From digital disruption to mental health: the impact of AI-induced educational anxiety on teacher well-being in the era of smart education

Abstract

Background

In the context of artificial intelligence (AI) profoundly reshaping the educational ecosystem, teachers, as core drivers of the intelligent education era, are facing unprecedented opportunities and mental health challenges. Although AI demonstrates immense potential for enhancing teaching efficiency and realizing personalized learning, its rapid integration has induced a significant phenomenon of ‘educational anxiety,’ which threatens teachers’ professional well-being and the sustainable development of the education system.

Methods

This study employed a mixed-methods approach, integrating Structural Equation Modeling (SEM) and Artificial Neural Network (ANN) analysis. It examined the direct, indirect, and mediating pathways among the perception of AI technology integration, educational anxiety, digital literacy, and teacher well-being. Subsequently, ANN was used to explore non-linear relationships and rank the predictive importance of the variables.

Results

(1) The perception of AI technology integration had a significant positive direct impact on teacher well-being, indicating that technology empowerment enhances professional satisfaction. (2) A higher perception of AI integration was significantly associated with lower levels of educational anxiety, highlighting that technological adaptation and support are key to mitigating psychological stress. (3) Educational anxiety had a significant negative effect on teacher well-being, confirming it as a major risk factor for teachers’ mental health. (4) Digital literacy played a key moderating role in the relationship between the perception of AI integration and teacher well-being.

Conclusion

Mitigating anxiety is a key leverage point for supporting educators. These findings provide a robust theoretical basis and practical guidance for developing policies aimed at supporting teachers’ mental health and promoting their professional well-being during the digital transformation of education.

Introduction

Human society is in an era of profound transformation driven by Artificial Intelligence (AI) technology. The application landscape of AI is demonstrating unprecedented breadth and depth. On one hand, Generative AI, represented by Large Language Models (LLMs) like ChatGPT and Generative Adversarial Networks (GANs), is reshaping the paradigm of knowledge production and information interaction with unprecedented force [1]. On the other hand, in more embodied and specialized domains—such as intelligent rehabilitation that integrates deep learning with 3D graphics and laser point cloud technology [2]—AI is empowering specific populations through sophisticated human-computer interaction, augmenting rather than replacing human capabilities [3]. This widespread penetration, from industrial manufacturing to healthcare and from financial services to cultural entertainment, has an impact comparable to the first three industrial revolutions. Education, as the cornerstone of social development and the core domain for the transmission of human civilization, is inevitably being drawn into the center of this technological wave. The application of AI in education is viewed with high expectations: the customization of personalized learning paths [4], exponential improvements in teaching efficiency [5], the equitable distribution of educational resources [6], and the precise assessment of students’ cognitive processes [7] collectively paint a grand blueprint for intelligent education. However, as philosophers of technology have repeatedly warned, any disruptive technology acts as a double-edged sword. When we shift our focus from the empowering potential of technology to the primary actors within the educational ecosystem—teachers—a more complex and tension-filled picture unfolds. The rapid iteration and penetration of technology, while creating opportunities for education, have also imposed unprecedented psychological impact and professional pressure on the teaching community, giving rise to a form of ‘educational anxiety’ characteristic of our times [8]. This anxiety is not merely an obstacle in teaching practice; it has evolved into a critical public health issue that directly threatens teachers’ mental health, undermines their professional well-being, and could potentially impact the sustainable development of the entire education system.

Teachers serve as interpreters of educational philosophies, designers of the pedagogical process, and facilitators of student growth. Their professional well-being is not only crucial for their personal physical and mental health and quality of life but is also directly linked to teaching quality, the atmosphere of teacher-student interactions, and even the health and sustainability of the entire education system [9]. A teacher with a high sense of well-being is better equipped to inspire students’ enthusiasm for learning and cultivate their well-rounded character, thereby achieving the ultimate goals of education [10]. However, the “incursion” of artificial intelligence is systematically challenging the traditional roles and established perceptions of teachers. It is particularly noteworthy that as the wave of AI surges into education—a field highly dependent on interpersonal interaction and emotional exchange—the mainstream narrative appears to focus more on “substitutive intelligence,” while its collaborative potential as “augmentative intelligence” to assist teachers and empower instruction has not been fully recognized or explored. This cognitive bias is a significant catalyst for teacher anxiety. First is the existential anxiety stemming from “technological replacement.” AI’s superior performance in areas such as knowledge dissemination, grading assignments, and analyzing student learning data has fueled widespread discussion on whether teachers will be replaced, shaking the professional identity and sense of security for some educators [11]. Second is the competence anxiety arising from the “skills gap.” Teachers are required to rapidly master and effectively utilize various emerging AI educational tools [12], transitioning from a “knowledge authority” to a “technology integrator” and “learning designer.” This role transformation places exceptionally high demands on teachers’ digital literacy, information-vetting skills, and capacity for lifelong learning. The pace of skill acquisition lags far behind the speed of technological iteration, and the resulting feelings of powerlessness and frustration become a major source of anxiety. Furthermore, there is responsibility anxiety originating from “ethical dilemmas.” How to address misinformation generated by AI [13], how to guide students in the critical use of AI tools [14], how to protect student data privacy [15], and how to ensure algorithmic fairness while maintaining the “human touch” in education [16]—these complex ethical choices impose immense moral pressure and decision-making burdens on teachers. These multidimensional anxieties intertwine to form the core syndrome of “AI-induced educational anxiety,” which, like a persistent shadow, continuously erodes teachers’ psychological capital and, consequently, significantly impacts their professional well-being.

Although the academic community has begun to pay attention to the impact of AI on education, existing research tends to show two inclinations. The first is a “technology-centric” research path, with a large body of literature focusing on the development of AI technology [17], application scenarios [18], and evaluation of teaching effects [19], treating teachers as passive recipients or operators of technology, while paying insufficient attention to their emotional experiences and psychological adaptation processes. The second is speculative discussion that “magnifies the problem,” with some scholars exploring the disruptive impact of AI on education from philosophical and sociological perspectives, but mostly remaining at the level of macro discourse and future predictions, lacking in-depth empirical research on specific contexts and the teacher group. A few studies touch on teachers’ attitudes toward or stress from AI, but these are often descriptive and fragmented, failing to systematically reveal how “AI technology pressure” ultimately affects “teacher well-being” through the mediating mechanism of “educational anxiety.” More importantly, existing research generally adopts a “variable-oriented” linear thinking, attempting to find single, universal factors affecting teacher well-being, while ignoring the complexity of reality. In fact, teachers are not a homogeneous group; individuals in different institutional environments may experience varying degrees of anxiety and changes in well-being when facing the impact of AI, possibly following completely different logic. In short, the decline or maintenance of teacher well-being may not be caused by a single “culprit,” but rather by a “configurational recipe” of multiple conditions.

In light of this, the present study aims to break away from traditional research paradigms by adopting a “teacher-centered” perspective to systematically explore the underlying mechanisms by which educational anxiety affects teachers’ well-being in the context of artificial intelligence (AI) technology. The core innovation of this research lies in the adoption of a mixed-methods research design that combines “symmetrical analysis” and “asymmetrical analysis,” with the goal of achieving both theoretical depth and practical precision. Specifically, this paper first constructs a theoretical model and proposes a hypothetical framework in which AI-related technological pressure influences teachers’ well-being through multidimensional educational anxiety. Subsequently, the study employs Structural Equation Modeling (SEM), a “variable-oriented” analytical approach, to test the model, aiming to reveal the causal pathways of the “net effects” among variables and to answer the symmetrical question of “to what extent does anxiety affect well-being.” To overcome the limitations of SEM in handling complex nonlinear relationships and higher-order interaction effects among variables, this study further introduces the Artificial Neural Network (ANN) model as a supplementary analytical tool. Through the training and prediction capabilities of the ANN model, this research is able to identify key variables affecting teachers’ well-being and their nonlinear pathways of influence, thereby uncovering the diverse mechanisms by which educational anxiety impacts teachers’ well-being under different combinations of individual characteristics and organizational environments. This addresses the asymmetrical question of “which factors and their combinations can effectively buffer AI-related anxiety and enhance teachers’ well-being.” This combined analytical strategy of “pathways” and “patterns” not only reveals universal causal chains but also provides insights into multiple realization paths in complex contexts, thus offering a more comprehensive, nuanced, and operational cognitive framework for understanding and intervening in teachers’ well-being in the AI era.

