Transforming Teacher Education: The Influence Of Artificial Intelligence On Educational Practices And Human Resource Dynamics

Information technology, especially artificial intelligence (AI), improves product quality, increases productivity, and develops new products and working methods. AI transforms decision-making options in all industries, including healthcare, finance, manufacturing, retail, and, recently, teacher education.

AI in healthcare helps in accurate diagnosis and care plans with imaging and data processing systems [1]. AI applied in manufacturing technologies fosters the improvement of predictive maintenance and reduces quality variance, thereby reducing facility operating time [2]. Retail industries embrace AI to market products efficiently and recommend others likely to appeal to consumers, improving customer satisfaction and loyalty [3].

AI is useful in teacher education, where it makes teaching and learning more personalized. AI facilitates curriculum development, which considers student performance data that may require special attention, thus reorganizing an environment according to the learning needs and styles of both teachers and students.

Although many studies have been done about AI and its effects on various fields [4], there is still much to learn about its holistic effects, especially for the practices in human resource (HR) management (HRM) and theories and guidelines for teacher training programs. Even if there is a significant amount of literature covering the use of AI, the consequences of its application for managing HR activity and educational approaches have yet to be investigated enough. Such gaps prevent the effective formulation of policies that would help solve new educational challenges and enhance the application of AI in teacher education contexts.

This study aims to examine how AI influences HR functions and educational practices in teacher education institutions. By identifying the challenges and opportunities presented by AI integration, this research should provide insights that support effective AI implementation strategies and enhance outcomes in teacher education.

The approach for this study was mainly exploratory, using secondary data to explore the impact of AI on HR practices and teacher education. A cross-sectional survey approach was combined with applying an econometric model to study these impacts. The literature used was from peer-reviewed journals and government publications to establish plausible gaps in knowledge about the use of AI in teacher education. Derived from Structural Equation Modeling (SEM), the econometric model offered a conceptual map through which postulated associations between the dependent variable, AI adoption, and three antecedents: enacted, espoused, and desired HR practices, educational challenges, and organizational performance in teacher education institutions were tested. The study also sought to establish the interrelationships between these factors, and Maximum Likelihood Estimation (MLE) and Bayesian methods were used to test the hypotheses. The study brings an understanding of how AI influences HR and teachers' education to inform policymakers and Educational leaders on the strategies to utilize AI in the training of teachers.

Industries have shown interest in adopting AI and machine learning (ML) technologies because they significantly transform business operations and decision-making.

AI in healthcare influences disease diagnosis and treatment. In neurosciences, the application of AI and ML is on the rise in interpreting the brain and behavior [1], helping make it easier for researchers to study neurological diseases and design treatments. In molecular medicine, AI has brought significant changes by helping researchers diagnose patients' genetics and develop necessary treatment regimens [5]. Baydoun et al. explain how AI, by computing massive datasets of the human body, improves prostate cancer diagnostic accuracy and helps develop treatment plans for a particular patient [6]. Similarly, AI is increasingly being applied to teacher education, helping in analytics and improving educational experiences.

AI is changing the basic concepts of business intelligence. Bharadiya outlines how AI shapes business processes, noting that it is a tool that redesigns possibilities for changes in practices and customer experience [2]. Bharadiya also discusses the trends of AI and ML technology applications in business intelligence, driving critical business decisions and forming competitive advantages [3].

Applied AI's most groundbreaking trend in scientific research is its use in producing new scientific research. Elbadawi et al. show how AI may help academia by searching for patterns in large data samples, defining areas requiring further empirical inquiry, and developing new research hypotheses [4]. This advancement could enhance the speed of innovations in several areas, including teacher education.

The deployment of AI solutions in HRM activities is rising. Evangelista et al. investigate the moderating role of knowledge-based HRM in organizational performance within small logistics firms [7]. Giudici et al. describe how AI measures risks, particularly considering myriad data to identify risks on time [8].

Huang et al. discuss virtual financial robo-advisors as one field where generative AI can assist in making efficient financial decisions concerning investments [9]. Islam provides how AI applies to diverse industries and is instrumental in creating new models, increasing productivity, and optimizing consumer satisfaction [10]. It emphasizes the revolutionary changes in traditional business models and the practices that AI enables.

Jain et al. review the applications of AI systems in business, focusing on the benefits of AI in decision-making, implementation, and innovation [11]. Stemming from a comparative analysis of business intelligence and AI with big data analytics, Jasmin explains how AI helps organizations optimize their use of complex information technologies to transform big data into meaningful insights for decision-making [12]. Similarly, integrating AI into teacher education can offer valuable insights into improving teaching strategies.

