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Background: Academic paper writing holds significant importance in the education of medical students, and poses a clear challenge for those whose first language is not English. This study aims to investigate the effectiveness of employing large language models, particularly ChatGPT, in improving the English academic writing skills of these students., Methods: A cohort of 25 third-year medical students from China was recruited. The study consisted of two stages. Firstly, the students were asked to write a mini paper. Secondly, the students were asked to revise the mini paper using ChatGPT within two weeks. The evaluation of the mini papers focused on three key dimensions, including structure, logic, and language. The evaluation method incorporated both manual scoring and AI scoring utilizing the ChatGPT-3.5 and ChatGPT-4 models. Additionally, we employed a questionnaire to gather feedback on students' experience in using ChatGPT., Results: After implementing ChatGPT for writing assistance, there was a notable increase in manual scoring by 4.23 points. Similarly, AI scoring based on the ChatGPT-3.5 model showed an increase of 4.82 points, while the ChatGPT-4 model showed an increase of 3.84 points. These results highlight the potential of large language models in supporting academic writing. Statistical analysis revealed no significant difference between manual scoring and ChatGPT-4 scoring, indicating the potential of ChatGPT-4 to assist teachers in the grading process. Feedback from the questionnaire indicated a generally positive response from students, with 92% acknowledging an improvement in the quality of their writing, 84% noting advancements in their language skills, and 76% recognizing the contribution of ChatGPT in supporting academic research., Conclusion: The study highlighted the efficacy of large language models like ChatGPT in augmenting the English academic writing proficiency of non-native speakers in medical education. Furthermore, it illustrated the potential of these models to make a contribution to the educational evaluation process, particularly in environments where English is not the primary language., (© 2024. The Author(s).)

The innovation capability largely determines the initiative for future development of a region. Higher school is the main position for training innovative talents. Accurate and comprehensive assessment of innovation cultivation capability is an important basis of higher schools for continuous improvement. Thus, this paper focuses on assessing innovative talent cultivation capability. First, by CIPP model (Context, Input, Process and Product Evaluation), an assessment indicator system is built, consisting of 89 indicators in 21 categories. Then, based on indicator characteristics, this paper uses public data statistics, database retrieving, student survey, teacher survey, support personnel and expert investigation, to collect indicator values. After this, by a powerful machine learning algorithm, gradient Boosting regression tree, a capability assessment model is established. And based on collected data, established model is compared with several regression models in innovative talent cultivation capability assessing. Results confirm the performance superiority of our solution.

In recent years, with the economic development of various countries, education has also made great leaps, and piano teaching has been popularized and promoted in various universities. Pianos are also put into use in universities through normal channels. According to the survey, there are certain drawbacks in the teaching process of piano, which hinder the teaching effect to a great extent. In order to better adapt to the interactive teaching mode, this paper takes the flipped classroom in piano teaching as an example to conduct a systematic study based on the method of artificial intelligence. This paper introduces three typical artificial intelligence technologies and analyzes the three evaluative indicators corresponding to the effect of piano interactive teaching. The analysis results show that the prediction effect based on fuzzy neural inference system is the best. In addition, the three-dimensional display of the prediction data shows that the prediction effect obtained by artificial intelligence shows a strong consistency with the actual evaluation effect.

As an emerging technology, industrial intelligence focus on the integration of artificial intelligence and production, which creates a new access to achieve the goal of carbon emissions reduction. Using data on provincial panel data from 2006 to 2019 in China, we empirically analyze the impact and spatial effects of industrial intelligence on industrial carbon intensity from multiple dimensions. Results show an inverse proportionality between industrial intelligence and industrial carbon intensity, and the mechanism is to promote green technology innovation. Our results remain robust after accounting for endogenous issues. Viewed from spatial effect, industrial intelligence can inhibit not only the industrial carbon intensity of the region but also the surrounding areas. More strikingly, the impact of industrial intelligence in the eastern region is more obvious than that in the central and western regions. This paper effectively complements the research on the influencing factors of industrial carbon intensity and provides a reliable empirical basis for industrial intelligence to reduce industrial carbon intensity, as well as a policy reference for the green development of the industrial sector., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

A discussion forum is an indispensable part of a massive open online course (MOOC) environment as it enables knowledge construction through learner-to-learner interaction such as discussion of solutions to assigned problems among learners. In this paper, a machine prediction model is built based on the data from the MOOC forum and the depth of discussion of solutions to assigned problems on the topic among students was analyzed. The data for this study was obtained from Modern educational technology course through Selenium with Python. The course has been offered to a total of 11,184 students from China seven times since February, 2016. The proposed model includes the formula of the depth of problem-solving discussion in MOOC forum and its prediction probability. The efficiency of the prediction model and the most important factor of the depth of problem-solving discussion in MOOC are explained in the paper. Based on the results, useful suggestions for effective teaching in MOOC forums are provided in the article.

Aural decoding skill is an important contributor to successful EFL listening comprehension. This paper first described a preliminary study involving a 12-week undergraduate flipped decoding course, based on the flipped SEF-ARCS decoding model. Although the decoding model (N = 44) was significantly more effective in supporting students' decoding performance than a conventional decoding course (N = 36), two main challenges were reported: teacher's excessive workload, and high requirement for the individual teacher's decoding skills. To address these challenges, we developed a chatbot based on the self-determination theory and social presence theory to serve as a 24/7 conversational agent, and adapted the flipped decoding course to a fully online chatbot-supported learning course to reduce the dependence on the teacher. Although results revealed that the chatbot-supported fully online group (N = 46) and the flipped group (N = 43) performed equally well in decoding test, the chatbot-supported fully online approach was more effective in supporting students' behavioral and emotional engagement than the flipped learning approach. Students' perceptions of the chatbot-supported decoding activities were also explored. This study provides a useful pedagogical model involving the innovative use of chatbot to develop undergraduate EFL aural decoding skills in a fully online environment.