The innovations of this study are manifested in several respects. First, it is the first to treat “educational anxiety” as a mediating variable and “teachers’ digital literacy” as a moderating variable, thereby systematically introducing an analytical framework for the mechanisms influencing teachers’ well-being. This advances our understanding of how AI technologies affect teachers’ mental health and enriches the theoretical corpus on AI applications in education. Second, the study adopts a multi-path and configurational analytic approach that integrates structural equation modeling (SEM) with artificial neural network (ANN) models, which not only enhances the scientific rigor and explanatory power of the findings, but also offers a paradigmatic reference for subsequent empirical research in related fields. Third, the study attends to the interaction between teachers’ individual differences and the organizational environment, incorporating variables such as technological adaptability, professional identity, and perceived organizational support into the analytical framework. In doing so, it reveals heterogeneous psychological responses and mechanisms of well-being formation as teachers confront the impact of AI technologies, thereby providing a theoretical basis for targeted policy interventions and differentiated guidance.

This study aims to answer the following core research questions: (1) In the context of widespread application of AI technology, what are the specific dimensions of teachers’ educational anxiety? What is its theoretical structural model? (2) Through what pathways does AI technology affect teacher well-being? What mediating or bridging role does educational anxiety play in this process? (3) Under different variable configurations, what are the typical pathways affecting teacher well-being? Are there multiple combinations of variables that can achieve “equivalent” improvement in teacher well-being?

In summary, this study hopes that through the above efforts, it can not only enrich the theoretical research on teacher psychology under technological transformation and provide empirical evidence for the construction of future human-machine collaborative education theory, but also, at the practical level, offer concrete and feasible strategic recommendations for education policymakers, school administrators, and teacher training institutions, helping the teaching community effectively cope with technological challenges, alleviate educational anxiety, and ultimately reshape and enhance their professional well-being in the era of coexisting with AI.

Literature review and theoretical foundation

Application of artificial intelligence technology in the field of education

In recent years, the application of artificial intelligence (AI) technology in the field of education has become increasingly widespread, serving as an important driving force for educational transformation and innovation. Through machine learning, natural language processing, computer vision, and other means, AI technology empowers various aspects of education, including educational management [20], instructional content generation [21], personalized learning [22], and intelligent assessment [23]. For example, Intelligent Tutoring Systems (ITS) can dynamically adjust instructional content and difficulty based on students’ learning behaviors and cognitive characteristics, thereby achieving personalized teaching support [24]. In addition, AI-driven adaptive learning platforms can analyze students’ learning data in real time, identify weak areas of knowledge, and provide targeted learning resources and feedback, significantly improving learning efficiency and outcomes [25]. In terms of instructional management, AI technology is widely used in areas such as student behavior analysis, academic early warning, and course recommendation. By mining and analyzing big data, AI can help educational administrators accurately identify students’ learning risks, optimize resource allocation, and enhance the scientific and precise governance of education [26]. Meanwhile, the application of AI in automated grading, intelligent scoring, and online exam monitoring has also greatly reduced teachers’ repetitive workload and improved the objectivity and efficiency of instructional evaluation [27].

With the recent rise of generative artificial intelligence, especially large language models (LLMs) such as OpenAI’s GPT series, a new wave of disruptive applications has emerged. These technologies can serve as powerful auxiliary tools for both teachers and students. For educators, generative AI can accelerate the creation of customized teaching materials, including lesson plans, quiz questions, interactive simulations, and case studies, significantly shortening lesson preparation time [28]. For students, these tools can act as on-demand Socratic dialogue partners and writing assistants. However, such powerful capabilities fundamentally challenge traditional notions of academic integrity, assessment design, and the definition of knowledge creation, placing new and complex demands on educators to guide students in the ethical and critical use of these tools [29].

Nevertheless, the application of AI technology in the field of education also faces numerous challenges. First, the complexity and uncertainty of the technology itself cause some teachers and students to experience anxiety and resistance during the adaptation process [30]. Second, ethical issues such as algorithmic bias and data privacy protection in AI systems are becoming increasingly prominent, posing new challenges to educational equity and student rights [31]. In addition, the widespread application of AI technology places higher demands on teachers’ professional competencies and role positioning, prompting teachers to continuously enhance their technological literacy and innovative capabilities [32].

Educational anxiety induced by artificial intelligence technology

As artificial intelligence technology becomes increasingly integrated into the field of education, its tremendous potential in optimizing teaching and enabling personalized learning has also given rise to a widespread and multi-layered sense of educational anxiety. This anxiety is not unfounded; rather, it has become a significant topic of concern among scholars at the intersection of educational technology, psychology, and sociology. Existing academic literature indicates that this AI-induced educational anxiety primarily stems from uncertainty about the future, doubts about current abilities, and concerns over the alienation of educational processes. It is widely manifested among various educational stakeholders, including students, teachers, and parents. A substantial body of research in economics and the sociology of technology provides a macro-level context for this anxiety. In their influential study, Frey and Osborne predicted that nearly half of all occupations face a high risk of being replaced by automation technologies in the future—a forecast that has profoundly shaped public perceptions of the future labor market [33]. This macro-level “automation anxiety” has quickly permeated the education sector, giving rise to a “skills panic” among students and parents. Scholars have pointed out that students, when planning their career paths, are increasingly worried that the knowledge and skills they acquire will quickly become obsolete upon graduation, thus falling into the predicament of “preparing for an uncertain future [34].” This anxiety has led to a more utilitarian approach to educational choices, with students tending to pursue majors perceived as “irreplaceable by AI,” while neglecting personal interests and holistic development—an educational alienation in itself.

For educators, anxiety is mainly manifested as technological pressure and the redefinition of professional roles. The introduction of AI tools requires teachers not only to master new instructional technologies but also to reshape their pedagogy and classroom roles. One study found that teachers face dual pressures: on the one hand, “competence anxiety” stemming from insufficient digital literacy and concerns about being unable to effectively leverage AI tools for teaching empowerment; on the other hand, “role anxiety” regarding the potential erosion of their professional value [35]. When AI can undertake tasks traditionally performed by teachers—such as knowledge transmission, assignment grading, and even learning analytics—what then is the core value of teachers? Kozhabayeva and Boivin warn that educators may feel their professional judgment and educational functions are being replaced by standardized algorithms, leading to concerns about “deprofessionalization,” which undoubtedly affects their teaching enthusiasm and professional well-being [36].

In summary, existing academic literature clearly reveals that educational anxiety induced by AI technology is a complex and multidimensional phenomenon. It is not only students’ confusion about future careers, but also teachers’ struggle with professional identity, and, more broadly, a profound reflection on equity and humanistic values within the entire education system in the face of technological disruption. Therefore, this anxiety is not merely an individual psychological issue, but rather a structural product of the complex interactions among technology, society, and the education system, urgently requiring systematic responses from policymakers, researchers, and practitioners.

