“Filter” or “Accelerator”? A Mixed-Methods Study of the Driving Mechanisms of Innovative Work Behaviour among Higher Vocational Education Teachers Under AI Ethics Constraints

Abstract

Artificial intelligence (AI) empowerment and “human–machine collaboration” have triggered a wave of transformation and innovation in vocational education, while simultaneously raising concerns about the ethical challenges posed by the “black box” nature of AI. Drawing on the Attribution Theory and Self-Determination Theory, this study develops a moderated mediation model and employs multiple regression together with Johnson-Neyman method and fuzzy-set qualitative comparative analysis to analyse 413 survey responses. The findings indicate that basic psychological needs constitute the core driving force behind higher vocational education teachers’ AI involvement and innovative work behaviour, whereas institutional learning support exerts its effect only when effectively combined with other factors. AI ethics negatively moderates the effect of AI involvement on innovative work behaviour, thereby uncovering the “filter” role in shaping teachers’ pedagogical innovation. Furthermore, the fsQCA identifies three equivalent configurations leading to high levels of innovative work behaviour and reveals the underlying complex causal relationships. This confirms the dual role of AI ethics as both a “filter” and a “steering wheel” in higher vocational education teachers’ innovation. Based on these insights, the study proposes concrete policy recommendations aimed at helping higher vocational education teachers to better balance innovation and responsibility when applying AI.

Plain Language Summary

The Driving Mechanisms of Innovative Work Behaviour among Higher Vocational Education Teachers Under AI Ethics Constraints

Background The deep integration of artificial intelligence (AI) into education is reshaping both teachers’ competence frameworks and educational paradigms. Technological empowerment and “human–machine collaboration” have triggered a wave of transformation and innovation in education, while simultaneously raising concerns about the ethical challenges posed by the “black box” nature of AI. Methods Drawing on self-determination theory, this study develops a moderated mediation model and employs multiple regression together with Johnson-Neyman method and fuzzy-set qualitative comparative analysis (fsQCA) to analyse 413 survey responses. Results The findings indicate that basic psychological needs constitute the core driving force behind higher vocational education teachers’ AI involvement and innovative work behaviour, whereas institutional learning support exerts its effect only when effectively combined with other factors. AI ethics negatively moderates mediating effect of AI involvement, thereby uncovering both the regulatory role and the specific pathways through which AI ethics shapes teachers’ pedagogical innovation. Furthermore, the fsQCA identifies three equivalent configurations leading to high levels of innovative work behaviour and reveals the underlying complex causal relationships. Conclusion These findings indicate that the dual role of AI ethics as both a “filter” and a “steering wheel” in higher vocational education teachers’ innovation. Based on these insights, the study proposes concrete policy recommendations aimed at helping vocational college teachers to better balance innovation and responsibility when applying AI, thus promoting responsible AI-driven educational practices.

Keywords

higher vocational education teachers AI involvement self-determination theory fsQCA AI ethics

Introduction

Artificial intelligence (AI) technologies are penetrating and reshaping modes of production, social structures, and educational paradigms with unprecedented depth and breadth (Ng et al., 2023). As AI applications/tools become more accessible and friendly, the domain of AI technology education, previously restricted to higher education institutions, has expanded to include the broader public. It is now regarded as a core component within the framework of digital literacy for citizens (Long & Magerko, 2020), and an essential skill for adapting to future learning, work, and daily life (Laupichler et al., 2022; Ng et al., 2023; van Laar et al., 2020). Since the advent of educational informatization, technology-driven reforms have continuously reshaped educational paradigms (Du et al., 2024), providing a strong engine for personalised learning, improved teaching efficiency, talent cultivation, and promoting educational equity. Accordingly, teachers are required to continuously strengthen their technological competencies and, within ethical boundaries, engage in more innovative work behaviours (IWB) so as to adapt to and even lead the digital transformation of education (Gümüş & Kukul, 2023; Sary et al., 2023).

Since AI literacy was first defined in 2016 as “the ability to understand the basic technologies and concepts behind AI products” (Kandlhofer et al., 2019; Ng et al., 2023), preliminary research has laid the foundations for conceptual development (Kong et al., 2024; Long & Magerko, 2020; Ng et al., 2021a; Pinski & Benlian, 2024) and measurement (e.g., Lintner, 2024). The question of how to enhance citizens’ AI literacy has thus become a research hotspot (Du et al., 2024). For teachers, however, the ultimate aim of improving AI literacy is to stimulate educational innovation, transform traditional teaching paradigms, and promote the digital and intelligent transformation of education (Kong et al., 2022), thereby cultivating citizens capable of thriving in a digital society (Long & Magerko, 2020). Yet, across diverse educational contexts (Al-Mamary & Abubakar, 2025; Laupichler et al., 2022; Ng et al., 2024), the pursuit of universally applicable findings often risks neglecting the unique circumstances of particular groups of teachers (Lintner, 2024). In particular, compared with K-12 and higher education contexts, studies focussing on higher vocational education teachers remain scarce.