AI is also reshaping the publishing industry and different phases of peer review mechanisms. Kousha and Thelwall have traced that AI enhances efficiency in several of the publisher-peer reviewer's responsibilities and the quality of published papers [13]. Messeri and Crockett share their thoughts on AI use in scientific research and express concern about how AI can be misused in research, potentially overshadowing critical and creative thinking [14], which is also relevant to teacher education.

AI is increasingly involved in business, including blockchain. Kumar et al. review the progression of AI and blockchain, indicating that these technologies effectively promote more dependable information and improved security [15]. Mia and Shuford discuss the complementary relationship of AI and Robotics in Industry 4.0 [16], which can be applied to teacher education through advancements in educational technology.

Neumann et al. employ a comparative case study approach to investigate the implementation of AI in public organizations, which includes teacher education institutions [17]. Nawaz et al. explore the use of AI in general HRM within organizations, emphasizing AI's capacity to facilitate recruitment activities, increase organizational commitment, and optimize HR administration [18], which is relevant to teacher education.

AI-supported learning analytics is changing the education system. Ouyang et al. suggest an AI-based learning analytics approach to study collaborative problem-solving dynamics, arguing that AI is crucial in enriching learner engagement and developing Collaborative and Active Strategies (CAS) in teacher education [19].

Budhwar et al. discuss the risks and opportunities of AI in international HRM, noting that organizations, including those in teacher education, require a framework for maximizing AI's use in managing employees [20].

Risya Putri focuses on individual development plans in HR competencies enhancement, emphasizing the importance of IDPs for developing employees' talents and performances [21]. Perifanis and Kitsios explore AI's potential for generating business value in the digital age, including reinventions of business models with AI [22], which can also impact teacher education.

Talebi et al. propose an organizational excellence model to explain the effect of HR competencies on the marketing of educational services and the corresponding impact on organizational performance and competitiveness [23]. Suniar uses HR competency assessment to enhance bank employee performance by aligning HR practices with organizational goals [24], a concept applicable to teacher education.

Thangavel et al. categorize the study of AI for trusted autonomous satellite operations, thus supporting the idea that AI is promising in decision-making [25]. Bashynska, Sarafanov, and Manikaeva underscore how existing and progressive deep learning models help oversee sentiments in textual data [26], which can be valuable for teacher education by enhancing educational staff motivation.

Cherniavska et al. mention AI for fundraising for universities, showing that AI's applications are not limited to official business enterprises but also extend to teacher education institutions [27].

Saienko et al. [28] highlight specific challenges enterprises face while implementing AI to address these challenges and train employees, including those in teacher education settings.

Muliarevych et al. focus on the digital learning environment, particularly the multiple learning hubs needed to continuously acquire and update knowledge and skills in AI-rich working environments [29]. These hubs can help teacher education institutions ensure their workforce is always up-to-date with the latest knowledge.

Ovsianiuk-Berdadina et al. consider the relationship between CSR and organizational profitability an essential topic for integrating AI, as CSR influences the ethics and performance of organizations and their personnel [30]. Kovshun et al. define state policies on labor resources in trade, showing the regulation affecting AI application to HR practices [31]. Knowledge of such policies aids teacher education institutions in mitigating concerns related to AI inclusion within labor policies.

Chunikhina et al. analyzed communication scenarios in Internet marketing for enterprises that utilize AI, which is crucial for its implementation [32]. Effective communication and marketing strategies are essential for implementing changes brought about by AI within educational institutions.

Tomchuk et al. determine the effectiveness of higher education institutions and study how they can contribute to teacher education by preparing future employees with AI skills for the labor market [33].

According to Chychun et al., focusing on management in distributed working arrangements is becoming increasingly important due to AI's facilitation of remote HRM [34]. As mentioned by Bashynska, Filippov, and Novak, innovative solutions, such as NFC card shielding, show that AI requires novel methods for addressing technology-related issues in its implementation [35]. Such intelligent solutions are valuable to teacher education institutions as they enhance the effectiveness of AI in HR.

Roieva et al. describe digitalization as a critical vector of innovative activity in modern enterprises [36]. Prokopenko et al. study communication business processes in industrial enterprises [37], where AI technologies can improve these processes by offering enhanced communication aids.

Tserklevych et al. study virtual museums as objects for learning activities and the roles of digital and AI technologies in this process [38], demonstrating possibilities for practical work in teacher education.