Stimulus-organism-response (S-O-R) theory

To systematically elucidate the complex interaction mechanisms among artificial intelligence technology (Stimulus, S), teachers’ internal cognitive and emotional states (Organism, O), and their ultimate well-being (Response, R), this study adopts the Stimulus-Organism-Response (S-O-R) theory as its core analytical framework. The S-O-R theory was first systematically proposed by environmental psychologists Mehrabian and Russell in their research on the impact of environments on individual behavior [37]. It modifies the traditional behaviorist “Stimulus-Response” (S-R) model by emphasizing that external stimuli do not directly elicit individual responses, but must act through the intermediary process of the individual’s internal state (i.e., the “organism”). The core insight of this theory lies in revealing the “black box” of human behavioral decision-making, highlighting the crucial role of cognition and emotion in linking the external environment to final behavior [38].

In recent years, due to its strong explanatory power, the S-O-R theory has been widely applied in various studies to explain how technological environments influence users’ psychology and behavior. For example, in the field of information systems, scholars have used the S-O-R model to explore how website environments (S) influence users’ purchase intentions (R) through perceived pleasure (O) [39]. In social media research, the model has been used to explain how information overload (S) leads to usage interruption behavior (R) through users’ technological fatigue (O) [40]. These studies fully demonstrate that the S-O-R theory provides a mature and effective analytical paradigm for understanding the chain of “technological environment → psychological mediation → behavioral outcome.”

Accordingly, drawing on Stimulus–Organism–Response (S–O–R) theory, this study develops a moderated mediation model: the AI technological environment functions as an external stimulus (S) that, by eliciting or intensifying teachers’ educational anxiety (O)—an internal psychological state—thereby influences their professional well-being (R). Meanwhile, teachers’ digital literacy, as an individual-difference variable, moderates the strength of the “AI technological stimulus → educational anxiety” pathway; that is, among teachers with high digital literacy, the anxiety-inducing effect of AI technologies is significantly attenuated. This extended framework not only overcomes the limitations of a mechanistic application of S–O–R theory but also more comprehensively illuminates the complex mechanisms shaping teachers’ well-being in the AI era.

Research design and methodology

Research model and hypothesis development

Based on an extended Stimulus-Organism-Response (S-O-R) theory, this study constructs a moderated mediation model. The model conceptualizes the application and cognitive level of AI technology, teachers’ perception of AI, and organizational support for AI as external stimuli (S); educational anxiety as the organism’s internal state (O); and teacher well-being as the behavioral response (R). Furthermore, teachers’ digital literacy is introduced as a moderating variable to examine its effect on the mediation pathway. Specifically, the AI technology environment is hypothesized to not only directly influence teacher well-being but also indirectly affect it through the mediating role of educational anxiety. The strength of this mediation pathway is, in turn, moderated by the level of teachers’ digital literacy. The research model for this study is illustrated in Fig. 1.

Fig. 1.

Hypothetical framework diagram

The impact of the extent of AI technology application on teacher well-being

AI technologies, such as intelligent grading systems, personalized learning path recommendations, and automated administrative management tools, have the potential to liberate teachers from heavy and repetitive administrative tasks. When teachers are able to effectively utilize these technologies, they can significantly reduce the time and energy devoted to lesson preparation, grading, and data analysis. This not only directly lowers the “job demands” associated with workload, but also allows teachers to devote more energy to creative and emotionally valuable teaching interactions and curriculum design, thereby enhancing their sense of professional achievement and autonomy. The effective application of AI technology serves as an important “job resource,” helping teachers achieve work goals and reduce energy depletion. Accordingly, this study proposes the following hypotheses:

• H1: The extent of AI technology application positively influences teacher well-being.

The impact of teachers’ cognition of AI technology on teacher well-being

Teachers’ cognition of AI technology is not merely an understanding of the tool itself, but rather a psychological construct that encompasses a comprehensive judgment of its value, threat, and feasibility. When teachers hold a positive perception of AI, viewing it as an effective assistant in teaching and an opportunity for professional development rather than a threat to their profession, they are more likely to proactively learn about and explore AI applications, thereby gaining a sense of efficacy and satisfaction. This positive mindset itself constitutes a valuable psychological resource. Conversely, if teachers perceive AI as a threat and are filled with doubts and resistance, they will experience additional psychological pressure and anxiety, forming an internal “cognitive demand” that in turn undermines their well-being. Based on this, the following hypothesis is proposed:

• H2: Teachers’ cognition of AI technology positively influences teacher well-being.

The impact of organizational support for AI technology on teacher well-being

The introduction of AI technology represents a significant professional transformation for teachers, which cannot be achieved without systematic organizational support. Organizational support specifically includes providing professional training, equipping necessary software and hardware facilities, establishing clear usage norms and incentive mechanisms, and fostering a culture that encourages innovation and tolerates mistakes. Sufficient organizational support can significantly reduce teachers’ uncertainty and frustration in the process of learning and using new technologies, enabling them to feel respected and cared for by schools or educational authorities. Organizational support is one of the core “job resources,” effectively buffering the new “job demands” brought about by technological change, empowering teachers, and thereby directly enhancing their job satisfaction and well-being. Therefore, the following hypothesis is proposed:

• H3: Organizational support for AI technology positively influences teacher well-being.

The impact of educational anxiety on teacher well-being

Educational anxiety refers to the persistent and pervasive feelings of worry and tension that teachers experience in educational activities due to pressures from students, parents, school management, public opinion, and educational policies. This anxiety constitutes a high-intensity “job demand” that continuously depletes teachers’ psychological and emotional resources, leading to energy exhaustion, professional burnout, and a direct decline in their quality of life and subjective well-being. Regardless of the conveniences brought by AI technology, if teachers remain in a state of high educational anxiety for a prolonged period, their overall well-being will be difficult to fundamentally improve. Accordingly, the following hypothesis is proposed:

• H4: Educational anxiety negatively influences teacher well-being.

The mediating role of educational anxiety

Integrating the logical chain of the S-O-R model, the core argument of this study is that the external AI technology environment (S) does not directly diminish teacher well-being (R), but primarily does so by triggering internal educational anxiety (O). Educational anxiety serves as a key bridge in this impact pathway. Therefore, the following mediation hypotheses are proposed:

• H5: Educational anxiety mediates the relationship between the AI technology environment (including application extent, teacher cognition, and organizational support) and teacher well-being.

• H5a: Educational anxiety mediates the relationship between the extent of AI application and teacher well-being.

• H5b: Educational anxiety mediates the relationship between teachers’ cognition of AI and teacher well-being.

• H5c: Educational anxiety mediates the relationship between organizational support for AI and teacher well-being.

The moderating role of teachers’ digital literacy

According to the extended S-O-R theory, an organism’s internal resources can modulate its responses to external stimuli. Teachers’ digital literacy, as a core professional competence in the digital era, encompasses not only the technical skills required to operate AI tools but also higher-order capabilities such as leveraging AI for pedagogical innovation, critically evaluating AI-generated information, and adhering to digital ethics. It constitutes a key psychological and skill resource. In line with the Job Demands–Resources (JD-R) model, the resources individuals possess can buffer the negative impact of job demands (e.g., stress induced by technological change). Accordingly, we have reason to believe that teachers with higher levels of digital literacy exhibit stronger self-efficacy and a greater sense of control when facing AI-driven disruption. They are more inclined to regard AI as an opportunity rather than a threat, and they can more effectively manage the uncertainties associated with technological application, thereby reducing the likelihood that these uncertainties translate into internal anxiety. By contrast, teachers with lower digital literacy are more prone to feelings of helplessness and loss of control in the face of technological change, and technostress more readily converts into pronounced educational anxiety. On this basis, we propose the following moderating-effect hypotheses:

• H6a: Teachers’ digital literacy positively moderates the relationship between the degree of AI application and educational anxiety.

• H6b: Teachers’ digital literacy positively moderates the relationship between teachers’ perceptions of AI and educational anxiety.