Existing studies have tended to draw on the Theory of Reasoned Action (TRA), the Theory of Planned Behaviour (TPB; Fayyaz et al., 2025), the Technology Acceptance Model, and the UTAUT/UTAUT2 framework to explain the antecedents and driving mechanisms of AI literacy (Du et al., 2024). These approaches place one-sided emphasis on either internal or external determinants of AI literacy while overlooking the joint and synergistic effects of organisational context and intrinsic motivation, which may lead to biased interpretations of behavioural causality (Deci & Ryan, 1985a; Ryan & Deci, 2020). Existing research has also tended to separate organisational factors from basic psychological needs when examining their joint influence or interaction in shaping individual behaviour (Chiu et al., 2022, 2024; Lee et al., 2020), and has rarely compared the relative strength of these influences. Accordingly, this study integrates Attribution Theory (Heider, 1958) and Self-Determination Theory (Deci & Ryan, 1985b), treating basic psychological needs as internal attributions and organisational support as external attributions, to jointly explain teachers’ AI involvement and IWB. Because fostering teachers’ active enhancement of AI literacy (Ortega-Bolaños et al., 2024), proactive responses to technological change, and the cultivation of personal creativity depends crucially on the extent to which they recognise the importance of AI technologies, and because involvement has been shown to robustly predict individual behaviour (Mou et al., 2020; Yang et al., 2023), this study introduces AI involvement as a mediating variable.

However, traditional linear regression and structural equation modelling can explain only symmetrical relationships and net effects among variables and cannot verify the asymmetrical interactions between internal and external factors (Fiss, 2011; Ragin, 2008). To mitigate the limitations of cross-sectional research for causal inference, this study combines multiple regression, the Johnson-Neyman technique, and fsQCA to examine, from an attributional perspective, how school learning support and basic psychological needs influence higher vocational education teachers’ IWB through AI involvement.

AI technologies demonstrate a “double-edged sword” effect (Dong et al., 2025): they generate significant innovative value but also raise numerous ethical dilemmas (L. Zhao et al., 2022). As a core dimension of AI literacy (Kong et al., 2022), AI ethics provides boundaries for innovation (Wang et al., 2025), regulates potential risks (Ng et al., 2021a), underscores the principle of social licence, promotes responsible innovation (Jobin et al., 2019), and steers AI technologies always centre on improving human welfare (Chen et al., 2026), thereby sustaining public trust (Ortega-Bolaños et al., 2024). Under such ethical constraints, existing research has mainly explored the predictive role of AI in non-moral behaviour (Cao et al., 2023; P. Zhao et al., 2026), but has paid little attention to the moderating effect on the process of IWB. Whether AI ethics functions as a “filter” or an “accelerator” has yet to be empirically tested.

To fill this knowledge gap, this study focuses on higher vocational education teachers and constructs a moderated mediation framework in which perceived school learning support and basic psychological needs affect IWB via AI involvement. Employing a mixed-methods design with multiple regression, Johnson-Neyman technique and fsQCA, the study addresses the following research questions:

RQ1: What are the relative effects of school learning support (external factor) and teachers’ basic psychological needs (internal factor) on AI involvement and IWB? Which plays the more significant role?

RQ2: Is the mediating effect of AI involvement robust in the vocational education context?

RQ3: Does AI ethics act as a “filter” or an “accelerator” in shaping IWB? Can it foster responsible innovation?

RQ4: How do the drivers of IWB interact with one another, especially the external and internal factors, and what configurational pathways lead to high IWB?

Theoretical Background and Hypothesis

Theoretical Background

Attribution Theory

Attribution Theory systematically examines how people explain the causes of their own and others’ behaviour, and how these explanations shape subsequent emotions, motivation, and behaviour (Hayat et al., 2022). In the early formulation of Attribution Theory, Heider (1958) classified the causes of behaviour into two broad categories: internal and external. Internal factors include personal traits, abilities, emotions, and effort, whereas external factors include task difficulty, luck, social pressure, and environmental conditions. Subsequent scholars further refined and extended this perspective. For example, Kelley’s (1967) covariation model argues that people rely on information accumulated across events and apply the covariation principle to resolve attributional questions under uncertainty. Weiner (1985) proposed that individuals make internal and external attributions mainly on the basis of locus of causality, stability, and controllability, thereby predicting and interpreting behaviour and events. The distinction between internal and external attribution has since become a cornerstone of attributional research and has been widely cited in the behavioural sciences (Soh & Kim, 2026), serving as an important theoretical framework for explaining complex human behaviour (Tavsanli, 2025).