Prokopenko, Holmberg, and Omelyanenko conducted a comparative study to analyze university ICT support for participation in innovation networks [39]. Their findings also support using ICT and AI to enhance creativity and teamwork, which applies to teacher education institutions.

AI can benefit organizations, including teacher education institutions, by improving decision-making and operational efficiency and fostering innovation. However, challenges such as data privacy, protection, ethical issues, and the need for continuous workforce training on new AI applications remain critical considerations for any organization intending to integrate AI technologies into its operations.

Solutions based on AI technologies have become critical drivers of change in various sectors [40]. AI is essential as an influencer for efficiency, decision-making, and competitiveness [41]. The influence of AI transcends operational management and affects strategic areas such as HRM and teacher education.

Promising HRM policies and procedures are crucial for preparing a skilled employee pool for recruiting, capacity maintenance, and productivity enhancement [42]. However, the development and normalization of AI use in organizations remains a research gap in the impact of AI on HR practices and teacher education. Prior studies have focused primarily on technology and organizational operations resulting from AI adoption and have yet to address the intricate impacts on HR and educational practices.

The SEM method, a statistical technique widely used in social sciences to identify direct and indirect effects between manifest and latent variables, was used to accomplish these goals [43]. SEM of the research model enabled the investigation of the interconnections between various latent variables and provided versatility in depicting the causal links between AI, HR, teacher education, and organizational performance. Variables related to organizations were selected, and hypotheses were formulated and tested on a sample of teacher education institutions (Figure 1).

This study sought to add knowledge by examining the effects of advancements in AI on HR practices and teacher education institutions in particular. The results provided recommendations on how AI can be better implemented within teacher education and address the associated issues. Furthermore, this study assists in maintaining a culture that encourages learning and skill enhancement as teacher education institutions automate their processes through AI.

The data on AI implementation in 150 teacher education institutions worldwide from 2019 to 2023 confirm the emerging nature of AI as a strategic tool and its implications for changes in HRM patterns, learning processes, and organizational effectiveness (Table 1). Introducing AI is gradually transforming conventional structures and processes in teacher education.

The data analysis shows that there has been a consistent trend of increased AI investment by countries to spur change through AI. It is crucial to implement several measures to tackle the obstacles to AI integration in teacher education: First, by providing continuous professional development for current human resources personnel, the organization will be able to secure qualified and dedicated HR personnel equipped with the necessary AI competencies that will enable the organization to adopt transformative change and ensure that the changes that are being made are in sync with the organizations' objectives. Specific regulations on the use of AI systems should be applied to eliminate possible problems in data protection and fairness. Engagement between the developers of AI, the people involved in policy-making in educational institutions, and the human resource managers will enhance the development of AI in a way that will supplement human input. Institutions should also allocate resources for research and pilot studies so that AI solutions can be developed and validated for appropriate implementation in teacher education. Furthermore, prescriptive and informative measures for educators will ensure their understanding of working in AI environments, thus enhancing teaching proficiency and staff satisfaction levels. With the help of these strategies, it is possible to meet the difficulties arising from the application of AI, adapt the experience of HRM in various institutions, and improve educational results.

The study concludes concerning the efficiency of the econometric model for practical application to the institutions involved in teacher education. Regarding HR, the empirical findings indicate the possibility of altering the HR practices in teacher education institutions when applying AI. AI also ensures proper recruitment, controls employee engagement, and makes sound decisions when handling human resource departments. The results obtained in this study are in line with the findings advanced by Budhwar et al. [20] and Nawaz et al. [18], which show that the adoption of AI could revolutionaries the HR practice in teacher education by way of relieving conventional operational HR duties, and so freeing up room for organizational managers to plan strategically.

The study also underlines the necessity of building specific HR competencies for the organizations implementing AI and Teacher Education Institutions in particular. Understanding the technologies associated with AI and using data analytics for AI implementation in teacher education settings will enable HR professionals to implement AI in the field with maximum productivity. The basis of this research is the studies conducted by Risya Putri [21] and Suniar [24], which cover individual development, HR competencies, performance, and competitiveness in organizations, including universities.

The research also has implications for teacher education institutions by highlighting the need for curriculum changes to meet demands in the digital economy. As Ouyang et al. described, intelligent learning analytics can increase the effectiveness of learning processes and better prepare students for new AI-centered workplaces [19]. It aligns with findings from broader literature on AI in learning practices, underscoring the importance of applying AI solutions and services to support innovation and competitiveness [19, 22].

