ШТУЧНІ ІНТЕЛЕКТУАЛЬНІ ТЕХНОЛОГІЇ ЯК ФОРМА УПТМ У ПОЧАТКОВІЙ ШКОЛІ: НОВІ МОЖЛИВОСТІ

Artificial intelligence is one of the leading technologies today, which is rapidly spreading into all spheres of human activities including education. In 2023, GPT chat was ranked fourth in the Top 100 Tools for Learning rating, which demonstrates the powerful potential of artificial intelligence tools for the development of innovative processes in education. Accordingly, educators need to understand what artificial intelligence is, its features, and how it can be used to replace the routine activities of both teachers and students in teaching and learning. The authors conducted a survey of primary school teachers about their understanding of this notion, ways to use it as their assistant, advantages and disadvantages of artificial intelligence, and prospects for its development and application in all areas of human activity. The level of respondents' knowledge and skills in artificial intelligence, its features and functions was determined. The professional standard of a primary school teacher and the types of activities, through which the teacher's labor functions are realized and for which it is advisable to use artificial intelligence tools, are analyzed. The development of primary school teachers' competencies can be positively influenced by the use of artificial intelligence in their professional activities. The authors describe digital artificial intelligence tools that will be useful for primary school teachers and classify them according to the types of their pedagogical activities. The areas of activity in which artificial intelligence can act as a teacher's assistant, as well as areas that are currently beyond the capabilities of artificial intelligence, are identified. It is concluded that primary school teachers need to be trained to use artificial intelligence in their professional activities and to transfer knowledge about artificial intelligence to pupils, as well as ways to teach pupils the features of artificial intelligence, methods of its application for teaching pupils and for their own professional development.

TPACK (Technological Pedagogical And Content Knowledge) is a form of knowledge that involves the interaction and integration of subject content, teaching methods, and technology in education. It represents a novel knowledge framework essential for teachers in the information age. The TPACK framework necessitates a deep understanding of each facet of knowledge to effectively integrate technology, teaching methods, and subject content for impactful instruction. This study employs literature research methodology and CiteSpace software to comprehensively identify and visually analyze the research landscape of TPACK among primary school teachers in China. It explores the distribution patterns, research hotspots, knowledge base, transformations, and emerging trends within TPACK research. The findings reveal that literature on TPACK studies has been published in CSSCI journals since 2011, illustrating the increasing attention towards TPACK research. Initially, the research focuses on the entire group of primary school teachers and subsequently narrows down to rural primary school teachers, further segmented by disciplines. Researchers primarily investigate the current status, influencing factors, and strategies related to professional development of primary school teachers. Domestically, collaborative research groups primarily concentrate in key normal universities, often encompassing cooperation between different departments within the same institution, while cross-institutional collaboration remains limited. Furthermore, intercollegiate cooperation is sporadic, with minimal established collaborative relationships.

Purpose While the literature on artificial intelligence (AI) capability is expanding, gaps remain in understanding how this capability is internally developed in technology-based startups (TBS) across different life cycle phases. This study, grounded in the resource orchestration theory (ROT), investigates the pathway through which TBS use organizational creativity to build AI capability and achieve performance. Design/methodology/approach A conceptual framework based on ROT emphasizes the role of organizational creativity in the structuring and bundling processes. Data were collected through a survey of 166 managers and employees of TBS operating in Brazil and international markets, using multiple linear regressions and the Sobel test for analysis. The study validated the AI capability scale in the TBS context. Findings AI capability fully mediates the relationship between organizational creativity and performance, confirming that organizational creativity is a critical resource for AI capability development. These findings advance ROT by deepening the understanding of how AI capability is developed in TBS. The study offers a dynamic, process-based view of performance trajectories in TBS, demonstrating that the synchrony between creativity and AI capability creates a cyclical process, maximizing company performance. Originality/value This research identifies an alternative pathway for TBS to develop AI capability and achieve performance, highlighting the synchronization and co-evolution of resources and capabilities. It provides novel insights into AI capability’s mediating role and expands understanding of resource management in TBS across life cycle phases.

The rapid development of Artificial Intelligence (AI) technology has made education enter a new era, and it also puts forward new requirements and challenges to the professional development of Information Technology(IT) teachers in primary and secondary schools. The study analyzes the connotation and status of the application of Artificial Intelligence technology in the field of education and Artificial Intelligence Education in primary and secondary schools, the impact of artificial intelligence education applications on the professional development of Information Technology teachers in primary and secondary schools, the connection between Artificial Intelligence Education and the development of Information Technology teachers in primary and secondary schools. Then, based on the above analysis, strategies for the professional development of Information Technology teachers in primary and secondary schools are proposed, which mainly include internal strategies for teachers themselves and external strategies for national policies, local policies, local education bureaus, schools, and corporate institutions. Hope that these strategies can promote the professional development of Information Technology teachers in primary and secondary schools.