As the global climate problem becomes increasingly serious, the green technology innovation to achieve "carbon peak and carbon neutral" has gradually become the global consensus of major countries, and how the rapid development of artificial intelligence (AI) technology affects green technology innovation (GTI) has received a great deal of attention in the field of economics. Therefore, based on China's inter-provincial panel data from 2006 to 2019, the system GMM, dynamic panel threshold model, and quantile regression model were constructed to examine various influences of AI development on GTI under different environmental regulation intensity, research and development (R&D) investment, and institutional environmental threshold conditions. The findings presented that AI development significantly contributes to GTI and GTFP, with an impact coefficient of 0.0122 and 0.0084, and this influence is mainly reflected in the western region of China and is more obvious in the 2006-2012 period. AI development mainly enhances green technological efficiency, and it has dampening effects on green technological progress during the period 2013-2019. Additionally, there are non-linear threshold effects in the relationship between the level of AI development and GTI when environmental regulatory intensity, R&D investment, and institutional environment are in different level intervals. AI development will boost GTI only when the intensity of environmental regulation and institutional environment is above a certain threshold value. However, the AI development represented by industrial robot applications still has no obvious effect on GTI even when the R&D investment exceeds a certain threshold. Furthermore, the growth effect of AI development on GTI indicates a decreasing nonlinear pattern as the GTI's quantile rises under the condition that R&D investment and institutional environment intensity cross the threshold, while this growth effect increases gradually with the rise of GTI's quantile when the environmental regulation is above the threshold., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This paper explores a possible future of postdigital education in 2050 using the means of social science fiction. The first part of the paper introduces the shift from 20th century primacy of physics to 21st century primacy of biology with an accent to new postdigital--biodigital reconfigurations and challenges in and after the COVID-19 pandemic. The second part of the paper presents a fictional speech at the graduation ceremony of a fictional military academy in a fictional East Asian country in 2050. This fictional world is marked by global warfare and militarization, and addressed graduates are the first generation of artificially evolved graduates in human history. The third part of the paper interprets the fictional narrative, contextualizes it into educational challenges of today, and argues for a dialogical, humanistic conception of new postdigital education in a biotech future.

Accurate prediction of water quality contributes to the intelligent management of water resources. Water quality indices have time series characteristics and nonlinearity, but the existing models only focus on the forward time series when long short-term memory (LSTM) is introduced and do not consider the parallel computation on the model. Owing to this, a new neural network called LSTM-multihead attention (LMA) was constructed to predict water quality, using long short-term memory to process time series data and multihead attention for parallel computing and extracting feature information. Additionally, water quality indices have the issues of multiple data types and complex data correlations, as well as missing data and abnormal data problems in water quality data. In order to solve these problems, this study proposes a water quality prediction model called GRA-LMA-based linear interpolation, gray relational analysis and LMA. Two experiments are carried out to verify the predictive performance of the GRA-LMA with the water quality data of the Huaihe River Basin as a case study sample. The first experiment focuses on data processing, including the processing of missing data and abnormal data of water quality data, and the correlation analysis of water quality indices. Linear interpolation is adapted to process the missing data, while a combination of boxplot and histogram is adopted to analyze and eliminate the abnormal data, which is then repaired the abnormal data with linear interpolation. The gray relational analysis is adopted to calculate the correlation between different water quality indices, and water quality indices with high correlation are retained to determine the input variables of the water quality prediction model. The data processing results demonstrate that repairs can be made using linear interpolation without altering the pattern of data change and the model by using the gray relational analysis to reduce the quantity of data it needs as input. In the second experiment, the predictive capacity of GRA-LMA and existing models such as backpropagation neural network (BP), recurrent neural network (RNN), long short-term memory (LSTM), and gate recurrent unit (GRU) was evaluated and compared using different numerical and graphical performance evaluation metrics. Comparative experimental results show that the mean square error of pH, dissolved oxygen, chemical oxygen demand, ammonia nitrogen, electrical conductivity, turbidity, total phosphorus, and total nitrogen of GRA-LMA is reduced to 0.05890, 0.40196, 0.32454, 0.04368, 14.71003, 8.13252, 0.01558, and 0.14345. The results indicate that GRA-LMA has superior adaptability for predicting various water quality indices and can significantly lower the induced prediction error., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Purpose: This purpose of this paper is to report on the development of an evidence-informed framework created to facilitate the formulation of generative artificial intelligence (GenAI) academic integrity policy responses for English medium instruction (EMI) higher education, responding to both the bespoke challenges for the sector and longstanding calls to define and disseminate quality implementation good practice. Design/methodology/approach: A virtual nominal group technique engaged experts (n = 14) in idea generation, refinement and consensus building across asynchronous and synchronous stages. The resulting qualitative and quantitative data were analysed using thematic analysis and descriptive statistics, respectively. Findings: The GenAI Academic Integrity Policy Development Blueprint for EMI Tertiary Education is not a definitive mandate but represents a roadmap of inquiry for reflective deliberation as institutions chart their own courses in this complex terrain. Research limitations/implications: If repeated with varying expert panellists, findings may vary to a certain extent; thus, further research with a wider range of stakeholders may be necessary for additional validation. Practical implications: While grounded within the theoretical underpinnings of the field, the tool holds practical utility for stakeholders to develop bespoke policies and critically re-examine existing frameworks. Social implications: As texts produced by students using English as an additional language are at risk of being wrongly accused of GenAI-assisted plagiarism, owing to the limited efficacy of text classifiers such as Turnitin, the policy recommendations encapsulated in the blueprint aim to reduce potential bias and unfair treatment of students. Originality/value: The novel blueprint represents a step towards bridging concerning gaps in policy responses worldwide and aims to spark discussion and further much-needed scholarly exploration to this end.

