Artificial intelligence innovation in education: A twenty-year data-driven historical analysis

Reflecting on twenty years of educational research, we retrieved over 400 research article on the application of artificial intelligence (AI) and deep learning (DL) techniques in teaching and learning. A computerised content analysis was conducted to examine how AI and DL research themes have evolved in major educational journals. By doing so, we seek to uncover the prominent keywords associated with AI-enabled pedagogical adaptation research in each decade, due to the discipline’s dynamism. By examining the major research themes and historical trends from 2000 to 2019, we demonstrate that, as advanced technologies in education evolve over time, some areas of research topics seem have stood the test of time, while some others have experienced peaks and valleys. More importantly, our analysis highlights the paradigm shifts and emergent trends that are gaining prominence in the field of educational research. For instance, the results suggest the decline in conventional tech-enabled instructional design research and the flourishing of student profiling models and learning analytics. Furthermore, this paper serves to raise awareness on the opportunities and challenges behind AI and DL for pedagogical adaptation and initiate a dialogue.     

Artificial intelligence for digital sustainability: An insight into domain-specific research and future directions

Recent research call for action on digital sustainability research could potentially contribute to achieving United Nations (UN) Sustainable Development Goals (SDG). In this opinion piece, we specifically focus on artificial intelligence (AI) as a technology that could help achieve digital sustainability. We identify six dimensions related to AI grounded in past literature: sensemaking, relationships among actors in the supply chain, green creativity skills, metrics, strategies, and AI tool improvement. We conceptualize several propositions for these six dimensions, highlighting the nuances associated with AI for digital sustainability to provide clear directions for future research.     

Artificial intelligence in information retrieval systems

A survey is given of the potential role of artificial intelligence in retrieval systems. Papers by Bush and Turing are used to introduce early ideas in the two fields and definitions for artificial intelligence and information retrieval for the purposes of this paper are given. A simple model of an information retrieval system provides a framework for subsequent discussion of artificial intelligence concepts and their applicability in information retrieval. Concepts surveyed include pattern recognition, representation, problem solving and planning, heuristics, and learning. The paper concludes with an outline of areas for further research on artificial intelligence in information retrieval systems.     

Artificial intelligence and African conceptions of personhood

Ethics and Information Technology - Under what circumstances if ever ought we to grant that Artificial Intelligences (AI) are persons? The question of whether AI could have the high degree of moral...     

构建职业院校技术通识教育体系

当代社会进步、科技发展与职业岗位变动的时代背景中,职业教育需要在专业教育中加强技术通识教育,培养“宽口径、复合型”的应用技术人才。技术通识教育体系主要包括技术公共知识教育、通用技能教育以及技术基本规范教育等内容,对于促进学生职业发展发挥着奠基性作用。     

Artificial intelligence and knowledge management: questioning the tacit dimension

Knowledge management (KM) has matured to the point that many organisations either believe they have such practices in place or at least understand they are relevant to the knowledge work commonly undertaken in many industries. What is lacking from the literature, however, is a solid foundation for the philosophies underpinning KM and particularly for how tacit knowledge informs the KM space. Research over decades shows tacit knowledge underpins all other forms of knowledge, enabling the interpretation and judicious application of knowledge, leading (at its highest levels) to the concept of wisdom. As an academic discipline, artificial intelligence (AI) was established before KM, has been grounded in the computing discipline for many decades, and is applied broadly in many domains. This paper explores how AI can inform the KM debate. Rather than simply provide examples of AI success stories as applied to KM in practice, it explores the theoretical and practical limitations of AI and KM in unison, providing at the same time a strong epistemological understanding of both disciplines as a means of furthering the knowledge debate, with particular emphasis on the role of tacit knowledge within this jurisdiction.     

产学研协同创新中智力资本的转化路径研究

产学研协同创新智力资本转化过程中,人力资本通过角色转换和知识溢出实现个体人力资本向组织人力资本的转化;结构资本转化包括个体与母组织的关系重构和协同创新中心结构资本的创立和优化;关系资本的转化包括利用母组织建立间接关系资本和发展直接关系资本。三维度在转化的过程中相互作用,形成了以人力资本为起点和核心、结构资本为基础、关系资本为保障的协同关系。     

人工智能赋能下产业创新生态系统的双重转型

当前,人工智能赋能不断推进技术创新和应用创新进入“双拐点”时期,正在从要素构成、功能边界、生态互动等多个维度对产业创新生态系统理论提出新挑战。本文集成自然生态系统和产业创新系统等相关理论,基于“核心驱动层-样态变革层”构建人工智能赋能下产业创新生态系统双重转型的理论框架,并以无人驾驶为例,深入剖析产业创新生态系统双重转型的运行逻辑和实践变革。研究发现结论如下：一是创新主体生态功能延展催生多宿主体和次生群落的出现,使得产业创新生态系统新型生态互动关系逐渐形成,成为产业创新生态系统由“旧四螺旋创新”向“新四螺旋创新”演进的核心动源;二是产业创新生态系统核心驱动与样态变革的相互作用日益增强,多宿主体和次生群落不断推动产业样态快速变革,使得产业样态呈现出价值分配重构、智能制造升级、商业模式转变、组织决策变革的发展特征。     