Self-Determination Theory

Proposed by Deci and Ryan (1985a) and subsequently developed, Self-Determination Theory (SDT) is a macro theory of human motivation, personality development and psychological wellbeing. It goes beyond a simple dichotomy of intrinsic versus extrinsic motivation, offering a more comprehensive framework for understanding the drivers of individual behaviour (Ryan & Deci, 2000). SDT rests on the assumption that individuals possess innate growth tendencies and basic psychological needs (Ryan, 1995). It posits that three needs—autonomy, competence, and relatedness—determine autonomous motivation and constitute the core driving force for sustained, high-quality and effective behavioural outcomes (Ryan & Deci, 2017). Autonomy refers to the perceived sense of choice and control in action and decision-making; competence denotes the belief that one is capable of undertaking specific activities or completing tasks; and relatedness concerns the extent to which individuals feel accepted, understood and cared for by their social groups (Ryan, 1995). Over time, SDT has matured into a coherent framework comprising six sub-theories: Cognitive Evaluation Theory (Deci & Ryan, 1985a), Organismic Integration Theory (Ryan & Deci, 2000), Causality Orientations Theory (Deci & Ryan, 1985b), Basic Psychological Needs Theory (Ryan, 1995), Goal Contents Theory (Kasser & Ryan, 1993), and Relationships Motivation Theory (Ryan & Deci, 2017).

AI Involvement

Within social judgement theory, Sherif and Cantrill (1947) proposed the concept of “self-involvement” to capture the strength of the association individuals perceive between a situation or stimulus and the self. Krugman (1965) later introduced the notion of involvement into consumer-behaviour research, where it has since been widely adopted (Bruwer et al., 2017; Yang et al., 2023). Because the literature recognises multiple forms of involvement (Mou et al., 2020; Zhu et al., 2019), scholars have not reached a single agreed definition. Zaichkowsky (1986) distinguished product involvement, information involvement, and purchase-decision involvement, and defined involvement as “a person’s perceived relevance of an object based on inherent needs, values, and interests.” Accordingly, this study defines teachers’ AI involvement as the degree to which teachers attach importance to AI technology, or the perceived importance of AI technology to teachers’ work.

The double-edged nature of AI can enhance employees’ work efficiency and capabilities, but it may also trigger technology anxiety and, in turn, resistance to innovation. Accordingly, a key issue in promoting the digital innovation of teaching is how to heighten higher vocational education teachers’ recognition of the importance of AI and its relevance to their own work. From the perspective of both external and internal attributions, this study therefore examines the mediating role of AI involvement and its interplay with other factors in the proposed model.

Innovative Work Behaviour

Innovative work behaviour (IWB) refers to employees’ proactive generation, promotion and implementation of novel and useful ideas, methods, or solutions, driven by the goal of improving task performance (Anser et al., 2021; De Jong & Den Hartog, 2010; Liehr & Hauff, 2025; Scott & Bruce, 1994). At the individual level, many scholars adopt a process perspective (e.g., Bratianu et al., 2023; Kang & Lee, 2017; Lajçi et al., 2026). A classic three-stage process includes idea generation, idea promotion and idea application (Janssen, 2000). In response to diverse application scenarios, some have elaborated the process further—for instance, Bratianu et al. (2023) distinguish idea exploration, idea generation, idea championing, and idea implementation (Dong et al., 2025). Following Janssen (2000), we define teachers’ IWB in the present study as: vocational teachers’ proactive adaptation to new requirements for cultivating technically skilled talent by breaking through limitations of traditional teaching models, actively employing AI and other digital technologies, and creatively proposing, designing, advocating and implementing new pedagogical ideas, methods and assessments.

Hypothesis

Perceived School Learning Support and AI Involvement

Perceived school learning support reflects institutional efforts to upgrade teachers’ TPACK competencies (Ng et al., 2021a, 2023) by creating safe and open learning environments, providing financial support, involving teachers in decision making processes, and providing rich and accessible learning opportunities to improve AI literacy (Chiu et al., 2024). In a safe, open campus environment, teachers can freely exchange digital knowledge such as AI, accelerating knowledge sharing and knowledge flows (Liehr & Hauff, 2025), and strengthening knowledge integration (Sondhi et al., 2024), thereby accurately recognising the value of AI literacy and its importance for teaching (Du et al., 2024). Moreover, financial support for AI-related training, encouragement to participate in decisions about smart-teaching reforms, and diverse training opportunities can bolster organisational commitment (Lee et al., 2020), thereby increasing the salience of AI knowledge and skills, and, in turn, AI involvement. Chiu (2022) confirmed that school learning support motivates teachers to invest more resources in integrating digital knowledge. Hence:

H1. Perceived school learning support positively influences higher vocational education teachers’ AI involvement.