The carbon-reducing effects of artificial intelligence (AI) will be a critical means of achieving carbon peak and carbon neutrality in China. However, in order to efficiently harness the power of AI, the relationship between AI and carbon reduction needs to be fully understood. In this study, we systematically investigated the impacts and mechanisms of action of AI on CO 2 emissions by constructing econometric models using dynamic panel data from 30 provinces in mainland China from 2006 to 2019. The empirical results show that AI significantly reduces CO 2 emissions. Further mediation effect tests found that in the western region, there are mediation effects of the quantity and quality of industrial structure advancedization and industrial structure ecology, while the mediation effect of industrial structure rationalization is not significant. In the eastern and central regions, the mediating effect of the quantity of industrial structure advanced is not significant, while the mediating effect of the quality of industrial structure advanced, industrial structure rationalization, and industrial structure ecology all exist. Our work provides evidence to support that AI reduces CO2 emissions in various regions of China. This can help regions formulate appropriate policies to promote the synergistic development of AI and the "dual-carbon" goal., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Blasting constitutes an essential component of the mining and construction industries. However, the associated nuisances, particularly blast vibration, have emerged as significant concerns that pose threats to operational stability and the safety of the surrounding areas. Given the increasing emphasis on sustainability, ecological responsibility, safety, and geo-environmental practices, the impact of blast vibration has garnered heightened attention and scrutiny. Nevertheless, the field still lacks comprehensive phase analysis studies. Therefore, it is imperative to elucidate the research progress on blast vibration and discern its current frontiers of investigation. To address this need, this study employs bibliometric methods and the CiteSpace 6.1.R2 software to analyze 3093 papers from the Web of Science database. Through this comprehensive analysis, the study aims to chronicle the developmental trajectory, assess the present research status, and identify future trends in the field of blast vibration. The findings of this study reveal that research on "blasting vibration" is advancing rapidly, with the number of citations exhibiting a J-shaped growth curve over time. China emerges as the leading contributor to this research, followed by India, and the foremost institution in this field is Central South University in China. Cluster analysis identifies the effects of ground vibration, numerical simulation, blast load, blasting vibration and rockburst hazard as the most prominent research areas presently. The primary research directions in this domain revolve around the rock fragmentation, compressive strength, particle swarm optimization, and ann. The emergence of these keywords underscores a dynamic shift towards a more holistic and multidisciplinary approach in the field of blasting-induced ground vibration. Furthermore, this study provides a concise overview of blast vibration, discusses prediction techniques, and proposes measures for its control. Additionally, the discussion delves into the social significance of intelligent blasting systems within the context of artificial intelligence, aiming to address the hazards associated with blast-induced ground vibrations., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Purpose: The purpose of this study is to construct an automatic carpal bone age evaluation system for Chinese children based on TW3-C Carpal method by deep learning and to evaluate the accuracies in test set and clinical test set., Methods: A total of 8184 radiographs of Chinese Han healthy children (2-14 years old) were collected from the 2005 China Skeletal Development Survey data and from the accumulated radiographs in bone age studies over the years. Three experienced radiologists and the China-05 standard maker jointly evaluated each bone development stage, and the consensual stage was decided as the reference standard. According to each epiphysis development stage, 10% of them were derived by stratified random sampling as test sets, and the remaining radiographs were used as training sets and validation sets. Furthermore, the overall performance of the model was estimated by comparing the mean difference, 95% limits of agreement, mean absolute difference (MAD) and root mean square (RMS) between the model predictions and the reference standard., Results: The percentage agreement of ratings in each epiphysis in the test set ranged from 82.82% to 90.06%, with an average of 86.94%. Compared with the reference standard, the automated bone age system has a mean difference of 0.01 years old, ± 0.45 years old in 95% confidence interval by single reading, an 85.93% percentage agreement of ratings, a 90.5% bone age accuracy rate, 0.20 years old of MAD, and 0.32 years old of RMS in the clinical test set., Conclusions: The automatic bone age system for Chinese children has a comparable accuracy and stable determination compared with experienced radiologists., (© 2022 American Association of Physicists in Medicine.)

Due to the difficulties of speech signal processing, there is still a considerable gap between the ability of machines to correctly process and that of human beings. In order to overcome the defects of isolated learning and noise sensitivity of SOM, this paper proposes a new time self-organization model (TSOM) from the perspective of deep learning. On the basis of self-organizing mapping network, time enhancement mechanism is introduced to improve the system performance. This method makes up for the fixed spatial topology of the original self-organizing mapping network and the neglect of the time factor, which is crucial to the voice signal. At the same time, this paper makes full use of computer-aided technology and rich network resources to provide a comprehensive and systematic English pronunciation learning database and establish learners' pronunciation files. Once learners understand and master the operation of voice analysis software, they can conduct self-assessment and judgment to find out their blind spots and weaknesses in voice acquisition.

Artificial intelligence (AI) has emerged as an innovative technology with the potential to revolutionize various industries including libraries and information science. Academic libraries are increasingly adopting artificial intelligence (AI) to enhance services, improve efficiency, and enhance user experience. This study utilizes a bibliometric approach to comprehensively analyze current research on AI in academic libraries (AI in ALs). This study employed bibliometric indicators to identify key trends, patterns, and research gaps in the existing literature. A comprehensive dataset of 373 research papers on AI in ALs published between 2002 and 2022 was collected and analyzed using the Scopus database. Various bibliometric tools, such as Biblioshiny, VOSviewer, and BibExcel, have enhanced this analysis. The findings of this study provide important insights. By 2022, there were 64 publications, constituting 17.16% of the total corpus, accompanied by 65 citations. In contrast, 2019 witnessed only 33 publications yet accumulated a substantial number of citations, amounting to 294, representing 8.85% of the overall citations. Conference papers exhibited the highest frequency among different publication types, with 165 publications, whereas journal articles had the highest citation count, accumulating 217 citations. Geographically, China emerged as the leading contributor with 119 publications, and Wuhan University stood out as the most prominent affiliation. Notably, the "Lecture Notes in Computer Science" series emerged as the most prolific source title, publishing 15 articles, of which eight were cited. The authors Wang J., Wang C., and Wang X. from China demonstrated significant contributions, consistently publishing four papers annually from 2010 to 2022. [ABSTRACT FROM AUTHOR]

While ChatGPT has recently become very popular, AI has a long history and philosophy. This paper intends to explore the promises and pitfalls of the Generative Pre-trained Transformer (GPT) AI and potentially future technologies by adopting a speculative methodology. Speculative future narratives with a specific focus on educational contexts are provided in an attempt to identify emerging themes and discuss their implications for education in the 21st century. Affordances of (using) AI in Education (AIEd) and possible adverse effects are identified and discussed which emerge from the narratives. It is argued that now is the best of times to define human vs AI contribution to education because AI can accomplish more and more educational activities that used to be the prerogative of human educators. Therefore, it is imperative to rethink the respective roles of technology and human educators in education with a future-oriented mindset.