Research methods and procedures

This study employs an Explanatory Sequential Mixed Methods Design, integrating Structural Equation Modeling (SEM) and Artificial Neural Network (ANN) analysis in a sequential manner to achieve a comprehensive understanding of the mechanisms through which agricultural packaging design influences consumer repurchase intention. This design adheres to the principle of complementarity in mixed methods research proposed by Venkatesh et al. [41]: SEM is employed to validate theoretical hypotheses and identify significant pathways (a theory-driven approach), while ANN is used to capture non-linear relationships and enhance predictive accuracy (a data-driven approach). The two methods are organically and methodologically linked through a logical chain of “explanation-elaboration-validation.”

The primary objective of the first phase is to test the theoretical model proposed in this study and to identify the key linear causal pathways influencing teacher well-being. We selected Structural Equation Modeling (SEM) as the primary analytical tool, as it can simultaneously handle both observed and latent variables and effectively test direct, indirect, and total effects among them, making it a mature technique for validating complex behavioral models [42]. The analysis was conducted using IBM SPSS AMOS 26.0. First, Confirmatory Factor Analysis (CFA) was performed to test the measurement model, ensuring that the reliability and validity of the survey scales met acceptable standards. After the measurement model was validated, we proceeded to construct the full structural model to conduct path analysis and mediation effect testing. This was done to verify the study’s theoretical hypotheses regarding the relationships among the AI technology environment (including extent of application, teacher perception, and organizational support), teacher anxiety, and teacher well-being. The output of this phase will provide theoretically validated input variables for the non-linear analysis in the second phase.

SEM analysis is primarily based on the assumption of linear relationships between variables. However, psychological processes are often characterized by complex non-linear features. To compensate for the limitations of SEM and deepen our understanding, this study introduces an Artificial Neural Network (ANN) as a complementary analytical tool. Specifically, this study will construct a Multi-Layer Perceptron (MLP) network, which includes one hidden layer and is trained using the Scaled Conjugate Gradient algorithm. ANN models do not require pre-specifying the functional relationships between variables; they can automatically learn and approximate highly complex non-linear patterns from the data [43]. In this way, we aim to provide an analytical perspective that is complementary to SEM, more accurately quantify the relative importance of each antecedent variable on teacher well-being, and construct a high-precision predictive model. To ensure the reproducibility of the research, the complete model parameters (e.g., activation function, data partitioning scheme) will be presented along with the model’s performance in the results section. The specific architecture and parameter settings of the ANN model used in this study are illustrated in Fig. 2.

Fig. 2.

Parameter Configuration of the ANN Model

As shown in Fig. 3, this study first employs structural equation modeling (SEM) to conduct path analyses of the relationships among the application of AI technologies, educational anxiety, teachers’ well-being, and related variables. SEM can simultaneously model multiple causal relationships and is well suited to testing mediation and moderation effects among complex constructs. Guided by the theoretical hypotheses, we construct a structural equation model that includes variables such as AI technology application, teachers’ cognition regarding AI, organizational support, educational anxiety, teachers’ digital literacy, and teachers’ well-being. Data are collected via questionnaire surveys, and the model is fitted and path coefficients are estimated using AMOS and related statistical software to assess the proposed hypotheses.

Fig. 3.

Research Flowchart

Subsequently, to further uncover nonlinear relationships among variables and the configurational effects of key antecedents, we introduce an artificial neural network (ANN) model. ANNs can approximate complex nonlinear mappings, thereby overcoming the limitations of traditional linear models. Using the significant predictors identified in the SEM analysis as inputs and teachers’ well-being as the output, we construct a multilayer perceptron (MLP) neural network to rank the importance of factors influencing teachers’ well-being and to conduct sensitivity analyses, thereby enhancing the model’s predictive performance and explanatory depth.

Questionnaire design and variable measurement

This was an online survey conducted from May 1, 2025 to July 20, 2025. Cluster sampling was used to survey teacher groups across different educational stages in China. All data were collected through an online questionnaire using the Questionnaire Star platform (www.wjx.cn). The survey content included demographic information, application of artificial intelligence technology, educational anxiety, and teacher well-being. The study was approved by the Ethics Committee of Shazhou Polytechnic (2025-07). All participants provided informed consent before completing the online questionnaire. The complete questionnaire is provided in Appendix 1.

This study employed a structured questionnaire survey to collect data. The instrument comprised two sections: (1) respondents’ demographic characteristics (gender, age, income, school level, years of teaching experience, region, etc.); and (2) measurement items for the core constructs, all rated on a five-point Likert scale (1 = strongly disagree, 5 = strongly agree), as shown in Table 1. To ensure reliability and validity, all items were reviewed by five experts in education and psychology, and ambiguously worded items were refined based on feedback from a small-scale pilot test (n = 50). The final questionnaire exhibited a coherent structure and scientific rigor, effectively capturing the relationships among AI technology application, educational anxiety, teachers’ digital literacy, and teachers’ well-being, thereby providing a robust data foundation for subsequent empirical analyses.

Table 1.

Questionnaire design

First-level dimension	Second-level explanatory variable	Questionnaire question
The extent of AI technology application (AIA)	Depth of AI Integration in Teaching	Frequency of using AI tools in teaching design, classroom interaction, and assignment evaluation[44].
	Transformation of Teaching Processes by AI	The degree to which traditional teaching organization, teacher-student interaction models, and evaluation systems have changed[45].
	Coverage of AI in Teaching Scenarios	The extent of AI technology application across different subjects, grade levels, and teaching stages[46].
	Density of AI Data Monitoring	The frequency of using AI monitoring technologies like classroom behavior recognition and learning process tracking[47].
Teachers’ cognition of AI technology(TCA)	AI Technology Awareness	The teacher’s level of understanding of AI technology[48].
	Perceived Controllability of AI	The teacher’s perception of the controllability of AI technology in teaching[49].
	Awareness of AI Ethical Risks	The teacher’s understanding of potential problems that AI technology may bring[50].
	Frequency of AI Application	The frequency with which the teacher uses AI in teaching[51].
Organizational support for AI technology (OSA)	AI Training Support	Whether the school provides teachers with AI-related training and support[52].
	AI Technology Sharing	The extent of communication among colleagues regarding AI technology[53].
	AI Technology Investment	The school’s resource investment in AI technology[54].
	Organizational Dependence on AI	The degree to which the school’s teaching management relies on AI systems[55].
Educational anxiety (EA)	Job Replacement Anxiety	Worry that the development of AI technology will lead to a reduction in teaching positions[56].
	Technological Lag Anxiety	Concern that one’s own teaching skills and knowledge cannot adapt to AI development[57].
	Pedagogical Method Anxiety	Worry that traditional teaching methods are no longer effective[58].
	Evaluation Pressure Anxiety	The feeling that teaching effectiveness is excessively monitored by data systems[59].
	Anxiety over Student Changes	Concern about student dependence on AI and changes in their autonomous learning abilities[60].
Digital Literacy (DL)	Digital Technology Operation Competence	Ability to quickly adapt to and operate new digital tools or AI platforms[61].
	Digital Resource Integration Competence	Know how to select and design digital tools to support specific teaching objectives and learning activities[62].
	Digital Security and Ethics Literacy	Understand and comply with regulations related to digital copyright and intellectual property[63].
Teacher well-being (TW)	Job Satisfaction	The overall evaluation of one’s teaching work.
	Psychological Well-being	Positive emotions, life satisfaction, etc.
	Sense of Teaching Accomplishment	The perceived effectiveness and value of one’s teaching.

Data collection and sample characteristics

Regarding the sampling strategy, given the wide geographical distribution of the teacher population in China and the lack of a unified, publicly available sampling frame, employing strict probability sampling methods, such as cluster sampling, is practically infeasible. Therefore, this study adopted a non-probability sampling method that combines convenience and snowball sampling. Specifically, we leveraged the researchers’ educational collaboration networks to distribute the questionnaire to multiple teacher WeChat groups, QQ groups, and professional education forums covering the eastern, central, and western regions of China. Concurrently, we established collaborations with four schools of different types (two primary schools, one junior high school, and one senior high school) for on-site administration of the questionnaire to ensure sample diversity across educational stages. To expand coverage, initial participants were also encouraged to share the questionnaire link with their professional networks. Although this method does not permit statistical generalization to the entire population, its objective was to obtain a sample that is as diverse and extensive as possible for an in-depth exploration of the relationships among the variables.