To guarantee high-quality instruction and enhance student learning outcomes, teachers' professional development is crucial. The objective of this study was to assess how well in-service teacher training programs contributed to the professional development of primary school teachers in Pakistan's Kohat district. A sample of 300 teachers chosen by stratified random sampling was used in a quantitative study design using a survey method. A structured questionnaire consisting of 15 Likert scale items was used to gather data on topics like teacher confidence, leadership abilities, dedication to lifelong learning, teamwork, and training relevance to classroom requirements. According to the results, in-service training programs greatly raised teachers' motivation for ongoing professional development, strengthened their leadership roles, and increased their professional competencies and confidence in teaching difficult concepts. Training programs' statistically significant effects on overall teacher professional development were confirmed by inferential analysis (p < 0.001). The study comes to the conclusion that effective in-service training programs that are tailored to the particular context are essential for empowering educators, encouraging teamwork, and raising the standard of instruction in elementary schools. To maintain the benefits of training, it suggests ongoing assistance, hands-on learning opportunities, and follow-up mentoring. Policymakers, school administrators, and educational planners looking to create more effective teacher development programs can learn a lot from these findings.

The article theoretically substantiates the foundations of the professional development of primary school teachers in the system of methodical work of institutions of general secondary education, in particular, the definition of the concept «professional development of primary school teachers in the system of methodical work of institutions of general secondary education» is specified, which we understand as a managed dynamic process, which is inherently aimed at achieving a high level of professional development and develops according to scientific principles and approaches in specific pedagogical conditions, with the use of developed and adapted learning models in accordance with the requirements of the digital society, the concept of the New Ukrainian School, updating the system of methodical work and crisis conditions. Recommendations are given for the purpose of positive dynamics of professional development of primary school teachers in the methodical work system of general secondary education institutions.

Purpose This study aims to examine how big data analytical intelligence (BDAI) assimilation promotes new product performance (NPP) in business-to-business (B2B) manufacturing firms. This study tests the intermediary role of artificial intelligence (AI) capabilities and the contingency impact of electronic supply chain collaboration (ESCC) in the relationship between BDAI assimilation and NPP. Design/methodology/approach Drawing on the dynamic capabilities theory (DCT), this study tests the moderated-mediation model using multi-wave, multi-source data collected from 291 Chinese B2B manufacturing firms. Structural equation modeling was applied to test the proposed hypotheses. Findings The results demonstrate that BDAI assimilation significantly promotes NPP, and AI capabilities act as an intermediary bridge – empowering B2B firms to convert BDAI assimilation into enhanced NPP. This study further found that this mediation model is strengthened through the contingency impact of ESCC and increases its indirect effect on NPP. Practical implications This study suggests that B2B managers and policy architects should recognize that investment in BDAI assimilation is not sufficient. However, building AI capabilities might fully support BDAI assimilation to gain innovation outcomes such as NPP. This study further suggests the practical implications of ESCC in achieving higher returns through AI capabilities in the B2B context. Originality/value This study has threefold contributions to the existing literature on big data innovation and B2B firms. This study contributes to extend DCT by emphasizing AI capabilities as an intermediary channel and ESCC as a vital contingency – strengthening the relationship between BDAI assimilation and NPP. We contributed and understand how Chinese B2B firms strategically used big data and AI strategies to reach firms’ competitive advantage.

In the aftermath of the COVID-19 pandemic, college students have faced various challenges that could negatively impact their critical thinking abilities due to disruptions to education, increased stress and anxiety, less social interaction, and the advancement of distance learning relying more heavily on digital tools. With the increasing integration of AI technology across sectors, higher education institutions have deployed various AI capabilities for intelligent campuses and modernized teaching. However, how to fully utilize AI capabilities to promote students’ thinking awareness on learning effectiveness is still not clear, as critical thinking is an essential skill set holding significant implications for college students’ development. This research adopts the resource-based theory (RBT) to conceptualize the university as a unified entity of artificial intelligence (AI) resources. It aims to investigate whether AI capabilities can foster critical thinking awareness among students by enhancing general self-efficacy and learning motivation. In particular, it examines the causal relationships between AI capabilities, general self-efficacy, motivation and critical thinking awareness. Primary data was collected through a questionnaire administered to 637 college students. Structural equation modeling was employed to test hypotheses pertaining to causality. The results showed that AI capabilities could indirectly enhance students’ critical thinking awareness by strengthening general self-efficacy and learning motivation, but the effect on critical thinking awareness was not significant. Meanwhile, general self-efficacy significantly affected the formation of learning motivation and critical thinking awareness. This indicates that AI capabilities are able to reshape the cognitive learning process, but its direct influence on thinking awareness needs to be viewed with caution. This study explored the role of AI capabilities in education from the perspective of organizational capabilities. It not only proves how AI facilitates cognition, but also discovered the important mediating role of general self-efficacy and motivation in this process. This finding explains the inherent connections between the mechanism links. Furthermore, the study expands research on AI capabilities research from the technical level to the educational field. It provides a comprehensive and in-depth theoretical explanation theoretically, guiding the practice and application of AI in education. The study is of positive significance for understanding the need for the future development of the cultivation of critical thinking awareness talents needed for future development through AI capabilities in education.

