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01 September 2022, Volume 40 Issue 9 Previous Issue   

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Artificial Intelligence Promotes the Development of Future Education: Essential Connotation and Proper Direction Xiaoqing Gu, Shijin Li 2022, 40 (9):  1-9.  doi: 10.16382/j.cnki.1000-5560.2022.09.001 Abstract ( 991 )   HTML ( 347 )   PDF (724KB) ( 1117 )   Save The development of artificial intelligence technology brings both opportunities and challenges to the education system. Based on the major social science project “Research on Artificial Intelligence Promoting the Development of Future Education”, the study found out the key focus points of artificial intelligence to promote the development of future education: artificial intelligence highlights the challenges of innovative talent development; artificial intelligence supports the personalized realization of large-scale education; artificial intelligence reshapes the concept of knowledge and teaching innovation; artificial intelligence empowers future teacher development research; and artificial intelligence promotes the systematic update of the education ecosystem. At the same time, technology empowerment education, technology innovation education, and technology reshaping education are the advanced forms of the “trilogy” of artificial intelligence to promote the development of future education. To explore the core mechanism of artificial intelligence to promote future education development, it is necessary to reveal the interaction law of “technology-education-society” from the perspective of complex systems, explore the essential impact of artificial intelligence on educational development from the perspective of learning science, and predict the development trend of future education from the perspective of “history-culture”. On this basis, it's important to grasp the development strategy of innovative talents driven by the intelligent era, focus on the key elements of education innovation driven by artificial intelligence, reshape the blueprint of future education ecology boosted by artificial intelligence, so as to create a “high-quality and warm” artificial intelligence education new ecology in China. Figures and Tables | References | Related Articles | Metrics

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Study on the Relationship Between New Demands of Human Capital and Educational Reform in Intelligent Era Sifeng Jing, Xiwei Liu, Xiaoyan Gong, Hongxia Zhao 2022, 40 (9):  10-18.  doi: 10.16382/j.cnki.1000-5560.2022.09.002 Abstract ( 426 )   HTML ( 258 )   PDF (884KB) ( 501 )   Save In intelligent era, the demand for talents has changed. First, human’s working and living space is extended to cyber-physical-social systems, which are characterized by human-computer collaboration and integration, and the complementarity of human intelligence and machine intelligence. This requires people to have new basic competences for intelligence era. Second, competition among countries is intensifying, and intelligent industries are rapidly developing with the deep and intensive application of the artificial intelligence (AI), and compound talents with the background of cross-border, cross-field and multidisciplinary integration are demanded. Third, the rapidly developing AI has also brought a lot of confusion and anxiety to society due to issues such as ethical governance, security and trust, and the development of humanities and social science should be gained more attention than ever. As a result, the demand for talents has brought many challenges to the existing education system, such as teaching and learning models, curriculum systems, teacher team building, and evaluation systems, and it is imperative to implement educational reforms, and it is demonstrated that the two-way empowerment between technology and education is the only way to promote educational reform. Figures and Tables | References | Related Articles | Metrics

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How Natural Language Processing Technology Empowers the AIED: The Perspective of AI Scientist Bo Zhang, Ruihai Dong 2022, 40 (9):  19-31.  doi: 10.16382/j.cnki.1000-5560.2022.09.003 Abstract ( 861 )   HTML ( 302 )   PDF (1287KB) ( 1403 )   Save Natural language processing (NLP) is one of the most important research branches of artificial intelligence (AI). With the boosting of computer performance and the construction of large-scale corpora in the last decade, NLP technology has made great progress and has been widely applied in various areas, especially in the field of education. Specifically, in this paper, we investigate the present research and the trends of NLP technology, and how NLP promotes the development of artificial intelligence in education (AIED) through studying and analyzing publications, reports, and speeches, etc. from eminent domestic and international AI specialists. We are aiming to explore the direction and trend of AIED in the future. Figures and Tables | References | Related Articles | Metrics