A descriptive bibliometric analysis of works on artificial intelligence (AI) in science education is provided in this article to help readers understand the state of the field's research at the time. This study's main objective is to give bibliometric data on publications regarding AI in science education printed in periodicals listed in the Scopus database between 2002 and 2023 end of May. The data gathered from publications scanned and published within the study's parameters was subjected to descriptive bibliometric analysis based on seven categories: number of articles and citations per year, countries with the most publications, most productive author, most significant affiliation, funding institutions, publication source and subject areas. Most of the papers were published between 2016 and 2022. The United States of America, United Kingdom, and China were the top-3 most productive nations, with the United States of America producing the most publications. The number of citations to the publications indexed in Scopus database increased in a progressive way and reached to maximum number in 2022 with 178 citations. Most productive author on this topic was Salles, P. with four publications. Moreover, Carnegie Mellon University, University of Memphis, and University of Southern California have the maximum number of publications as affiliations. The National Science Foundation was the leader funding institution in terms of number of publications produced. In addition, "Proceedings Frontiers in Education Conference Fie" have the highest number of publications by year as a publication source. Distribution of the publications by subject area was analyzed. The subject areas of the publications were computer sciences, social sciences, science education, technology and engineering education respectively. This study presents a vision for future research and provides a global perspective on AI in science education.

Information technology is a significant action to promote education informatization. Currently, there are numerous literature studies on information technology education, but existing research lacks a comparative summary of the research status, research hot spots, and research trend of information technology in China and abroad from an overall perspective. Therefore, this paper selected the information technology education literature collected by China National Knowledge Infrastructure (CNKI) and Web of Science Core Collection (WoS) from 2000 to 2021 as the research object and used bibliometrics to analyze the literature characteristics, core forces, research hot spot, and frontier trend of information technology education in China and abroad. The results showed that: (1) The discipline literature of information technology education showed a fluctuating growth trend, in which the growth rate of foreign literature was faster, and the scale of Chinese literature was slightly smaller; Both Chinese and foreign literature involved pedagogy, computer, and other disciplines, with the characteristics of multi-disciplinary integration. (2) The United States was the leading country in this field, followed by China, with the total literature accounting for more than 50%, ranking the leading position in the world; China Audio-Visual Education and Computers & Education were representative journals in China and abroad. (3) Foreign research focused on information technology, education, science, attitude, model, perception, experience, self-efficacy, Internet, etc. China's hotspots included information literacy, computational thinking, curriculum standards, application contexts, teaching models, subject integration, etc. Foreign research focused on both theory and practice, while China focused on the theoretical nature of the subject, but the degree of practice was insufficient. (4) Student participation, technology integration, teaching innovation, Internet +, core literacy, and artificial intelligence (AI) have been popular until now, constituting the discipline frontier in information technology education.

Recently, artificial intelligence (AI)-programmed automated writing evaluation (AWE) has attracted increasing attention in language research. Using a small data set arising from an analysis of five Chinese university-level English as a foreign language (EFL) students' submissions, this paper examined in detail how EFL students interacted with the feedback of Pigai, the largest AI-programmed AWE in China. The analysis started with the intention of capturing the machine feedback on the five students' submissions and the exchanges between the participants and Pigai over repeated submissions, ranging from 3 to 12 submissions. The analysis showed that the learners' interactions with Pigai focused on error corrective feedback in the initial two submissions. In the case of one student who had 12 submissions, the non-error corrective feedback increased gradually over time, providing rich linguistic resources but without examples and contextual information. The students' take-up rates of feedback with linguistic resources were much lower than that of error corrective and general feedback. A terrain model to map the stages and nature of student responses showed a more complete dynamic process, in which students' responses changed from the initial mechanical responses at the discrete language level to more considered approaches in response to machine feedback. The findings of this study have implications for both language pedagogy and the future design and development of AWE for second or foreign language learning.

In order to understand the real situation of classroom teaching under the environment of information technology and the problems existing in the integration of information technology and curriculum in actual classroom teaching, this paper takes multimedia, interactive electronic whiteboard and Moodle platform as the research and analysis object and uses ITIAS to describe the real situation of classroom teaching. This paper makes a quantitative analysis of the classroom teaching behavior and technology using behavior of 3 teachers and 151 students in the integrated teaching of information technology and curriculum, and makes a comparative analysis with the data obtained from the questionnaire survey of the information education behavior of 213 teachers and a lot of students in 36 middle schools in southern Henan. After investigation and research, three findings have been found: firstly, the initiative of many Chinese teaching is still in the hands of teachers. Secondly, the interaction between teachers and students in classroom teaching has an important impact on teaching and learning. Thirdly, implication of information technology can effectively promote students' autonomous learning and teachers' reflection. Therefore, under the background of artificial intelligence, this paper puts forward the following teaching countermeasures: firstly, it is imperative for teachers to use information technology for effective teaching. Secondly, teachers ought to encourage students to use information technology to improve their cognitive ability and inquiry ability. Thirdly, teachers are encouraged to pay attention to the formative evaluation of the learning process and individual students. Fourthly, middle school Chinese teachers need make effective use of information technology to create an interactive teaching environment. Fifthly, teachers are supposed to focus on indirect influence to make the classroom atmosphere more democratic and harmonious in classroom teaching.