With the rapid integration of artificial intelligence (AI) into educational practice, its influence on teachers’ innovative teaching behavior has attracted growing attention. However, less is known about the dual psychological mechanisms through which AI use may both facilitate and constrain teaching innovation. Drawing on the cognitive appraisal theory of stress, this study examined whether teachers’ challenge and threat appraisals mediate the relationship between AI use and innovative teaching behavior, and whether school innovation support moderates these pathways. A nationwide survey was conducted among 1275 primary and secondary school teachers in China. Data were collected using validated measures of AI use, challenge appraisal, threat appraisal, innovative teaching behavior, and school innovation support, and were analyzed through confirmatory factor analysis, structural equation modeling, and bootstrapped moderated mediation analysis. The results indicated that AI use was positively associated with both challenge appraisal and threat appraisal. In turn, challenge appraisal was positively associated with innovative teaching behavior, whereas threat appraisal was negatively associated with it. Challenge and threat appraisals both served as significant mediating pathways linking AI use to innovative teaching behavior. In addition, school innovation support moderated the effects of AI use on both appraisals: higher levels of support strengthened the positive association between AI use and challenge appraisal, but unexpectedly also amplified its association with threat appraisal. These findings highlight the double-edged nature of AI use in educational settings and suggest that cognitive appraisal is an important mechanism through which AI use relates to teachers’ innovative teaching behavior. The study further implies that schools should provide supportive conditions that encourage challenge appraisal while carefully managing the pressures that may intensify threat appraisal.

The field of education has witnessed a rapid expansion in the utilization of Artificial Intelligence (AI) technologies, fundamentally transforming classroom instruction. Thus, it is critical for pre-service teachers to implement AI-powered technology in their future teaching. This study was conducted in six higher education institutions (HEIs) in China and is grounded in resource-based theory, the technology acceptance model (TAM), and relevant literature. SmartPLS 4.0 was utilized to develop a partial least squares structural equation model (PLS-SEM) to examine the relationships among AI capability (AIC), creativity, self-efficacy, Technological Pedagogical Content Knowledge (TPACK), and pre-service teachers’ behavioural intentions towards AI-assisted teaching. The findings indicated that HEIs’ AIC is a significant predictor of pre-service teachers’ behavioural intentions towards designing AI-assisted teaching. It also predicts their creativity, self-efficacy, and TPACK. Furthermore, creativity, self-efficacy, and TPACK were found to mediate the relationships between HEIs’ AIC and pre-service teachers’ behavioural intentions. These findings suggest that HEIs should support the development of pre-service teachers by enhancing AIC, including resources (data and technology) and awareness (reform and innovation), providing insights into AI integration in higher education within the Asia-Pacific context.

Sustainable development with Artificial Intelligence: Examining the absorptive capacity pathways to green innovation.

Incumbent industrial firms are putting in a lot of effort in developing capabilities for machine learning (ML) systems that help them better predict and perform a variety of industrial and business processes and decisions. Given the data-, process-, and organizational structure-related requirements for effective implementation of such systems, these organizations encounter a major challenge in developing capabilities in this context. However, the existing literature has yet to unravel the organizational processes and practices associated with artificial intelligence (AI) capability development and deployment in industrial incumbent firms. The present study frames AI adoption in established industrial firms as a process of history-embedded, situated organizational learning involving explorative and exploitative learning. Based on a qualitative study of seven firms utilizing ML algorithms in their industrial and business processes, we develop a grounded model that explains AI capability building as both enabled and constrained by perceptual and functional triggers and barriers, leveraged via communicative and structural practices, and resulting in ongoing and interdependent processes of exploration and exploitation. The study contributes to the literature by showing how the convergence of organizational learning and AI technology's unique features promotes a distinct dynamic of AI capability building and deployment.