Data for this study were collected via a questionnaire survey. The questionnaire was distributed through a combination of online and offline channels. Online distribution primarily relied on social media platforms and communication groups relevant to the education sector, while offline distribution involved organized sessions for on-site completion at select schools. The data collection period was from May to July 2025. A total of 400 questionnaires were distributed, and 334 valid responses were collected, yielding an effective response rate of 83.5%. To ensure the representativeness of the sample and the scientific rigor of the data, all returned questionnaires were rigorously screened, and any invalid or logically inconsistent responses were excluded. Regarding the sample characteristics, among the respondent teachers, 62.4% were female and 37.6% were male; the age distribution was predominantly 31–40 years old (45.2%), followed by 41–50 years old (28.7%); in terms of teaching experience, 34.5% had 5–10 years and 38.2% had more than 10 years of experience; and the majority held a bachelor’s degree or higher (81.7%). The surveyed teachers encompassed various regions, educational stages, and academic disciplines, demonstrating strong representativeness. Overall, the sample structure is reasonable and adequately reflects the current status of educational anxiety and well-being among teachers in China within the context of AI technology application, providing a solid data foundation for the subsequent analysis.

SEM data analysis and results

Reliability and validity testing of the scale

This study employed Cronbach’s α coefficient to test the reliability of the scale. The overall Cronbach’s α value of the questionnaire indicates good data reliability. Additionally, the Cronbach’s α coefficients for each dimension were analyzed, as shown in Table 2.

Table 2.

Reliability analysis of the data

Construct	Item	Cronbach’s α
AIA	Depth of AI Integration in Teaching	0.831
	Transformation of Teaching Processes by AI
	Coverage of AI in Teaching Scenarios
	Density of AI Data Monitoring
TCA	AI Technology Awareness	0.858
	Perceived Controllability of AI
	Awareness of AI Ethical Risks
	Frequency of AI Application
OSA	AI Training Support	0.872
	AI Technology Sharing
	AI Technology Investment
	Organizational Dependence on AI
EA	Job Replacement Anxiety	0.867
	Technological Lag Anxiety
	Pedagogical Method Anxiety
	Evaluation Pressure Anxiety
	Anxiety over Student Changes
DL	Digital Technology Operation Competence	0.903
	Digital Resource Integration Competence
	Digital Security and Ethics Literacy
TW	Job Satisfaction	0.890
	Psychological Well-being
	Sense of Teaching Accomplishment

AIA The extent of AI technology application, TCA Teachers’ cognition of AI technology, OSA Organizational support for AI technology, EA Educational anxiety, DL Digital literacy, TW Teacher well-being

According to the data presented in Table 3, the KMO measure of sampling adequacy in this study is 0.896, which is well above 0.8, indicating that the sample data are suitable for factor analysis. The approximate chi-square value of the Bartlett’s test of sphericity is 4680.727, with 253 degrees of freedom and a significance level of 0.000 (p < 0.001), suggesting that the correlation matrix is not an identity matrix and that there are strong correlations among the variables. This makes the data appropriate for subsequent factor analysis. Therefore, the data possess good structural validity and can provide a reliable foundation for further structural equation modeling analysis.

Table 3.

KMO and bartlett’s test

KMO		0.896
Bartlett’s sphericity	spherical test	4680.727
	df-value	253
	p-value	0.000

Confirmatory factor analysis

According to the measurement results of the fit indices in Table 4, the overall fit of the structural equation model in this study is relatively ideal. First, the chi-square to degrees of freedom ratio (CMIN/DF) is 2.278, which falls within the acceptable range of 1 to 3, indicating good model fit. Both the GFI (0.889) and AGFI (0.858) are above 0.8, demonstrating good goodness-of-fit. The RMSEA is 0.062, which is below 0.08, further indicating a satisfactory model fit. The incremental fit index (IFI) is 0.940, the normed fit index (NFI) is 0.898, the Tucker-Lewis index (TLI) is 0.929, and the comparative fit index (CFI) is 0.940; all are close to or exceed 0.9, meeting high fit standards. In summary, all fit indices fall within acceptable ranges, indicating that the structural equation model constructed in this study fits the actual data well, and the model results have strong explanatory power and reliability.

Table 4.

Validated factor analysis model fit

Fitting index	Acceptable range	Measured value
CMIN		489.775
DF		215
CMIN/DF	1–3	2.278
GFI	≥ 0.8	0.889
AGFI	≥ 0.8	0.858
RMSEA	< 0.08	0.062
IFI	≥ 0.9	0.940
NFI	≥ 0.8	0.898
TLI(NNFI)	≥ 0.9	0.929
CFI	≥ 0.9	0.940

Path coefficients and hypothesis testing results

To test the theoretical hypotheses proposed in this study, we estimated and examined the significance of the path coefficients in the structural equation model. Using Amos 24.0 software, key indicators such as model path coefficients, standard errors (S.E.), critical ratios (C.R.), and significance levels (P values) were obtained, as shown in the Table 5; Fig. 3. In this study, an absolute C.R. value greater than 1.96 (corresponding to p < 0.05) was used as the criterion for statistical significance of the path coefficients.

Table 5.

Model measurements

Hypothesis				Estimate	S.E.	C.R.	P	Testing the hypothesis
H1	TW	<---	AIA	0.366	0.096	3.794	***	Established
H2	TW	<---	TCA	0.336	0.073	4.599	***	Established
H3	TW	<---	OSA	0.230	0.054	4.260	***	Established
H4	TW	<---	EA	−0.251	0.063	−3.982	***	Established
H5a	EA	<---	AIA	−0.335	0.105	−3.193	0.001	Established
H5b	EA	<---	TCA	−0.252	0.079	−3.182	0.001	Established
H5c	EA	<---	OSA	−0.174	0.059	−2.948	0.003	Established

AIA The extent of AI technology application, TCA Teachers’ cognition of AI technology, OSA Organizational support for AI technology, EA Educational anxiety, TW Teacher well-being

*** indicates p < 0.001

Regarding the direct effects of AI-related factors on teacher well-being, the application of AI technology has a significant positive impact on teacher well-being (β = 0.366, p < 0.001), indicating that the higher the degree of AI technology application in teaching, the higher the teachers’ professional well-being. Therefore, Hypothesis H1 is supported. Teachers’ cognition of AI technology also has a significant positive impact on teacher well-being (β = 0.336, p < 0.001), suggesting that the deeper the teachers’ understanding of AI technology, the more positive their well-being experience. Thus, Hypothesis H2 is supported. Organizational support for AI has a significant positive impact on teacher well-being (β = 0.230, p < 0.001), meaning that the more sufficient the support provided by the school, the higher the teachers’ well-being. Therefore, Hypothesis H3 is supported.

In terms of the direct effect of educational anxiety on teacher well-being, educational anxiety shows a significant negative impact on teacher well-being (β = −0.251, p < 0.001). This indicates that the higher the level of educational anxiety among teachers, the lower their professional well-being. Therefore, Hypothesis H4 is supported. Regarding the effects of AI-related factors on educational anxiety, the application of AI technology has a significant negative impact on educational anxiety (β = −0.335, p = 0.001), indicating that the actual application of AI technology helps reduce teachers’ educational anxiety. Thus, Hypothesis H5a is supported. Teachers’ cognition of AI technology also has a significant negative impact on educational anxiety (β = −0.252, p = 0.001), meaning that the more teachers understand AI, the lower their anxiety. Therefore, Hypothesis H5b is supported. Organizational support for AI also has a significant negative impact on educational anxiety (β = −0.174, p = 0.003), indicating that support from the school can effectively alleviate teachers’ educational anxiety. Thus, Hypothesis H5c is supported.