Basic Psychological Needs and AI Involvement

SDT posits that autonomy, competence and relatedness are key determinants of work motivation (Gagné & Deci, 2005; Ryan & Deci, 2000). The greater the satisfaction of these basic psychological needs, the more intrinsic motivation is activated, and autonomous motivation dominates behaviour (Deci & Ryan, 2008). Driven by autonomous motivation, vocational teachers’ behaviour is fuelled by interest, enjoyment and wellbeing associated with their professional mission (Deci & Ryan, 1985a). Confronted with new technologies such as AI, a positive professional attitude prompts teachers to enhance AI self-efficacy through greater technological involvement (Bergdahl & Sjöberg, 2025), thereby improving digital literacy (Du et al., 2024; Ng et al., 2023, 2024). According to Causality Orientations Theory, as need satisfaction increases, motivation becomes more internalised; teachers are more likely to respond to AI and smart-teaching requirements in line with professional interests and personal values (Deci & Ryan, 1985b), proactively increasing involvement with AI to strengthen their AI literacy framework (Long & Magerko, 2020). Need satisfaction is also motivating in itself and positively affects organisational commitment (Gagné et al., 2010), encouraging investment in AI involvement to achieve excellent performance. Similarly, Al-Mamary and Abubakar (2025) show that basic needs are positively related to task–technology fit and that individuals invest resources to learn tools such as ChatGPT. Therefore:

H2. Autonomy need positively influences higher vocational education teachers’ AI involvement.

H3. Competence need positively influences higher vocational education teachers’ AI involvement.

H4. Relatedness need positively influences higher vocational education teachers’ AI involvement.

AI Involvement and Innovative Work Behaviour

AI involvement reflects the degree to which vocational teachers attach importance to AI technologies (Yang et al., 2023). As competence in AI increases, teachers, motivated by professional values and enjoyment, proactively identify problems, and innovate work methods and solutions (Cai & Tang, 2022). Involvement positively predicts domain-specific learning (Kyndt & Baert, 2013). As AI literacy improves, AI self-efficacy and self-esteem grow (Rodríguez-Ruiz et al., 2025), enhancing the willingness and ability to generate insights and propose creative solutions (Dong et al., 2025). In the face of technological change, high involvement signals a strong sense of responsibility and a positive work attitude; responsibility awakens a sense of ownership and concern for disciplinary viability and competitive advantage (Blanco-Gonzalez et al., 2020), fostering proactive transformation and more IWB. Importantly, higher AI involvement accelerates AI knowledge accumulation, enabling better adaptation to and mastery of smart-education innovations, promoting knowledge sharing and internal diffusion, raising team AI literacy, and stimulating pedagogical innovation. Hence:

H5. AI involvement positively influences higher vocational education teachers’ IWB.

The Mediation Role of AI Involvement

From H1 and H5, perceived school learning support creates richer learning opportunities, accelerates AI knowledge flows, and enhances perceptions of insider status and professional wellbeing (Chiu et al., 2024). Through AI involvement, teachers learn and share AI knowledge, and better implement IWB (Dong et al., 2025; Zhang et al., 2024), thereby fulfilling professional commitments. Prior research shows that external stimuli influence behavioural intentions via involvement (Mou et al., 2020; Yang et al., 2023). According to the S–O–R model (Mehrabian & Russell, 1974), school learning support (S) can influence IWB (R) via teachers’ AI involvement (O). Therefore:

H6. AI involvement mediates the relationship between perceived school learning support and IWB.

Similarly, need satisfaction activates intrinsic motivation (Ryan & Deci, 2017). Autonomous motivation enhances professional identity and wellbeing (Tang et al., 2020) and organisational commitment (Lee et al., 2020), leading to greater emphasis on capability development (Yao et al., 2021). In response to AI, and to attain intrinsically valued goals that yield sustained wellbeing (Kasser & Ryan, 1993) and honour organisational commitments (Ćulibrk et al., 2018), vocational teachers increase AI involvement to drive digital-teaching reforms and innovation. Thus:

H7. AI involvement mediates the relationships between basic psychological needs and IWB: H7a. Autonomy → AI involvement → IWB; H7b. Competence → AI involvement → IWB; H7c. Relatedness → AI involvement → IWB.