The integration of AI in teacher education institutions has profoundly impacted HR practices, as indicated by the econometric model results. The coefficients for the HR equation (HR = α0 + α1AI + α2EDU + ϵ1) demonstrate a clear positive relationship between AI implementation and HR practice improvement. For instance, in the United States, where AI investment increased from $5 million in 2019 to $12 million in 2023, there was a corresponding increase in HR practices improvement from 15% to 35% during the same period. Similarly, Germany saw a rise in AI investment from $4 million to $8.5 million, increasing HR practices improvement from 18% to 38%.

The econometric model results provide empirical evidence of AI's influence on recruitment, employee management, and other HR practices. The coefficients for recruitment efficiency (RECRUIT), employee performance (EMP_PERF), and training and development (TRAIN_DEV) in the HR equation demonstrate the significant impact of AI on these aspects. For instance, in Japan, where the AI integration level was reported as "Daily High" by 90% of companies in 2023, there was a corresponding improvement in recruitment efficiency, employee performance, and training and development initiatives.

The econometric model results can be used to illustrate specific case examples and statistics. For example, the improvement in HR practices in the United States from 15% to 35% between 2019 and 2023 can be attributed to companies like Amazon and IBM, which have successfully implemented AI in recruitment and employee management. Similarly, the rise in AI investment in Germany from $4 million to $8.5 million led to improved HR practices, particularly in employee performance and training.

The econometric model results highlight the educational challenges introduced by AI implementation. The coefficients for the EDU equation (EDU = β0 + β1AI + ϵ2) indicate a positive relationship between AI implementation and the need for continuous learning and training programs. For example, in China, where AI investment increased from $6 million to $12 million between 2019 and 2023, the need for continuous learning and training programs increased from 20% to 50%.

The results of the econometric model specifications are consistent with research regarding training and upskilling needs. For example, the change in the necessity for learning and training from 20% in China to 50% could be attributed to the existence of Huwaei, Alibaba, and other firms that instituted comprehensive processes for training workers in AI competencies. Likewise, the demand for continuous professional development in teacher education emphasizes the need for competencies in education technologies with AI characteristics.

The results of the econometric models can also be employed to bring attention to some of the institutional reactions toward the educational impacts of AI. For instance, with the rise of the need for continuing education in learning and training programs in China from 20% to 50%, Alibaba firm developed an initiative called "Alibaba Academy," which offers various courses on AI and digital skills. Similarly, when it comes to the United States of America, the same transformation has been identified; Google and Microsoft have even launched internal learning platforms for training in AI and other technologies.

AI has successfully been incorporated into teacher education institutions and has changed operational activities, decision-making, and competition strategies. However, the focus on economic gains and technological innovation resulting from the deployment of AI requires a more refined examination of what the phenomenon portends for HR practices and some of the educational issues in teacher training institutions.

The incorporation of AI in teacher education institutions has changed operations, and there is a need to formulate policies that will allow for the adoption of AI and effective learning strategies. As for the case of active participation, policymakers and industry leaders are instrumental in framing these policies, while the education sector needs to prepare human resources for such AI-driven environments. After that, policy implications, best practices, and recommendations regarding the integration of AI in teacher education are presented to policymakers, leaders in industry, and educational institutions, as presented in Table 2.

The recommendations summarised in this table are crucial for information technology sector leaders, institutions and policymakers regarding AI incorporation into teacher education. They will help to use AI effectively and responsibly to improve performance, competitiveness, and management workforce preparation.

The study of the implications of AI in HR and educational institutions brought new knowledge on AI implementation improvements. This report summarizes the findings and identifies prospective and shortcomings in research areas in transforming teacher education. Key findings include the impact of AI on human resource practices in teacher education, such as recruitment, personnel administration, and training, as well as educational challenges, such as the need for new skills and continual learning for educators.

AI alters teacher education institutions, necessitating policy, corporate practice, and educational system changes. While AI brings benefits such as enhanced efficiency, better decision-making, and competitiveness, it poses problems, including ethical and legal problems and requires continual teacher skill development.

The study's use of secondary data and concentration on teacher education institutes limit the findings' applicability to other industries. AI's impact on HR practices and educational issues may vary by setting.

Despite these limitations, the study contributes to understanding AI's influence on human resources and teacher education, adding to the existing literature. Future research could look into AI's long-term impact on human resource and educational issues in teacher education institutions and its effects in other areas such as employment and income distribution.