This paper explores the practical prospects for using artificial intelligence technologies in professional English-speaking translator education. At the online conference 'Translation Skills in Times of Artificial Intelligence' (DingTalk platform, January 2022), the teachers of higher education institutions in China prioritized the translator's competencies necessary for successful professional activity during the digital transformation of social and economic business relations. The educators also evaluated the demand for online services used in the education of English-Chinese interpreters. The survey results showed that the use of artificial intelligence technologies in educational practices could have a significant impact on the development of key competencies of future translators. Using a competency-based approach to interpreter training and considering the need to develop abilities, knowledge, and skills required for successful professional translation activity, the author developed the pedagogical concept of the online educational course 'Simultaneous and asynchronous translation in a digital environment.'

Not only has artificial intelligence changed the production methods of traditional industries; it has also presented a great opportunity for future industrial development to decouple from environmental degradation and the promotion of green economic growth. The article studies the influence of artificial intelligence on green economic growth and its mechanism. The research shows that (1) artificial intelligence can promote green economic growth in China. After accounting for spatial factors, it was found that artificial intelligence could promote local green economic growth, but had a siphon effect on neighboring green economic growth. From the perspective of dynamic effects, in the short term, artificial intelligence will not significantly dampen green economic growth in neighboring regions. In the long run, artificial intelligence will have a stronger role in promoting green economic growth, and the siphon effect on neighboring cities will be more significant. (2) As the level of human capital increases, the negative spillover effect of artificial intelligence will be significantly weakened. The promotion effect of artificial intelligence on green economic growth is relatively weak in resource-based cities. (3) Artificial intelligence has obvious attenuation characteristics on the spatial spillover effect of green economic growth, but significant influence is limited to within 200 km. (4) Artificial intelligence has the greatest impact on productivity, accounting for 30.59% in promoting green economic growth. The green innovation effect was 0.0181, accounting for 5.64%. The resource allocation effect is 0.0011, accounting for 3.44%. This paper provides policy enlightenment for promoting industrial intelligence and green economic growth., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

With the deep application of artificial intelligence and big data in education, adaptive learning has become a new research hotspot in online education. Based on the systematic review of the connotation and research progress of adaptive learning, a new definition of adaptive learning is given. By literature analysis, this paper points out the challenges faced by adaptive learning research, such as the lack of cognition of brain and technology, the bottleneck of the model of emotion domain, the separation of education and technology, the security of data management and the risk of privacy leakage. These challenges can be summarized into two aspects: one is mechanical issues, the other is safety issues. Different from traditional research perspectives, the paper opens a new research window, and puts forward countermeasures from the perspectives of cognitive principles, zone of proximal development theory in technology, breakthrough in the emotional domain model, learning data management and privacy security. In view of the centralization of learning data management nowadays, the concept of code chain and the decentralized management mode based on code chain are proposed. Different from the traditional adaptive learning recommendation technology, a new adaptive learning pulling model is proposed.

Although the study of artificial intelligence (AI) used in language teaching and learning is increasingly prevailing, research on language two (L2) learner cognitive psychological factors about AI writing corrective feedback (WCF) is scarce. This paper explores L2 learner cognitive psychology of pigai, an AI evaluating system for English writings in China, from perspectives of perception, noticing, uptake, initiative, retention and emotion. It investigates the consistency between learner cognitive psychology about AI WCF and the expected one and probes into the correlation of learner cognitive psychological factors about AI WCF, aiming at bridging the gap between the research of AI WCF and that of L2 learner cognitive psychology. After a 5-point Likert anonymous questionnaire survey of 1952 undergraduate L2 learners in Anhui University of Finance and Economics (AUFE), the statistical data of Pearson correlation coefficient indicate that learner perception, noticing, uptake, initiative, retention and emotion are positively related in the context of AI WCF, which conforms to the early research of learner cognitive psychology about WCF. But one sample t-test reveals that learner cognitive psychology of AI WCF only occasionally or sometimes consists with the expected one. The subsequent random interviews with 15 respondents suggest that pigai WCF is beneficial to L2 writing, yet there is still much room for it to improve to be deeply integrated with human WCF.

Purpose: This research aimed to examine the current status of artificial intelligence's (AI's) integration into Chinese adult education, by analyzing the influences that AI has had on current adult education practices in China and by discussing the opportunities and challenges that adult education in China is faced with under the rapid AI development in the past 12 years. Design/methodology/approach: This research employed systematic literature analysis. CNKI (China National Knowledge Infrastructure) Chinese Journals Full-text Database was used to collect scholarly publications on the use of AI in adult education in China that was published in the past decade. Data analysis included the following steps: identifying key words and phrases, detecting underlying meanings, searching for logical connections and relationships, collecting and connecting evidence to the research questions, and drawing logical and credible conclusions. Findings: The findings indicated that AI has been gradually integrated into Chinese adult education through innovations and explorations and AI's influence is broad and profound. More specifically, the following five main themes were identified. The field's understanding of AI technology and AI's influence on adult education has evolved and become more comprehensive; AI challenges traditional Chinese adult education practices by helping to actualize personalized learning and precision education; AI transforms adult learning resource development; AI helps to turn learning environment into an open intelligent learning system; and lastly, AI urges the shift of adult educator's role in adult learning. Research limitations/implications: This study is not without limitations. Contextualized in China, this study shares the limitations with other single country studies. One such limitation is "cumulation" issue. This study should be replicated in other country contexts to further validate the generalizability of the five main themes identified in this research. Practical implications: The five themes identified in this study can help understand the promises and challenges that AI brings to the field of adult education in China. These five themes can also serve as an integrated lens through which one can make sense of AI's integration into other countries' adult education practices. Originality/value: This paper fulfills an identified need of understanding the current status of AI's integration into and influence on the field of adult education in China.