In summary, all the hypotheses proposed in this study are supported by the data. The results clearly reveal that AI technology application, technological cognition, and organizational support jointly influence teachers’ professional well-being through both direct paths and indirect paths mediated by educational anxiety.

Analysis of the mediating effect of educational anxiety

According to the results in Table 6, educational anxiety plays a significant mediating role in the pathways through which AI-related variables affect teacher well-being. Specifically, the indirect effect of the extent of AI technology application on teacher well-being through educational anxiety is 0.084, with a confidence interval of [0.020, 0.183] and a p-value of 0.005, indicating that this mediating effect is significant. At the same time, the direct effect of the extent of AI technology application on teacher well-being is 0.366, and the total effect is 0.450, both reaching significant levels. This suggests that the extent of AI technology application not only directly enhances teacher well-being, but also indirectly promotes it by reducing educational anxiety. Similarly, the indirect effect of teachers’ cognition of AI technology on teacher well-being through educational anxiety is 0.063, with a confidence interval of [0.006, 0.147] and a p-value of 0.024; the direct effect is 0.336, and the total effect is 0.399, all of which are significant. This indicates that the higher the level of teachers’ cognition of AI technology, the lower their educational anxiety, and consequently, the higher their well-being, with educational anxiety playing a partial mediating role in this process. In addition, the indirect effect of organizational support for AI technology on teacher well-being through educational anxiety is 0.044, with a confidence interval of [0.006, 0.103] and a p-value of 0.020; the direct effect is 0.230, and the total effect is 0.274, all of which are significant. This demonstrates that organizational support not only directly enhances teacher well-being, but can also have a positive indirect effect by alleviating educational anxiety.

Table 6.

Analysis of intermediation effects

Mediation Path	Effect	Estimate	Lower	Upper	P
AIA—EA—TW	Indirect Effect	0.084	0.020	0.183	0.005
	Direct Effect	0.366	0.118	0.649	0.003
	Total Effect	0.450	0.208	0.726	0.001
TCA—EA—TW	Indirect Effect	0.063	0.006	0.147	0.024
	Direct Effect	0.336	0.156	0.517	0.001
	Total Effect	0.399	0.205	0.584	0.001
OSA—EA—TW	Indirect Effect	0.044	0.006	0.103	0.020
	Direct Effect	0.230	0.085	0.370	0.002
	Total Effect	0.274	0.128	0.418	0.001

AIA The extent of AI technology application, TCA Teachers’ cognition of AI technology, OSA Organizational support for AI technology, EA Educational anxiety, TW Teacher well-being

In summary, educational anxiety plays a significant mediating role in the impact of AI technology application, cognition, and organizational support on teacher well-being, further confirming the key regulatory role of educational anxiety in the pathway to enhancing teacher well-being.

Analysis of the moderating effect of teachers’ digital literacy

The results of the moderation tests for digital literacy are reported in Table 7; Figs. 4 and 5. Simple slope analyses reveal that when teachers possess high digital literacy, the positive effect of the extent of AI application on teachers’ well-being is significant (β = 0.103, p < 0.05); when digital literacy is low, this positive effect is not significant. Likewise, when teachers exhibit high digital literacy, teachers’ cognition of AI has a significant positive effect on teachers’ well-being (β = 0.289, p < 0.05); under low digital literacy, this positive effect is not significant. Therefore, teachers’ digital literacy positively moderates the relationship between the extent of AI application and teachers’ well-being, as well as the relationship between teachers’ cognition of AI and teachers’ well-being. This may be because teachers with higher digital literacy can more proficiently employ AI tools to optimize instructional processes and reduce workload, thereby amplifying the well-being benefits associated with AI application and cognition; by contrast, teachers with lower digital literacy may encounter operational barriers and adaptation difficulties when faced with AI technologies, making it hard for AI application and cognition to translate into tangible well-being gains and potentially exacerbating their educational anxiety.

Table 7.

Analysis of the moderating effect of teachers’ digital literacy

Effect Type	Effect Size	Standard Deviation	T	Confidence Interval
				LLCI	ULCI
DL×AIA→TW	0.103	0.042	2.433	0.020	0.187
DL×TCA→TW	0.108	0.046	2.320	0.016	0.199

 AIA The extent of AI technology application, TCA Teachers’ cognition of AI technology, OSA Organizational support for AI technology, EA Educational anxiety, DL Digital literacy, TW Teacher well-being

Fig. 4.

The moderating effect of teachers’ digital literacy on the relationship between the extent of AI application and well-being

Fig. 5.

The moderating effect of teachers’ digital literacy on the relationship between the perception of AI and well-being

Construction of the ANN neural network model

To further optimize the model and enhance its predictive accuracy, this section combines the results from the Structural Equation Modeling (SEM) analysis with the Artificial Neural Network (ANN) approach to construct an SEM-ANN-based model of teacher well-being. Drawing upon the research of Liébana et al. [64], and based on the test results of the SEM and the principles of ANN, three artificial neural network models—Model A, Model B, and Model C—were constructed, as illustrated in Fig. 6. The input layer for Model A includes the extent of AI technology application, teachers’ perception of AI (AIA), organizational support for AI (TCA), and organizational support for AI (OSA); the output is educational anxiety (EA). The input layer for Model B comprises teachers’ perception of AI (AIA) and organizational support for AI (TCA), while the output layer is teachers’ digital literacy (DL). The input layer for Model C integrates teachers’ perception of AI (AIA), organizational support for AI (TCA), organizational support for AI (OSA), educational anxiety (EA), and teachers’ digital literacy (DL), with teacher well-being (TW) as the output layer.

Fig. 6.

Artificial neural network model construction

Root mean square error validation

As shown in Table 8, this study employed ten-fold cross-validation. The dataset was partitioned into ten equal folds; in each iteration, 90% were randomly selected as the training set and the remaining 10% served as the test set. This process was repeated ten times to ensure that every fold was used as a test set to evaluate model performance. Three sets of artificial neural network (ANN) models were constructed to predict educational anxiety and teachers’ well-being using different combinations of input variables. Model A used AIA, TCA, and OSA as inputs with EA as the output. Model B used AIA and TCA as inputs with DL as the output. Model C used AIA, TCA, OSA, EA, and DL as inputs with TW as the output.

Table 8.

Root mean square error test for artificial neural network models

	Model A		Model B		Model B
	Input: AIA、TCA、OSA		Input: AIA、TCA		Input: AIA、TCA、OSA、EA、DL
	Ouput: EA		Ouput: DL		Ouput: TW
Neural network	Training	Testing	Training	Testing	Training	Testing
ANN1	0.370	0.232	0.326	0.260	0.232	0.195
ANN2	0.362	0.220	0.309	0.295	0.216	0.159
ANN3	0.362	0.176	0.308	0.232	0.215	0.159
ANN4	0.369	0.219	0.311	0.190	0.245	0.214
ANN5	0.356	0.402	0.339	0.254	0.215	0.246
ANN6	0.369	0.308	0.306	0.189	0.237	0.140
ANN7	0.350	0.413	0.293	0.326	0.237	0.293
ANN8	0.377	0.421	0.299	0.369	0.253	0.306
ANN9	0.358	0.297	0.315	0.376	0.230	0.221
ANN10	0.374	0.398	0.281	0.441	0.238	0.211
Mean	0.365	0.309	0.309	0.293	0.232	0.214
SD	0.092	0.307	0.127	0.290	0.114	0.235

AIA The extent of AI technology application, TCA Teachers’ cognition of AI technology, OSA Organizational support for AI technology, EA Educational anxiety, DL Digital literacy, TW Teacher well-being

We computed the root mean square error (RMSE) for each ANN model. As shown in Table 7, the RMSE values on the test sets ranged from 0.140 to 0.441, and those on the training sets ranged from 0.215 to 0.377. The overall prediction errors were small, indicating that the ANN models produced relatively accurate predictions on the test data and provided reliable support for subsequent analyses and decision-making.

