度定制化的特征,因此具备主观面向的“个性因素”,“人机二分说”将工具误认为主体,“算法唯一说”缺乏对创作流程的完整考量;第三,《著作权法实施条例》第三条第一款的“直接”应解释为“主要贡献”而非“直接决定”,后者既与域内外的一贯实践不符,也有悖于社会化创作的现代艺术发展规律。

“助”与“替”:生成式AI对学术研究的双重效应

生成式AI应用于学术研究,揭开了知识生产的新篇章,它可以通过“助”读、“助”知、“助”写和“助”思来助力人的学术研究,自动化一些准备性或辅助性的工作,减轻人的认知负荷,使人将主要精力集中学术创新的关键环节,提高学术知识生产的效率。但对于生成式AI辅助功能的过度使用,可能导致“替”读、“替”知、“替”写、“替”思等负面效应,带来人的学术能力多向度退化。如何“助而不替”地合理使用生成式AI,是智能时代人机合作进行学术知识生产的新课题。

基于生成式AI的智能制造专业型应用框架研究与应用

智能制造是全面实现现代化的重要保障。随着人工智能技术的不断发展,如何利用生成式AI进一步促进智能制造质量与效率的提升是十分重要的研究课题。本文按照智能制造生命周期维度划分,结合生成式AI的基本思想和原理,提出了基于生成式AI的智能制造专业型应用框架(SMPF-GAI),并通过案例分析了生成式AI在智能制造中的应用潜力。该框架系统地整合了智能制造的关键阶段与生成式AI的核心原则,描绘了生成式AI在智能制造中的运行机理和应用路线。本研究为智能制造领域的研究人员、从业者和决策者提供了新的见解,为有效开展智能制造工作、提高智能制造建设水平提供理论支撑及方法指导。