The Moderated Mediation Role of AI Ethics

AI ethics refers to the design, development and use of AI in ways that enhance human wellbeing while adhering to human values, moral norms, and social customs (Gümüş & Kukul, 2023; Kong et al., 2024; Ng et al., 2024; Pinski & Benlian, 2024; L. Zhao et al., 2022). Centred on human considerations, AI ethics typically encompasses five elements: fairness, accountability, transparency, ethical considerations, and safety (Ng et al., 2021a). Its goals are to develop AI that benefits society whilst preventing risks arising from misuse (Laupichler et al., 2022; Ng et al., 2021b). Ng et al. (2021a) argue that AI should be human-centred; AI literacy entails not only building citizens’ AI capabilities and enabling them to benefit, but also educating them to be responsible and ethical users. Jobin et al. (2019) list 11 ethical principles for citizens to observe (Beneficence, Non-maleficence, Trust, Transparency & Explainability, Freedom & Autonomy, Privacy, Justice, Fairness & Equity, Responsibility & Accountability, Dignity, Sustainability, Solidarity). In this study, higher versus lower AI ethics reflects individuals’ perceptions of differing degrees of ethical constraints associated with AI.

AI ethics requires balancing productivity with “AI for social good” (Chai et al., 2020; Jobin et al., 2019), minimising negative impacts while meeting users’ needs. AI bears responsibility for contributing to a sane social system, pursuing fairness and equality (Ng et al., 2021b; Ortega-Bolaños et al., 2024), and organisations should devise policies to address potential unemployment associated with AI diffusion (Jobin et al., 2019). Consequently, when perceived constraints of AI ethics are strong, teachers’ motivation to learn and share AI knowledge may be dampened, prompting them to act more cautiously and to engage in ongoing AI ethical reflection (Wang et al., 2025); they may lower AI involvement to avoid downsides and reduce disruptive pedagogical innovations in order to fulfil social responsibilities (Ortega-Bolaños et al., 2024). With tighter boundary-setting and risk control measures in place, innovation costs and cycles increase, creativity is supplanted by a “prudent creativity” approach, and the policy effectiveness of school learning support operating through AI involvement experiences a decline. Conversely, when perceived constraints of AI ethics are weak, AI’s spill-over effects are fully released with fewer uncertainty constraints (Le et al., 2024) and may even give rise to moral suspension (Chen et al., 2026); as the positive impact of school learning support is amplified and AI identity becomes more readily formed, AI involvement increases due to a sense of psychological entitlement, potentially accompanied by moral relativism (Cao et al., 2023) and a surge in educational innovation. Therefore:

H8. AI ethics negatively moderates the mediation effect of AI involvement between perceived school learning support and IWB: the stronger the ethical constraint, the weaker the mediation (and vice versa).

By the same logic, when perceived constraints of AI ethics are strong, the level of motivation internalisation driven by autonomy, competence and relatedness needs diminishes; individuals perceive greater external pressure (Ryan & Deci, 2000), autonomous motivation shifts towards controlled motivation, and work anxiety (Gillet et al., 2016) and stress (Tremblay et al., 2009) rise, while job satisfaction and engagement fall. To adapt, teachers may reduce AI involvement, restrain their creativity, limit disruptive innovation, and avoid deviant innovation (Criscuolo et al., 2014). When perceived constraints of AI ethics are weak, external control lessens, need-driven internalisation strengthens (Deci & Ryan, 1985a; Mungra et al., 2024), autonomous motivation increases (Ryan & Deci, 2000) and positively predicts organisational citizenship behaviour (Lee et al., 2020; Sedlářík et al., 2024). In pursuit of excellent performance (Good et al., 2022), teachers will heighten AI involvement, enhance AI literacy and exhibit more IWB. Hence:

H9. AI ethics negatively moderates the mediating effect of AI involvement between basic psychological needs (H9a. autonomy, H9b. competence, and H9c. relatedness) and IWB: the stronger the ethical constraint, the weaker the mediation (and vice versa).

Based on the theoretical foundations and hypotheses above, we construct the conceptual model shown in Figure 1.

Figure 1.

Research model.

Methodology

Data Collection and Sample Characteristics

The formal survey was conducted between April and July 2025. The electronic questionnaire was created on the Wenjuanxing platform (https://www.wjx.cn/) and distributed to respondents via social media platforms such as WeChat and QQ. The sampling frame consisted of in-service teachers at vocational colleges and universities in China. Using purposive sampling, we selected representative Double High Plan institutions and non-Double-High institutions. Contacts were asked to distribute the electronic questionnaire only within their own departments to avoid the quality problems commonly associated with snowball sampling. Throughout the data-collection process, the research team actively monitored the sample structure to ensure adequate representativeness. Prior to distribution, we contacted friends, former classmates and middle- and senior-level administrators at target institutions to explain the research requirements; they subsequently shared the questionnaire link in work groups and encouraged teachers to complete it. To increase response rates, small electronic red packets were provided as incentives. We fully respected participants’ right to informed consent. The introductory page clearly stated the identity of the researchers, the purpose of the study, and the intended use of the data. Participants began the questionnaire only after providing consent, and an exit button was available throughout the survey so that respondents could withdraw at any time if they felt that any item infringed on their privacy.