Sensitivity analysis

According to the results in Table 9, this study constructed three sets of artificial neural network models—Model A, Model B, and Model C—and analyzed the average relative importance and normalized importance of each input variable. In Model A, AIA had the highest average relative importance (0.375, normalized to 100%), followed by TCA (0.331, 88.267%) and OSA (0.295, 78.667%), indicating that AIA has the most significant impact on educational anxiety (EA). In Model B, the average relative importance of TCA was far higher than that of AIA (0.845 vs. 0.155, normalized to 100% vs. 18.343%), showing that teachers’ perception of AI is the core driving factor for digital literacy (DL). In Model C, AIA had the highest importance (0.254, 100%), with TCA and OSA ranking second (both at 0.243, 95.669%), followed by EA in third place (0.189, 74.409%), while DL had the least impact (0.072, 28.347%). This highlights the crucial mediating role of EA in the pathway influencing teacher well-being (TW).

Table 9.

Analysis of the importance of normalisation in artificial neural network models

	ModelA (Output: EA)			Model B (Output: DL)		Model C (Output: TW)
Neural network	AIA	TCA	OSA	AIA	TCA	AIA	TCA	OSA	EA	DL
ANN1	0.413	0.260	0.327	0.028	0.972	0.254	0.243	0.243	0.189	0.072
ANN2	0.303	0.379	0.318	0.126	0.874	0.269	0.195	0.210	0.205	0.122
ANN3	0.364	0.352	0.284	0.138	0.862	0.299	0.160	0.191	0.197	0.152
ANN4	0.338	0.286	0.375	0.166	0.834	0.332	0.214	0.143	0.257	0.054
ANN5	0.352	0.449	0.199	0.248	0.752	0.275	0.232	0.192	0.174	0.127
ANN6	0.344	0.208	0.447	0.133	0.867	0.199	0.269	0.174	0.244	0.114
ANN7	0.339	0.347	0.315	0.232	0.768	0.281	0.180	0.236	0.216	0.087
ANN8	0.367	0.370	0.263	0.223	0.777	0.305	0.219	0.142	0.263	0.070
ANN9	0.334	0.370	0.296	0.130	0.870	0.261	0.273	0.255	0.136	0.075
ANN10	0.594	0.285	0.121	0.125	0.875	0.094	0.268	0.232	0.075	0.096
Average relative imporance	0.375	0.331	0.295	0.155	0.845	0.254	0.243	0.243	0.189	0.072
Normanlized relative importance (%)	100.000	88.267	78.667	18.343	100.000	100.000	95.669	95.669	74.409	28.347

AIA The extent of AI technology application, TCA Teachers’ cognition of AI technology, OSA Organizational support for AI technology, EA Educational anxiety, DL Digital literacy, TW Teacher well-being

Overall, AIA and TCA were primary influencing factors across all models, while the role of OSA was relatively stable but secondary, and the influence of DL was weak. These findings reveal the dominant role of the extent of AI technology application and teachers’ perception on educational anxiety, digital literacy, and teacher well-being. They also validate the important bridging effect of educational anxiety and digital literacy as mediating variables, providing precise data support and a theoretical basis for optimizing strategies for AI application in education and for enhancing teachers’ digital literacy and well-being.

Discussion and recommendations

Discussion

This study’s central finding represents a significant breakthrough compared to monolithic technostress models. Our results resonate with the Job Demands-Resources (JD-R) theory, which posits that occupational well-being depends on the balance between job demands and job resources [65]. The findings position artificial intelligence technology as a unique factor that simultaneously embodies the dual nature of being both a potential demand and a resource. The indirect pathway, mediated by educational anxiety, frames AI as a job demand, where the perceived threat of substitution and the pressure to adapt exacerbate psychological strain. Conversely, the direct positive pathway positions AI as a critical job resource, where its effective integration can automate administrative tasks, provide data-driven pedagogical insights, and enhance professional competence, thereby directly boosting job satisfaction and well-being. This duality explains the contradictory reports in existing literature and suggests that the impact of AI is not determined by the technology itself, but rather by the context of its implementation and teachers’ cognitive appraisal of it as either a threat or an opportunity—a view supported by recent research on technology adoption and professional identity [66].

Furthermore, the Artificial Neural Network analysis powerfully reveals that the core of this dual mechanism lies in the quality of teachers’ practical, situated interactions with AI, a finding that strongly aligns with Bandura’s theory of self-efficacy [67]. The primacy of application experience as a predictor indicates that “mastery experiences”—successful, practical engagement with AI tools in authentic teaching contexts—are the most potent source of efficacy beliefs. High self-efficacy, in turn, can mitigate threat appraisal and buffer against anxiety. This shifts the focus of intervention from abstract training to practice-oriented professional development. More critically, our findings reveal an evolution in the nature of technological demands: a shift from technical skills to complex ethical and pedagogical reasoning. As teachers become “gatekeepers of algorithmic decisions and guardians of student data,” they face new stressors related to algorithmic fairness, data privacy, and the pedagogical implications of AI-generated content [68]. This emerging “ethical technostress” constitutes a key frontier for future research, requiring a shift in support systems from mere technical training to fostering teachers’ critical AI literacy and ethical decision-making capacities.

Recommendations

Intervention strategies for alleviating educational anxiety

Traditional intervention logic focuses on remedying teachers’ skill deficits. However, as this line of research reveals, the source of teacher anxiety has deepened from mere technological inadaptability to include ethical and responsibility-related anxieties when confronting issues of AI algorithmic fairness and data privacy protection [69]. When teachers are required to use algorithms that may contain social biases for student evaluation, or when they become the de facto “guardians” of vast amounts of student data, their anxiety touches upon the core of their professional ethics and autonomy. Therefore, intervention strategies must shift from a focus on technological adaptation to one of ethical empowerment. First, cognitive training on artificial intelligence technology for teachers should be strengthened to enhance their digital literacy and technological adaptability. It is recommended that educational authorities organize regular specialized training sessions and practical workshops on AI technology. These initiatives would help teachers understand the real-world application scenarios of AI in teaching, thereby reducing the anxiety that stems from technological unfamiliarity. Second, a mental health support system for teachers should be established. To address issues such as professional insecurity and role ambiguity that arise during the widespread adoption of AI technology, this system should offer multifaceted services including psychological counseling, emotional regulation, and career planning. Furthermore, teachers should be encouraged to participate in the decision-making and feedback mechanisms of AI-related educational reforms. This would enhance their sense of professional engagement and belonging, thereby reducing the anxiety caused by passive adaptation. Innovatively, this study proposes the introduction of “Teacher-AI Collaborative Growth” projects. By enabling teachers and AI to jointly participate in processes such as curriculum design and pedagogical assessment, these projects would foster constructive interactions between educators and technology, gradually alleviating educational anxiety.

Policy recommendations for enhancing teacher well-being

To enhance teacher well-being in the age of artificial intelligence, the core of policy must shift from providing traditional welfare benefits to professionally empowering teachers within the new ethical landscape. Recent research has repeatedly emphasized that when teachers are compelled to use AI algorithms they perceive as unfair or work under ambiguous data privacy policies, their professional well–being is severely eroded by “moral exhaustion [70].” This exhaustion stems from the conflict between their professional values and the technological practices they are required to implement. Therefore, truly effective strategies for enhancing well-being must directly address and mitigate these ethical stressors. First, the support system for teachers’ professional development should be improved by establishing competency certification and promotion mechanisms tailored to the AI era, thereby incentivizing teachers to engage in proactive learning and innovation. Second, the teaching work environment should be optimized by rationally distributing the new pedagogical tasks that arise from the application of AI technology, preventing a decline in well-being due to an increased workload. It is recommended that an “AI-Empowered Teacher Innovation Fund” be established to encourage educators to explore innovative teaching models that integrate AI with their specific disciplines, thereby enhancing their sense of professional achievement and self-actualization. Furthermore, the social recognition of teachers should be strengthened through media campaigns and the promotion of exemplary cases to increase public appreciation for the role of teachers in the AI-driven educational transformation. An innovative proposal is the creation of a “Teacher Well-being Monitoring and Feedback Platform” to regularly collect data on teacher well-being, dynamically adjust relevant policies, and ensure the continuous improvement of teachers’ well-being within the AI technology environment.