Digital reading is now an important way for children to read. This article defines the concept of children's digital reading based on the current situation, reading needs, and research progress of reading promotion methods. Aiming at the problem that only simple matching method is used for entity extraction in BAG model, this paper proposes an entity extraction method based on reference words and an entity graph construction method based on problem-related entities. Experiments on data sets verify the effectiveness of the model. The results show that the BAG model is improved by 2.4% on the verification set and 4.1% on the test set, which proves the effectiveness of the improved method. Based on the graph convolution algorithm of the central node, the local subgraph convolution and the global graph convolution are connected in the entity graph behind the central node, so that the model can obtain local and global information at the same time and enhance the reasoning ability of the model.

The goal is to promote the healthy and stable development of music education in China. The time-frequency sequence topology in frequency domain can improve the effect of convolution operation. Therefore, this paper applies the above algorithms to classical music education, including the recognition of classical instruments, the feature extraction and recognition of classical music, and the quality evaluation of classical music education. The quality of the music quality evaluation system can be judged according to the correlation between the output results and the subjective evaluation. The higher the correlation, the better the music quality evaluation method. Through relevant experiments, it is proved that DTW score alignment and end-to-end are more successful in extracting the features of classical music, and more accurate in identifying classical instruments. The objective evaluation method of pronunciation teaching quality is more objective and accurate than P.563 music teaching quality evaluation.

Adaptive learning systems personalize instruction to students' individual learning needs and abilities. Such systems have shown positive impacts on learning. Many schools in the United States have adopted adaptive learning systems, and the rate of adoption in China is accelerating, reaching almost 2 million unique users for one product alone in the past 3 years. Given such rapid adoption in China, it is useful to examine the efficacy of adaptive learning within that country's educational system. This study aimed to compare the learning impacts of individualized adaptive learning courseware to two common instructional approaches in China: large-group and small-group classroom instruction. This paper describes the results of two efficacy studies of one of China's first adaptive learning systems, Squirrel AI Learning. One study compares classroom-based individualized adaptive learning instruction to large-group instruction, and another to small-group instruction. Chinese eighth-grade students from two provinces randomly assigned to use Squirrel AI Learning showed greater gains on a mathematics test than those randomly assigned to whole-class or small-group instruction led by expert teachers. Findings provide a basis for further research into the selection, use, and impact of adaptive learning systems in Chinese education.

In this paper we begin to explore how knowledges being generated in bioscience might be brought into productive articulation with the Sociology of Education, considering the potential for emerging transdisciplinary, 'biosocial' approaches to enable new ways of researching and understanding pressing educational issues. In this paper, as in our current research, we take learning as our focus. Our work brings together collaborators from across fields: sociology of education; molecular biology and biochemistry; cognitive neuroscience; fMRI imaging; and EEG. Through the paper we explore the generative potential of an encounter between life sciences and sociology of education. Through consideration of the conceptual and methodological elements of our 'Synchrony in Learning' research and engagement with our pilot experimental approach, our research is suggesting that our central concept, learning, is undergoing metamorphosis, challenging us to understand learning as a phenomenon produced through the intra-action of a multiplicity of forces and processes.

This paper examines the political economy of artificial intelligence (AI) and education in China, through an analysis of government policy and private sector enterprise. While media and policy discourse often portray China's AI development in terms of a unified national strategy, and a burgeoning geopolitical contestation for future global dominance, this analysis will suggest a more nuanced internal complexity, involving differing regional networks and international corporate activity. The first section considers two key policy documents published by the central Chinese government, which are shown to implicate educational institutions as influential actors in national and regional strategies for AI development, with a significant role in plans to train domestic expertise. The second section outlines three prominent private education companies: New Oriental Group, Tomorrow Advancing Life (TAL), and Squirrel AI. These companies are selected to represent important aspects of China's development of educational AI applications, including the influence of a well-established private education sector, and a growing interest in international corporate activity. The paper concludes with the suggestion that while central government policy reserves a significant role for education in the national AI strategy, the private sector is utilising favourable political conditions to rapidly develop educational applications and markets.

College English teaching is supposed to cover both language acquisition and culture learning due to the close relationship between language and culture, taking cultural teaching as an indispensible part of college English courses. With the rapid integration of information technology and English curriculum, artificial intelligence has brought new opportunities to college English teaching, and college English cultural teaching methods are now faced with new innovations. In the age of intelligence, to promote teaching quality and learning effect, artificial intelligence technology can be embedded in English teaching practice, exerting its technical advantages and frontier characteristics. In consideration of integrated developing tendency of college English cultural teaching model and modern information technology, the paper is aimed to design and build up an intelligence-aided system so as to extend the depth and width of the application of modern information technology in college English cultural teaching as well as to exploit the great application potential of modern information technology in college English cultural teaching, thus opening a new way and presenting a direction for college English cultural teaching.

Educational data mining (DEM) provides valuable educational information by applying data mining tools and techniques to analyze data at educational institutions. In this paper, tree-based machine learning algorithms are used to predict students' overall academic performance in their bachelor's program. The transcript data of the students in the same department in a Chinese university were collected. All the courses in the bachelor's program were then divided into six typical categories, and the mean GPAs of each category were taken as primary input features for prediction. Three tree-based machine learning models were established, i.e. decision tree (DT), Gradient boosting decision tree (GBDT) and random forest (RF). Results show that we can successfully identify more than 80% of the students at low-performance risk using the RF model at the end of the second semester, which is meaningful because the global quality of teaching and learning of the department can be improved by taking targeted measures in time according to the machine learning model. Feature importance and the structure of decision tree were also analyzed to extract knowledge that is valuable for both students and teachers. The results of this case study can be used as a reference for other engineering departments in China.