Given the use of self-reported questionnaires, several measures were taken to ensure data quality: the causality among variables was concealed, anonymity was guaranteed to protect respondents’ privacy, and the order of research variables was randomised to reduce potential covariance and social desirability bias (Yang et al., 2023).

A total of 503 questionnaires were collected. After excluding invalid responses such as those with no variance in answers, respondents who were not in-service teachers, or not working in vocational colleges or universities, 413 valid samples remained, yielding a response rate of 82.11%. The structural characteristics of the sample are presented in Table 1.

Table 1.

Sample Profile (N = 413).

Variable	Category	N	Percentage (%)
Gender	Female	248	60
Gender	Male	165	40
Age	Less than 30 years	40	9.7
	30–40 years	109	26.4
	41–50 years	190	46
	51 years and above	74	17.9
Educational level	Bachelor	83	20.1
	Master	262	63.4
	PhD	68	16.5
Academic title	Junior	64	15.5
	Intermediate	148	35.8
	Associate senior	154	37.3
	Senior	47	11.4

Measurement

Perceived School Learning Support

Measured using the unidimensional scale revised by Chiu et al. (2024), comprising four items. Cronbach’s alpha = .881.

Basic Psychological Needs

Measured using the three-dimensional scale revised by Chiu et al. (2024), covering autonomy, competence and relatedness, with four items each. Cronbach’s alpha = .871, .863, and .921, respectively.

AI Involvement

Measured with the unidimensional scale developed by Ghali-Zinoubi and Toukabri (2019), with minor modifications to fit the educational AI context, comprising six items. Cronbach’s alpha = .901.

Innovative Work Behaviour

Measured with the unidimensional six-item scale used by Anser et al. (2021), with item wording adapted from Sary et al. (2023) to suit the teaching context. Cronbach’s alpha = .885.

AI Ethics

Measured using the five-item scale adapted from L. Zhao et al. (2022), theoretically developed by Ng et al. (2021a, p. 4). Cronbach’s alpha = .876. The items are listed in Appendix 1 .

Control Variables

Following previous studies, gender, age, educational level, academic title, and institute level were included as covariates.

Data Analysis and Results

Reliability and Validity Analysis

Using SPSS 26, the internal consistency of all scales was examined. Results show that Cronbach’s alpha coefficients of all scales exceeded the critical threshold of .70, indicating satisfactory reliability.

A seven-factor measurement model was constructed in AMOS 26. The confirmatory factor analysis results were as follows: χ² = 1339.444, df = 474, χ²/df = 2.826; SRMR = 0.056; RMSEA = 0.067; CFI = 0.911; GFI = 0.828; IFI = 0.911; TLI = 0.901. These indicate a good fit between the model and the sampling data. Standardised factor loadings of all items were greater than 0.65 (p < .001). Average variance extracted (AVE) values of all latent constructs exceeded 0.50, and composite reliability (CR) values were higher than 0.70, confirming good convergent validity (Hair et al., 2020). As shown in Table 2, the square roots of AVEs were greater than their correlations with other constructs, demonstrating acceptable discriminant validity (Hair et al., 2020).

Table 2.

Descriptive Statistics, Correlation Matrix and Discriminant Test (N = 413).

Variables	PSLS	PAU	PCO	PRE	INVO	ETAI	IWB
PSLS	0.810
PAU	0.533**	0.801
PCO	0.546**	0.648**	0.802
PRE	0.484**	0.678**	0.629**	0.862
INVO	0.322**	0.468**	0.529**	0.416**	0.783
AIET	0.290**	0.457**	0.515**	0.382**	0.518**	0.777
IWB	0.429**	0.476**	0.627**	0.458**	0.577**	0.591**	0.770
C.R.	0.883	0.876	0.877	0.920	0.905	0.884	0.896
Mean	5.196	5.512	5.453	5.583	5.563	5.828	5.780
SD	1.303	0.983	0.942	1.011	0.925	0.821	0.781

Note. The diagonal elements represent the square roots of the AVE values for the respective latent variables. PSLS = Perceived school learning support; PAU = Perceived autonomy; PCO = Perceived competence; PRE = Perceived relatedness; INVO = AI involvement; AIET = AI ethics; IWB = Innovative work behaviour. The same below.

p < .05. **p < .01.