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Research on Learning Affective Computing in Online Education: From the Perspective of Multi-source Data Fusion Xuesong Zhai, Jiaqi Xu, Yonggu Wang 2022, 40 (9):  32-44.  doi: 10.16382/j.cnki.1000-5560.2022.09.004 Abstract ( 871 )   HTML ( 127 )   PDF (2774KB) ( 1397 )   Save Learning affection is an essential factor affecting learning performance, perception, and higher-order thinking ability. Existing research on learning affective computing is mainly based on a small sample analysis of heavyweight physiological feedback technology. There is a lack of learning affective computing research in online courses. In the online course environment, the data sources for learning affective computing are relatively limited, mainly based on a single facial expression data. On the other hand, learners are often under insufficient supervision in online learning scenarios. The body posture is more random, so it is very likely to affect the extraction of facial features. However, this study believes that the pose of online learners also has affective characteristics and is also a key source of affective information. Therefore, we try to fuse the learner’s posture data into the facial expression data, build a multi-source data fusion deep learning affective calculation model, and make up for the facial recognition defects caused by the learner’s posture change. Also, we perform collaborative analysis multi-source affective data to realize data cross verification and mutual compensation. The research concludes that it is an effective method for online learning affective calculation to build a dataset containing 7,878 facial expressions and posture images of online learners constructed through training, and use the convolutional neural networks and decision fusion methods to integrate learner posture data into facial expression data.The accuracy of affections recognition increases 3%, compared with a single facial expression recognition. In theory, this research provides a model basis for the effectiveness of multi-source data fusion in learners’ affective computing. In practice, it provides an effective technical path to learning affective computing in an online education environment. Figures and Tables | References | Related Articles | Metrics

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How to Build Future School: Prospective Analysis and Practical Enlightenment of Application Scenarios of AI in Education Huiying Cai, Haixia Dong, Xu Chen, Xiaoqing Gu 2022, 40 (9):  45-54.  doi: 10.16382/j.cnki.1000-5560.2022.09.005 Abstract ( 365 )   HTML ( 80 )   PDF (677KB) ( 895 )   Save From the perspective of time change, the development of learning technology has an impact on the state, operation mechanism and cultural atmosphere of schools in different social and historical periods. Therefore, in the case of artificial intelligence as the main driving force of future social development, what kind of future schools will appear and how we should effectively build future schools are important topics of current education field. In order to respond to this problem, using the basic ideas of future research method, the application scenarios of future school education governance driven by artificial intelligence are prospectively analyzed. This paper analyzes the characteristics of core elements, including structure and context of future school driven by artificial intelligence. On this basis, the construction path of future schools is proposed. First, under the innovative idea of learning-drive logic, we should pay attention to the practical problems in different educational scenes. Second, based on the design-based research methods, continuous improvement method is used to promote the integration artificial intelligence in education. Third, under the interdisciplinary and institutional cooperation, sustainable development mechanisms should be explored to help the construction of future schools. References | Related Articles | Metrics

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Governance of Artificial Intelligence Education: Logical Mechanism and Practical Approach Shijin Li, Chenglong Wang, Xiaoqing Gu 2022, 40 (9):  55-66.  doi: 10.16382/j.cnki.1000-5560.2022.09.006 Abstract ( 359 )   HTML ( 80 )   PDF (1135KB) ( 459 )   Save As an endogenous force leading educational innovation, artificial intelligence not only empowers education, but also generates a series of practical challenges that cannot be ignored. Therefore, in the process of deep integration of artificial intelligence and education, scientific governance is particularly critical, and clarifying Its logical mechanism is a solid foundation for effective governance. In order to find a forward-looking and effective governance logic, it is necessary to fully consider innovative governance actions in multiple contexts. The study used international observation and case study methods, compared 12 strategic actions of artificial intelligence governance around the world, expanded the global thinking of artificial intelligence governance, and took the New Zealand artificial intelligence collaborative supervision practice project as an example to clarify the practical process and educational inspiration of artificial intelligence governance. The study found that the logical mechanism of intelligence education governance was expressed as follows. The open and inclusive system context is the prerequisite; the collaborative edification of the advocacy coalition is the backbone, and the scientific and complete supervision mechanism is the motivation guarantee. In view of this, the practical approach for artificial intelligence education governance in China are proposed. Create open and inclusive governance scenarios to promote the systematization of artificial intelligence education governance; shape diverse and synergistic governance mechanisms to enhance the effectiveness of artificial intelligence education governance; apply dynamic forecasting governance methods to ensure the foresight of artificial intelligence education governance. Figures and Tables | References | Related Articles | Metrics