Management and support measures for AI application in education

To ensure the scientific and ethical application of AI technology in the educational domain, the construction of management and support systems must transcend conventional technical regulations and pivot towards establishing a governance framework centered on “algorithmic accountability” and “data justice.” A large body of recent research warns that AI applications lacking transparency and oversight can directly undermine the well-being of both teachers and students by perpetuating biases and violating privacy [16]. Consequently, the foremost priority for management measures must be to ensure that ethical considerations are embedded into the deployment of technology from its inception, adhering to the principles of “responsible innovation.” First, ethical and regulatory standards for AI applications in education must be established to clearly delineate the rights and responsibilities of teachers, students, and AI systems, thereby preventing technology misuse and mitigating data security risks. Second, school-enterprise partnerships should be promoted, bringing together AI companies, research institutions, and schools to jointly establish “AI in Education Innovation Labs.” These labs would provide teachers with authentic technological application scenarios and ongoing technical support. It is recommended to establish an “Evaluation and Feedback Mechanism for AI in Education Applications” to periodically assess the impact of AI tools on pedagogical effectiveness, teacher psychology, and student development, allowing for the timely adjustment and optimization of implementation strategies. An innovative proposal is to introduce a “Teacher AI Literacy Certification System,” integrating AI literacy into the professional development evaluation framework for teachers to enhance their capacity for autonomous innovation and adaptation within the AI-integrated environment. Finally, the digital sharing of educational resources across regions should be strengthened to narrow the educational digital divide between urban and rural areas and among different regions, thereby promoting educational equity and the universal accessibility of high-quality resources.

Conclusion

The core contribution of this study lies in integrating structural equation modeling (SEM) with artificial neural networks (ANN) to demonstrate not only a mediating pathway whereby perceived AI integration alleviates educational anxiety and thereby enhances occupational well-being, but also a direct benefit pathway from technology integration to well-being. The practical significance of this dual-path finding is substantial: it challenges the reductive view of AI as a singular stressor and reveals its dual nature as both an enabling tool and a potential threat. The ANN analyses further deepen this insight by showing that teachers’ hands-on application experience is the pivotal hub regulating their psychological state. As the strongest predictor of anxiety—and, together with anxiety, one of the two principal fulcrums for predicting well-being—its effect size far exceeds that of other factors. This implies that any theoretical support or cognitive training divorced from real application scenarios will be markedly less effective. It points to the most cost-effective entry point for educational intervention: focus on improving the quality of teachers’ interactions with AI in instructional practice.

More importantly, this study engages in dialogue with recent research on technostress and the reshaping of teachers’ professional identity. Whereas earlier work has emphasized the added workload and skills gaps accompanying technology adoption, our findings indicate that the essence of educational anxiety has moved beyond the level of technical operation to a deep uncertainty about future educational roles and professional values. When teachers’ perceived AI integration is high, they tend to regard technology as a partner that enhances pedagogical autonomy and professional competence, thereby directly improving well-being—an observation consonant with the “technology empowerment” perspective. Conversely, low perceived integration heightens the sense of “technological substitution,” making educational anxiety the main driver eroding occupational well-being and confirming its central mediating role under the emerging paradigm of human–AI collaboration. Notably, in light of frontier research, the theoretical framework should be further extended to address algorithmic fairness and data privacy—two emerging and increasingly critical sources of anxiety. Teachers are no longer merely passive users of technology; they are becoming gatekeepers of algorithmic decisions and guardians of student data.

This study not only reveals the dual impact of AI application on teachers’ mental health and occupational well-being but also, through the ANN model, identifies the importance ranking of the influencing factors, thereby providing data support for targeted interventions by educational administrators. The findings show that AI application and teachers’ cognition are the core variables shaping educational anxiety and teachers’ well-being, and the moderating role of digital literacy is likewise non-negligible. Based on the data analysis, we propose multidimensional policy recommendations to mitigate educational anxiety, enhance teachers’ well-being, and improve the governance of AI applications in education, emphasizing coordinated efforts across teacher training, psychological support, organizational safeguards, and ethical norms to advance the deep integration of AI and education.

Research limitations and future prospects

Although this study conducted an in-depth, multidimensional exploration of teachers’ educational anxiety and professional well-being in the context of artificial intelligence by integrating SEM and ANN models, and arrived at a series of valuable conclusions, there are still several limitations due to research design and implementation conditions. These limitations also point to promising directions for future research.

First, the main limitation at the data level lies in the cross-sectional design and reliance on self-reported measures. While cross-sectional data can effectively reveal correlations and structural paths among variables, they have inherent weaknesses in establishing rigorous causal relationships. Teachers’ anxiety and well-being are dynamic processes that fluctuate with improvements in technological proficiency and changes in policy environments. Therefore, future research should adopt longitudinal tracking designs, continuously collecting data at multiple time points to capture the evolving trajectories of teachers’ psychological states, thereby more accurately testing the lagged and reciprocal effects among variables. Second, this study primarily focused on the individual and organizational levels of teachers. Future research should expand to more macro- and micro-level, multi-layered perspectives. At the macro level, regional educational policies, the level of digital infrastructure, and the broader socio-cultural climate undoubtedly influence teachers’ acceptance of AI technology and their anxiety levels. Future studies could employ multilevel linear models, integrating policy variables at the regional or school level with psychological variables at the individual teacher level to explore cross-level interactions. At the micro level, classroom dynamics—especially changes in teacher-student interaction patterns after the introduction of AI, and how these changes in turn affect teachers’ sense of achievement and well-being—represent a highly valuable but underexplored area. Through qualitative research methods such as classroom observation and in-depth interviews, researchers can provide rich and vivid micro-level explanations for the macro patterns identified in quantitative studies, thereby constructing a more comprehensive and multidimensional theoretical framework for understanding teachers’ professional development and psychological well-being in the AI era.

Authors’ contributions

Conceptualization, H.Z.; methodology, H.Z.; software, H.Z.; validation, H.Z.; formal analysis, H.Z.; investigation, H.Z.; resources, H.Z.; data curation, H.Z.; writing—original draft preparation, H.Z.; writing—review and editing, J.C.; visualization, H.Z.; supervision, J.C.; project administration, J.C.; funding acquisition, J.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by 2025 Jiangsu Province Higher Education Teaching Reform Research Project: “Practical Research on Collaborative Innovation of Vocational College Teaching Resources Driven by AIGC: Taking the Graphic and Image Processing Course as an Example” (Project No.: 2025JGYB565).

Data availability

The dataset used and analyzed in this study is available from the corresponding author upon reasonable request. The assessment was conducted using an online questionnaire specifically designed for the Chinese teacher population. This approach may result in a higher degree of subjectivity in the assessment data and limited representativeness of the survey sample. However, current research indicates that, with the rise of artificial intelligence, teachers’ levels of educational anxiety and stress remain high, all of which may further contribute to a decrease in teacher well-being and potentially lead to more serious psychological problems.

Declarations

Ethics approval and consent to participate

This study was approved by the Ethics Committee of Shazhou Professional Institute of Technology (2025-07). All procedures performed in this study followed the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all participants involved in the study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.