人工智能与现代叙词表和分类法的革新

叙词表和分类法现代化的基础是计算机能够理解和处理其概念之间的关系,并建立可以理解的模型;同时要使其内容事务化。在构建计算机理解模型中。人工智能的产生式系统和框架方法可以为分类法和叙词表的革新提供有益的借鉴,这就是用产生式系统和框架表示法给分类法和叙词表提供过程性知识推导与结构性知识表示,借以建立分类模型和等级知识系统。     

浅议高职院校数字校园建设

数字校园是信息时代学校教育发展的必然，它是一个以计算机网络为基础的层次化模块结构系统。建设高职院校数字校园，应根据五个基本愿则，结合职业教育的实际情况，有针对性地进行。     

Artificial intelligence in science: An emerging general method of invention

This paper offers insights into the diffusion and impact of artificial intelligence in science. More specifically, we show that neural network-based technology meets the essential properties of emerging technologies in the scientific realm. It is novel, because it shows discontinuous innovations in the originating domain and is put to new uses in many application domains; it is quick growing, its dimensions being subject to rapid change; it is coherent, because it detaches from its technological parents, and integrates and is accepted in different scientific communities; and it has a prominent impact on scientific discovery, but a high degree of uncertainty and ambiguity associated with this impact. Our findings suggest that intelligent machines diffuse in the sciences, reshape the nature of the discovery process and affect the organization of science. We propose a new conceptual framework that considers artificial intelligence as an emerging general method of invention and, on this basis, derive its policy implications.     

数字经济时代中小企业的数字化转型研究

以5G、大数据、人工智能、云计算、AR/VR、区块链等为代表的新一代信息技术正在日益成为全球经济发展的重要驱动力.在数字经济发展中,中小企业是不可忽视的重要主体.本文基于文献和案例研究,认为数字经济时代广泛的数字化转型正在重构产业生态,中小企业面临实现规模经济、拓展应用场景的新机遇,并提出加快中小企业数字化转型的措施建议,以期为相关实践和理论研究提供参考.     

职业教育与乡土中国的改造

长期的城乡二元经济结构和以城市为中心的政策导向造成了乡土中国与城市中国之间的天壤之别,农村成了被世人遗忘的角落,一个“土”字即能生动体现出农民所遭受的歧视和不公正待遇。中国现今仍是一个     

科技成果转化过程中的风险与对策研究

科技成果转化具有高风险与高收益并存的特征，科技成果转化过程中的风险，是影响我国科技成果转化效率提高的关键因素。针对我国科技成果转化率偏低的现状，在系统分析科技成果转化风险的基础上，提出了防范风险、提高科技成果转化率的对策。     

The art of digital transformation as crafted by a chief digital officer

Chief Digital Officers (CDOs) play a multitude of roles as they facilitate digital transformation. In this opinion paper, we draw on an interview that we undertook with the CDO at a major Chinese software and technology services firm to highlight the intricacies and idiosyncrasies of digital transformation work both within the organization and externally to clients. We characterise the nature of the CDO’s role as ‘the art of digital transformation’, recognising the blend of business and technology knowledge with superior communication and persuasion skills.     

从价值观的嬗变看技术创新的生态化转向

生态危机本质上也是一种价值观危机,技术创新的传统价值观在当代种种现实问题面前已经失去了原来的合理性,面临着各个层面的调整与转变。技术创新价值观的嬗变在理论和实践上的最终结果都将是技术创新的生态化转向。     

国家科技成果转化（南宁）综合信息服务平台的设计

科技成果转化综合服务平台是科技成果转化服务基地的重要组成部分,是推广科技超过转化的重要手段。文章介绍了国家科技成果转化（南宁）综合信息服务平台的产生背景、主要功能以及组成结构。     

Artificial intelligence implication on energy sustainability in Internet of Things: A survey

The massive number of Internet of Things (IoT) devices connected to the Internet is continuously increasing. The operations of these devices rely on consuming huge amounts of energy. Power limitation is a major issue hindering the operation of IoT applications and services. To improve operational visibility, Low-power devices which constitute IoT networks, drive the need for sustainable sources of energy to carry out their tasks for a prolonged period of time. Moreover, the means to ensure energy sustainability and QoS must consider the stochastic nature of the energy supplies and dynamic IoT environments. Artificial Intelligence (AI) enhanced protocols and algorithms are capable of predicting and forecasting demand as well as providing leverage at different stages of energy use to supply. AI will improve the efficiency of energy infrastructure and decrease waste in distributed energy systems, ensuring their long-term viability. In this paper, we conduct a survey to explore enhanced AI-based solutions to achieve energy sustainability in IoT applications. AI is relevant through the integration of various Machine Learning (ML) and Swarm Intelligence (SI) techniques in the design of existing protocols. ML mechanisms used in the literature include variously supervised and unsupervised learning methods as well as reinforcement learning (RL) solutions. The survey constitutes a complete guideline for readers who wish to get acquainted with recent development and research advances in AI-based energy sustainability in IoT Networks. The survey also explores the different open issues and challenges.     

班级“差”生在素质教育中的转化

"差"生问题行为的成因极为复杂.既有外在的社会环境,学校教育的因素,也与学生本人生理、心理的内在因素有关."差"生的转化是一项长期而艰巨的任务.它不是一朝一夕的工夫能取得成效的,也不是靠简单的方法能够完成的.这项工作的开展需要教师,家庭和社会共同协作.