Descriptive Statistics and Correlation Matrix

Table 2 presents means, standard deviations and Pearson correlation coefficients. Perceived school learning support was positively correlated with IWB (r = .429, p < .01). Autonomy, competence and relatedness were positively correlated with IWB (r = .458–.627, p < .01). Both school learning support and basic psychological needs (autonomy, competence, relatedness) were positively correlated with AI involvement (r = .322–.529, p < .01). AI involvement was also positively correlated with IWB (r = .591, p < .01). The moderating variable, AI ethics, showed low-to-moderate correlations with other predictors (r = .290–.518, p < .01).

Common Method Bias

Since both independent and dependent variables were self-reported, common method variance (CMV) could pose a threat (Mou et al., 2020). We applied both procedural and statistical remedies (Podsakoff et al., 2003; Yang et al., 2023). Procedurally, item order was randomised, causal relationships were concealed, anonymity was assured, and mature validated scales with high reliability and validity were used. Statistically, Harman’s single-factor test was performed: exploratory factor analysis without rotation showed that the first factor explained 40.627% of total variance, below the 50% threshold. In addition, a single-factor measurement model was estimated in AMOS 26 (χ² = 4,747.278, df = 495, χ²/df = 9.590; SRMR = 0.110; RMSEA = 0.144; CFI = 0.563; GFI = 0.485; IFI = 0.564; TLI = 0.533). The fit indices of the previously reported seven-factor model were markedly better than those of the single-factor model (Δχ² = 3,407.834, Δdf = 21, p < .001), further suggesting that CMV was not a serious concern.

Hypothesis Testing

Causal Analysis

To reduce collinearity effects, latent variable means were used in hypothesis testing (Variance inflation factors ranging from 1.030 to 2.371). With PROCESS 4.0 (95% confidence interval, 5,000 bootstrap resamples, Model 4), the results (see Table 3) revealed: Basic psychological needs were the primary driver of IWB (B = 0.481, β = .529, p < .001; Boot CI = [0.352, 0.605]). The effect of perceived school learning support on IWB was smaller but significant (B = 0.071, β = .119, p < .05; Boot CI = [0.001, 0.146]). Among the three needs, only competence significantly influenced IWB (B = 0.429, β = .517, p < .001; Boot CI = [0.322, 0.552]); autonomy (B = 0.047, β = .060, p > .1) and relatedness (B = 0.040, β = .051, p > .1) were not significant.

Table 3.

Mediation Effects of AI Involvement (N = 413).

Independent variables	Point estimate	Products of coefficients		Bootstrapping (5,000 samples)
Independent variables	Point estimate	SE	Z	Lower	Upper
	Indirect effects
School learning support	−0.002	0.012	0.167	−0.026	0.024
Autonomy	0.050*	0.020	2.471	0.011	0.092
Competence	0.105***	0.026	4.086	0.058	0.159
Relatedness	0.011	0.021	0.549	−0.032	0.050
	Contrasts
School learning support vs. autonomy	−0.052*	0.023	−2.229	−0.097	−0.007
School learning support vs. competence	−0.107***	0.029	−3.737	−0.164	−0.050
School learning support vs. relatedness	−0.013	0.024	−0.537	−0.060	0.034
Autonomy vs. competence	−0.055	0.033	−1.677	−0.120	0.010
Autonomy vs. relatedness	0.039	0.029	1.345	−0.018	0.096
Competence vs. relatedness	0.094**	0.033	2.813	0.029	0.159

p < .05. **p < .01. ***p < .001.

Autonomy (B = 0.179, β = .190, p < .01; Boot CI = [0.046, 0.320]), competence (B = 0.372, β = .379, p < .001; Boot CI = [0.244, 0.504]), and education level (B = 0.144, β = .090, p < .05; Boot CI = [0.012, 0.272]) positively influenced AI involvement. In contrast, school learning support (B = −0.008, β = −.011, p > .1; Boot CI = [−0.094, 0.083]) and relatedness (B = 0.040, β = .044, p > .1; Boot CI = [−0.112, 0.183]) were not significant. AI involvement positively predicted IWB (B = 0.282, β = .334, p < .001; Boot CI = [0.186, 0.378]). Thus, H2, H3, and H5 were supported, while H1 and H4 were not.

Mediation Effect Analysis

Mediation effect was tested using PROCESS with 5,000 bootstrap iterations. Results (see Table 3) showed that AI involvement significantly mediated the relationships between autonomy, competence and IWB, with the strongest indirect effect in the competence → IWB path. Comparisons indicated that mediation effects for autonomy and competence were stronger than for school learning support → IWB. This finding further highlights basic psychological needs as the core driving force of AI involvement and IWB. Thus, H7a and H7b were supported, while H6 and H7c were not.