This paper explores a paradigm shift in work culture in the contemporary art sector due to digital transition and the introduction of AI. New ways of working with AI and digital software are embedded and normalized in everyday Chinese artistic practices. This work includes new forms of creativity and efficiency, yet, simultaneously includes new types of digital labour. This paper conceptualizes this as "digital art work," which draws attention to the often-overlooked aspects of artists' work, particularly their everyday artistic practices that increasingly include digital software and AI. What is the role and position of the artist in an environment where digital software and AI are becoming more central in artistic creation? How do artists creatively (mis)use AI? What does this paradigm shift in work culture mean for the future of the artist's role and the future of the contemporary art sector? This paper draws on 48 semi-structured interviews with visual artists and arts professionals, including painters, sculptors, mixedmedia, and internet artists as well as contemporary art gallery owners, museum project directors, curators, and culture policymakers living and working in China during 2023. The findings show how Chinese artists are mastering AI and opening up new spaces for creativity and how the contemporary art sector in China has already transitioned to a new "digital way" in artistic creation. These findings can help to create policy around AI globally and provide solutions for the sustainability of the artist profession and the future of the contemporary art sector. [ABSTRACT FROM AUTHOR]

Green supply chain (GSC), one of the most vital sub-topics of sustainable development, indicates people provoking on the rationality of business practices and resource consumption patterns. Under the background of economy globalization, developing countries, especially China, severely affected by green barriers became the global focus. A systematic review of articles about GSC which published in leading journals of Web of Science Core Collection (WoSCC) and China Knowledge Resource Infrastructure (CNKI) is proposed for exploring publishing trends, the distribution of authors and journals, research topics, and hotspots and predicting frontiers by utilizing VOSviewer, Sci2, and CiteSpace. The results show that (1) there are differences in the attention of GSC between international and Chinese academia. (2) "Green" is referred to environmental friendly practices in international academia. Scholars advocate to promote management to strengthen cooperation among GSC members and boost technology investment to improve the comprehensive performance; however, specific practices such as "low-carbon," "emission reduction," "recycling," and "remanufacture" are referred to environmental friendly behaviors in Chinese academia. Scholars expect to avoid enterprises' short-term profit compression relying on government subsidies and make contracts to share environment protection cost equally out for ensuring GSC stable operation. (3) Exploring collaboration among GSC members using complex operation research and artificial intelligence will be international research frontier. Relevant papers are to provide Chinese research with merely innovation in methodology. Besides, the "government-enterprise-university-research institute-customer-economy" management mode proposed by development countries like China will enrich the international GSC research scope, leading international GSC knowledge structure to change. The contribution of this study is to afford reference for future research on GSC.

Purpose: This paper aims to present a comprehensive review of the present state and trends of smart education research. It addresses the need to have a systematic review of smart education to depict its research landscape in view of the growing volume of related publications. Design/methodology/approach: A bibliometric analysis of publications on smart education published in 2011 to 2020 was conducted, covering their patterns and trends in terms of collaboration, key publications, major topics and trends. A total of 1,317 publications with 29,317 cited references were collected from the Web of Science and Scopus for the bibliometric analysis. Findings: Research on smart education has been widely published in various sources. The most frequently cited references are all theoretical or discussion articles. Researchers in the USA, China, South Korea, India and Russia have been most active in research collaborations. However, international collaborations have remained infrequent except for those involving the USA. The research on smart education broadly covered smart technologies as well as teaching and learning. The emerging topics have addressed areas such as the Internet of Things, big data, flipped learning and gamification. Originality/value: This study depicts the intellectual landscape of smart education research, and illustrated the evolution and emerging trends in the field. The results highlight its latest developments and research needs, and suggest future work related to research collaborations on a larger scale and more studies on smart pedagogies.

Deep learning (DL), as the core technology of artificial intelligence (AI), has been extensively researched in the past decades. However, practical DL education needs large marked datasets and computing resources, which is generally not easy for students at school. Therefore, due to training datasets and computing resources restrictions, it is still challenging to popularize DL education in colleges and universities. This paper considers solving this problem by collective intelligence from a resource sharing perspective. In DL, dataset marking and model training both require high workforce and computing power, which may implement through a resource sharing mechanism using collective intelligence. As a test, we have designed a DL education scheme based on collective intelligence under the background of artistic creation to collect teaching materials for DL education. Also, we elaborate on the detailed methods of sharing mechanisms in this article and discuss some related problems to verify this shared learning mechanism.

Objective: To understand the current status of research in the field of metaverse, and to analyze the research progress and evolutionary trends in this field. Methods: Based on the bibliometric analysis, a total of 921 papers were obtained by searching the Web of science core database for the keyword "metaverse". CiteSpace was used to visualize and analyze the current status and trends of metaverse research in China. Results: Ireland is currently the country with the highest research impact. China is currently the country with the largest number of publications in this field, but the impact of the research is insufficient. The current research in the highly cited literature focuses on technical and history reviews of the metaverse as well as its development in the field of education. Artificial Intelligence and utaut2 are the underlying clusters of cited literature in this research area. Several research hotspots have been formed, such as virtual reality, augmented reality, mixed reality, digital twins and artificial intelligence. Conclusion: The current research on metaverse in various fields is basically in its infancy, but has a great potential for future development and will gradually penetrate into many different directions with many challenges. [ABSTRACT FROM AUTHOR]

Based on the panel data of 285 cities in China from 2000 to 2019, this paper searches the number of patent applications related to urban artificial intelligence from five dimensions: algorithm, data, computing power, application scenario and related technology. Combining the two perspectives of industrial upgrading and rationalization, we analyze the internal influence theory of the research topic from the theoretical and empirical perspectives. The results show that artificial intelligence is not only conducive to industrial upgrading, but also significantly inhibit the deviation of industrial structure from equilibrium, which is conducive to industrial rationalization. In addition, the conclusion of this paper is still valid after a series of robustness tests, such as eliminating the samples of central cities, winsorize treatment and instrumental variables method. Through the heterogeneity test, it is found that the promoting effect of artificial intelligence on industrial upgrading is more obvious in big cities and cities with high level of industrial upgrading. The internal mechanism test results show that artificial intelligence promotes industrial upgrading by promoting technological innovation. In cities with a high degree of marketization and Internet development, the role of artificial intelligence in promoting industrial upgrading can be strengthened. The research conclusions of this paper will be conducive to accelerating the development of artificial intelligence to promote industrial upgrading, and provide a useful reference for realizing high-quality development. [ABSTRACT FROM AUTHOR]