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Artificial Intelligence for Knowledge Understanding: An Empirical Study Aimed at Conceptual Change Hua Du, Xiaoqing Gu 2022, 40 (9):  67-77.  doi: 10.16382/j.cnki.1000-5560.2022.09.007 Abstract ( 317 )   HTML ( 86 )   PDF (1056KB) ( 453 )   Save Understanding is widely regarded as an important value pursuit in education. “Teaching for understanding and learning for understanding” has become a consensus. Knowledge understanding is the basis of concept transformation, the premise of knowledge application and innovation, the key to the development of learners’ higher-order thinking, and the aim of deep learning. At present, when human education is transforming to intelligent education, artificial intelligence can enhance personalized learning, enrich the presentation form of knowledge, support man-machine collaborative learning, and create real learning situations, which brings many possibilities for promoting learners’ knowledge understanding. Based on this background, the authors conduct an empirical study which aims to explore the influence of artificial intelligence learning environment on learners’ conceptual change. The results show that the intelligent learning environment has a positive promoting effect on learners’ conceptual change. Figures and Tables | References | Related Articles | Metrics

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The Game and Integration between AI and Human in Knowledge Production and its Enlightenment to Education Xiangjun Hao, Xue He 2022, 40 (9):  78-89.  doi: 10.16382/j.cnki.1000-5560.2022.09.008 Abstract ( 564 )   HTML ( 99 )   PDF (774KB) ( 889 )   Save Since making stone tools, humans have been committed to using tools to liberate themselves. As an artificial technology tool, artificial intelligence has entered many fields of society and participated in human knowledge production activities. It is gradually sharing the physical and even mental labor of human and showing strong characteristics of autonomy. Therefore, humans and artificial intelligence are bound to engage in a “game” to maintain their own subjectivity. This paper analyzes the current situation of artificial intelligence participating in knowledge production activities from three aspects: the change of the subject of knowledge production, the change of the methods of knowledge production and the change of the form of knowledge presentation, and analyzes the characteristics and differences between artificial intelligence and human intelligence based on this. It is believed that the game between artificial intelligence and human intelligence will move towards the “handshake and reconciliation” between human and machine under the trend of high division of labor, turning “zero-sum” into “win-win”, and reaching a new stage of development through external man-machine cooperation and internal man-machine intelligence integration. Finally, artificial intelligence reconstructs the knowledge production process, which changes the society’s demand for talents, and also causes the thinking on education. The study summarizes three points of inspiration to discuss the focus of future education. References | Related Articles | Metrics

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Could Intelligent Essay Feedback Improve the Effect of Writing Teaching in Middle School? Shujun Liu, Yan Li, Yuewei He, Jingjing Wang 2022, 40 (9):  90-104.  doi: 10.16382/j.cnki.1000-5560.2022.09.009 Abstract ( 324 )   HTML ( 80 )   PDF (936KB) ( 349 )   Save The development of Chinese Intelligent Essay Evaluation System is expected to change the practice and research of traditional writing teaching. However, the way of its integration into daily writing teaching and its teaching effect are highly concerned by Chinese teachers and writing teaching researchers. This study took 28 middle school students who participated in the writing extension course in B school as samples, and carried out a 10-week quasi-experimental study with the method of pre- and post-test in a single group, to verify the effect of integrating Intelligent Essay Feedback into writing teaching in middle school from multiple dimensions. Students took part in three writing activities after receiving argumentative writing guidance. Each time, they needed to conduct intelligent evaluation of the first draft and then revised the essay according to the intelligent feedback. The research focuses on analyzing the characteristics of writing revision and the improvement of writing quality, exploring the development of students’ writing motivation and revision belief, and investigating students’ perception of Intelligent Essay Feedback. The findings are as follows. Firstly, the most commonly used revision behaviors of students were adding and replacing, followed by deleting and rearrange; the ratio of lower order revision was higher than that of higher order revision; students attached great importance to self-directed revision, the success rate of which was lower than that based on intelligent feedback. Secondly, students’ writing performance improved significantly, the length of essays increased significantly, and students made significant progress in the use of stylistic elements such as arguments, explanations and conclusions. Thirdly, students’ writing motivation significantly improved in the dimensions of persistence and passion, and their writing revision belief significantly improved in the lower and higher order dimensions. Fourthly, most students believe that Intelligent Essay Feedback can promote writing practice, and the quality of feedback is the key factor affecting students’ perception. Therefore, Intelligent Essay Feedback can effectively support students’ writing revision process and improve the quality of writing revision. The continuous exploration of multiple integration paths of Intelligent Essay Feedback, teacher feedback, peer feedback and curriculum structure would be beneficial to the promotion of human-computer collaborative writing teaching practice. Figures and Tables | References | Related Articles | Metrics