Emerging economies confront the dual challenge of accelerating digital transformation while simultaneously mitigating environmental degradation under conditions of institutional and governance heterogeneity. In this context, this study examines how artificial intelligence (AI) capability influences green innovation efficiency (GIE) in emerging Asian economies and investigates whether environmental, social, and governance (ESG) performance conditions this relationship. Using an unbalanced panel of 59,112 firm-year observations from 4926 publicly listed firms across 15 emerging Asian economies over the period 2011–2022, we employ a comprehensive panel-data econometric framework that accounts for unobserved heterogeneity, dynamic effects, endogeneity, and potential self-selection bias. The empirical results indicate that AI capability is positively and significantly associated with higher green innovation efficiency. More importantly, ESG performance strengthens this relationship, suggesting that robust governance frameworks enhance firms’ ability to translate digital intelligence into environmentally efficient innovation outcomes. These findings underscore that AI adoption alone is insufficient to generate sustainable value; rather, its environmental effectiveness depends critically on complementary governance structures that promote transparency, accountability, and responsible risk management. The results remain robust after correcting for endogeneity concerns, alternative model specifications, and extensive sensitivity and heterogeneity analyses. Overall, this study contributes to the literature on digital transformation and sustainability by providing large-scale, multi-country evidence that highlights the pivotal role of ESG in shaping the sustainability returns to AI adoption in emerging economies.

PurposeThis paper presents pyBibX, a Python library devised to conduct comprehensive bibliometric and scientometric analyses on raw data files sourced from Scopus, Web of Science and PubMed, seamlessly integrating state-of-the-art artificial intelligence (AI) capabilities into its core functionality.Design/methodology/approachThe library executes a comprehensive exploratory data analysis (EDA), presenting outcomes via visually appealing graphical illustrations. Network capabilities have been deftly integrated, encompassing citation, collaboration and similarity analysis. Furthermore, the library incorporates AI capabilities, including embedding vectors, topic modeling, text summarization and other general natural language processing tasks, employing models such as sentence-BERT, BerTopic, BERT, chatGPT and PEGASUS.FindingsAs a demonstration, we have analyzed 184 documents associated with “multiple-criteria decision analysis” published between 1984 and 2023. The EDA emphasized a growing fascination with decision-making and fuzzy logic methodologies. Next, network analysis further accentuated the significance of central authors and intra-continental collaboration, identifying Canada and China as crucial collaboration hubs. Finally, AI analysis distinguished two primary topics and chatGPT’s preeminence in text summarization. It also proved to be an indispensable instrument for interpreting results, as our library enables researchers to pose inquiries to chatGPT regarding bibliometric outcomes. Even so, data homogeneity remains a daunting challenge due to database inconsistencies.Originality/valuePyBibX is the first application integrating cutting-edge AI capabilities for analyzing scientific publications, enabling researchers to examine and interpret these outcomes more effectively. pyBibX is freely available at https://bit.ly/442wD5z.

Purpose Artificial intelligence (AI) is a significant trend in digital technology that is revolutionizing the field of global business and internationalization. Based on institutional theory and resource-based view, this study examines the intricate relationship between AI capabilities and export performance, taking into account the different province market development within a country and cultural distance. Design/methodology/approach This study gathered data from the websites of privately owned Chinese exporters and complemented it with a survey in 2023. In conducting the survey, we employed a simple random-sampling approach to select 1,500 exporters in China, with a focus on economic development indicators, particularly GDP contribution. We received 1,000 surveys, but only 749 were valid due to missing data. The study’s comprehensive coverage of regions ensured the inclusion of potential variations and subnational disparities within the country. This study conducted ordinary least squares (OLS) regression, and standardized variables before entering into the regressions. Findings The results demonstrate that AI capabilities have a significant positive impact on export performance. In addition, the influence of AI capabilities on export performance varies depending on the home-country and host-country institutional environment. The relationship between AI capabilities and export performance is strengthened by larger cultural distance, while province market development within a country has a negative moderating effect on this relationship. In less developed markets, the AI capabilities can drive significantly export performance. In developed markets with more advanced institutional development, the significance of AI capabilities in reducing transaction costs diminishes due to established institutions and market structures. AI capabilities serve as an intermediary institutional mechanism that connects the institutional context of the home country with the cultural environment of the host country. Originality/value While the impact of AI on international business and internationalization performance is a growing area of study, further exploration of the moderating factors that influence this relationship is needed. Organizations operating in diverse global markets are profoundly shaped by institutional contexts in their operational environments. This research addresses the relatively unexplored role of institutional factors within the home country (provincial market development) and host country (cultural distance) in moderating the effects of AI capabilities on export performance. This study illuminates the intricate dynamics underlying the relationship between AI capabilities and export performance, with a specific focus on province-level market development and cultural distance. Employing institutional theory as the overarching framework, this research sheds light on how AI serves as an intermediary institutional mechanism, bridging the gaps related to cultural differences and varying market development levels. In doing so, it contributes to academia by advancing our understanding of how AI is shaping internationalization dynamics and the interaction between AI capabilities and institutional factors. Additionally, it offers insights for business managers and policymakers to make informed strategic decisions regarding AI capabilities.