Cultivating students' key competencies contributes to students' adaptation to the fast developing intelligent era. Artificial intelligence education is an important way to cultivate students' key competencies. It is increasingly important to set up appropriate artificial intelligence courses at the fundamental education stage and cultivate students' key competencies via such courses. This paper describes quantitatively how AI courses cultivate students' key competencies. First, we analyze the necessity of artificial intelligence education at the fundamental education stage and the status quo of artificial intelligence education in China. Next, we elaborate the students' key competencies which is composed of knowledge competence, team competence and learning competence, and design the curriculum of artificial intelligence at the fundamental education stage. Finally, through collecting students' questionnaires, we analyze the relationship between the curriculum of artificial intelligence and the cultivation of students' key competencies. Experimental evaluations demonstrate that the 7 categories of artificial intelligence courses are beneficial to the cultivation of the six key sub-competencies of students. In addition, the problems in the current artificial intelligence curriculum are identified and suggestions for improvement are put forward.

In order to solve the challenges brought by multi-source and cross-domain scenarios to online music education, this paper designs an online music education system based on advanced artificial intelligence technology, which can provide personalized learning course resource recommendations for music online learners. The system includes four layers, consisting of user interface layer, application module layer, function module layer and data storage layer. At the application module level, this paper proposes a music recommendation algorithm based on a personalized multimodal network model. The recommendation algorithm performs music information retrieval (MIR) based on the similarity judgment of the contour of music pitch and the overall change, and constructs a multimodal network model based on the user's preference for resources to achieve personalized music recommendation. This paper crawls more than one million music score data from a well-known music platform database in China to establish a dataset to evaluate the performance of this method. The comparison results with three existing works show that the method proposed in this paper has good performance and can provide users with suitable music recommendations. The artificial intelligence technology-driven online music education mechanism proposed in this paper has good prospects. [ABSTRACT FROM AUTHOR]

In this paper, 1,538 papers retrieved with the keywords "sports artificial intelligence (AI)" on the Web of Science database since 2007 were taken as the data source, and the Cite Space V software was used to visualize and analyze them. A visual knowledge graph was used to streamline the countries, institutions and authors conducting sports AI research, discipline distribution, research hotspots and development trends in the past 15 years. Subsequently, its development direction and research progress were discussed. Sports AI was widely distributed, with the US, China and the UK leading the way. The most prolific authors and teams in research on sports AI were concentrated in American universities. Their main research direction is to develop and improve smart wearable devices based on machine learning and deep learning technologies for different groups of people. Research on sports AI involved multiple disciplines, which mainly applied and referred to research methodologies and theories on engineering, computer science and sports science. It could be seen from the frequency and centrality of keywords that in the current field of sports AI, machine learning is the main direction, artificial neural networks is the main algorithm, and practical and empirical research based on data mining is the focus. The research hotspots were divided into three major clusters: physical health promotion, sports injury prevention and control, and athletic performance enhancement. How to introduce intelligent technology into sports for a perfect integration still has an arduous and long way to go. Future development requires joint efforts and participation of scientific researchers, professionals and common people. [ABSTRACT FROM AUTHOR]

In this paper, we aim to document and analyze the long arc of connectivity between China and Latin America. First, using analytical international trade and economic tools, we provide a new economic perspective of the First Globalization–-the Ming and early Qing China's economic and business links with Latin America and Europe. Second, due to U.S. government trade policies toward China in the 1990s, the Second China's Globalization was ushered in with its entry to the World Trade Organization (WTO). During this second phase, China exchanged manufactured goods with minerals and resources from Latin America. Finally, the tough technology and trade stance by the Trump and Biden Administrations prompted China to intensify the third phase of China interaction with Latin America. For the 2020s and beyond, one important pillar of the Third China-Latin America-Europe Globalization is heightened efforts for China to increase its digital-AI, telecommunication and platform business presence and access into the Latin American markets. To enrich our analysis, we will provide Chinese-style digital-AI business and economic models to help illustrate our discussions. Throughout our paper, we want to point out that government policies were instrumental in bringing forth these Three Globalizations that involve China-Latin America-Europe. [ABSTRACT FROM AUTHOR]

In the context of rapid advancement in automation and increasing global warming, understanding the impact of artificial intelligence (AI) on carbon emissions (CES) is a cutting-edge research topic. However, there is limited focus in existing research on the nonlinear carbon reduction effect (CRE) of AI. This paper first theoretically elaborates the dual impact mechanisms of AI on CES and illuminates the nonlinear carbon reduction mechanisms of AI. Then, this study employs panel data encompassing 30 Chinese provinces between 1997 and 2019 to empirically test the net effect of AI on CES and the nonlinear carbon reduction effect of AI through econometric models. The results are as follows: first, although AI can both reduce and increase CES, AI primarily helps decrease CES. This conclusion holds true even after considering robustness, endogeneity, and spatial heterogeneity. Secondly, relative to the central and western regions, AI has significant achievement in reducing carbon intensity and per capita CES in the eastern region. However, there is still room for improvement in terms of reducing the total CES in the eastern region. Thirdly, improving the AI development level (AIDL) can magnify the marginal CRE of AI and lead to a nonlinear CRE of AI. Lastly, even if the AIDL remains constant, improving the level of marketization, human capital, digital infrastructure, economic development, openness, and government intervention can also amplify the marginal CRE of AI and lead to a nonlinear CRE of AI. To fully harness the potential of AI for green development, concerted efforts should be directed towards enhancing the innovation and application of AI technologies with carbon reduction potential. [ABSTRACT FROM AUTHOR]