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The Multi-sensory Experience in Smart Learning and its “Enabling” Enlightenment Guolong Quan 2022, 40 (9):  105-117.  doi: 10.16382/j.cnki.1000-5560.2022.09.010 Abstract ( 341 )   HTML ( 90 )   PDF (831KB) ( 345 )   Save The teaching and learning application of artificial intelligence technology has become a hot spot in educational research and educational practice. The expected effect of intelligent teaching and learning with subject empowerment and educational empowerment as the value destination should be explored from the perspective of the subject in practice. In the learning application of smart technology, the smart learning experience can be examined and analyzed from the perspective of students to improve the functional design of smart technology, to adjust its application methods and to optimize its application process. Comparing the subject’s multi-sensory experience in the context of smart learning, and analyzing the subject’s feelings about the conditions, process and results of smart learning based on multi-dimensional data statistics can provide insight into the enlightenment of the subject in smart learning. The research found that the difference in the smart learning situation makes the subject’s multi-sensory experience different. The significant influence of dynamic and tactile experience has revealed the tendency and demand for the three-dimensional and substantive smart learning experience; and the application of smart technology is the main influencing element of the learning outcome experience, as well as the main source of the multi-sensory experience of smart learning, which can cause chain interactions in curriculum learning and enhancement of the subject’s experience. It is believed that the design of smart learning experience needs to require “thinking on the spot” within a rational framework that clearly defines the characteristics of intelligence to ensure its effectiveness, so as to promote the empowerment of the subject in the interactive communication of smart technology participation. The research has guiding value for the construction of smart teaching environment, the design and implementation of smart teaching and the analysis and recommendation of smart learning. It also has positive significance to effectively promote the development of educational application of smart technology. Figures and Tables | References | Related Articles | Metrics

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Research on the Co-evolution Model of AI Education Ecosystem: The Perspective of Complex System Yiling Hu, Zihong Zhao, Fang Wen 2022, 40 (9):  118-126.  doi: 10.16382/j.cnki.1000-5560.2022.09.011 Abstract ( 397 )   HTML ( 81 )   PDF (1105KB) ( 302 )   Save As the core technology that triggered the fourth technological revolution, artificial intelligence (AI) has brought about subversive changes and reshaping of various industries in the human social system, and the resulting industrial structure and human capital structure have undergone major changes. The education system is the main position for talent training and human capital output. In the face of the integration of AI technology and the transformation of social talent needs, the education system urgently needs to change its original development goals and structural forms to deal with the impact of AI technology on the education system shocks and challenges. Therefore, this research uses complex system science as the theoretical basis to construct a co-evolution model of the education ecosystem, and analyzes the subjects contained in the education system at different levels and the complexity between subjects from the three levels of macro, meso, and micro. Through the deconstruction of the system co-evolution model, the co-evolution dynamic mechanism driven by AI education reform is established, and the overall strategy of “incentive-guarantee-operating mechanism” is proposed to integrate the wisdom and power of multiple subjects in the education ecosystem and jointly promote the co-evolution of the education ecosystem, so as to promote the AI ??education reform of the education system. Figures and Tables | References | Related Articles | Metrics