Moderated Mediation Effect of AI Ethics

Following the procedure proposed by Edwards and Lambert (2007), we tested the moderated mediation model as a whole. After setting AI ethics at one standard deviation above and below the mean and controlling for covariates, Mplus 8.3 was used to estimate the high and low AI ethics subgroups and to compare differences in the first stage, second stage, direct, and indirect path coefficients. As shown in Table 4, AI ethics negatively moderated the second stage of the mediation model (Difference = −0.106, p < .05), whereas its moderating effects on the four indirect paths were not significant. These findings suggest that AI ethics plays a filtering role in AI-driven IWB and helps safeguard responsible innovation. Taken together with the mediation results, the evidence provides only partial support for H9a and H9b, whereas H8, H9c are not supported.

Table 4.

Results of the Moderated Mediation Effect (N = 413).

Mediation path	Moderator	Stage		Effect
Mediation path	Moderator	First stage	Second stage	Direct effect	Indirect effect
PSLS → INVO → IWB	High AIET	−0.037	0.144**	0.067	−0.005
	Low AIET	0.039	0.250***	0.094	0.010
	Difference	−0.077	−0.106*	−0.028	−0.015
AU → INVO → IWB	High AIET	0.142*	0.144**	0.250***	0.020
	Low AIET	0.124	0.250***	−0.106*	0.031
	Difference	0.017	−0.106*	0.021	−0.011
CO → INVO → IWB	High AIET	0.238**	0.144**	0.376***	0.034*
	Low AIET	0.273***	0.250***	0.134*	0.068**
	Difference	−0.035	−0.106*	0.242**	−0.034
RE → INVO → IWB	High AIET	0.144**	0.144**	−0.073	0.021
	Low AIET	−0.055	0.250***	0.129*	−0.014
	Difference	0.199	−0.106*	−0.203**	0.034

Note. PSLS = Perceived school learning support; AU = Autonomy; CO = Competence; RE = Relatedness; INVO = AI involvement; IWB = Innovative work behaviour.

p < .05. **p < .01. ***p < .001.

Using the Johnson-Neyman technique recommended by Hayes (2018) and Preacher et al. (2007), this study further estimated the ranges of the moderator for which the conditional mediation effects were significant, together with their 95% confidence intervals. As shown in Figures 2 and 3, across the full observed range of AI ethics, the indirect effect of autonomy on IWB was not significant. When 4.744 ≤ AI ethics ≤ 6.705, the indirect effect of competence on IWB was significant and decreased as the moderator increased, thereby revealing the “filtering” role of AI ethics in shaping educational innovation among higher vocational education teachers.

Figure 2.

Johnson-Neyman plot of the moderated mediation effect of AI ethics (independent variable = autonomy).

Figure 3.

Johnson-Neyman plot of the moderated mediation effect of AI ethics (independent variable = competence).

Fuzzy-Set Qualitative Comparative Analysis

Variable Selection and Calibration

Variable Selection

Correlation analysis revealed that perceived school learning support (r = .429, p < .01), autonomy (r = .476, p < .01), competence (r = .627, p < .01), relatedness (r = .458, p < .01), AI involvement (r = .577, p < .01) and AI ethics (r = .591, p < .01) were all significantly and positively correlated with IWB. Among control variables, academic title was significantly and negatively correlated with IWB (r = −.103, p < .05). Accordingly, perceived school learning support, autonomy, competence, relatedness, AI involvement, AI ethics and academic title were selected as conditions. The outcome variable was IWB.

Calibration

Continuous variables were calibrated in fsQCA 3.0 following Ragin’s (2008) direct method, with the fifth percentile representing full non-membership, the 50th percentile as the crossover point, and the 95th percentile as full membership. For the categorical variable academic title, values were assigned as follows: junior = 0, intermediate = 0.33, associate senior = 0.67, senior = 1 (Emmenegger, 2011).

Necessary Condition Analysis

Necessity analysis was conducted for the seven conditions to determine whether any constitute necessary conditions for high (or non-high) IWB. As shown in Table 5, all consistencies were below 0.90, though coverage was adequate (Fiss, 2011). Thus, no single factor alone is necessary for high or non-high IWB, and further configurational analysis was required.

Table 5.

Necessity Condition Analysis (N = 413).

Conditions	High innovative work behaviour		Non-high innovative work behaviour
Conditions	Consistency	Coverage	Consistency	Coverage
PSLS	0.793	0.724	0.580	0.621
∼PSLS	0.585	0.544	0.742	0.807
AU	0.794	0.752	0.585	0.649
∼AU	0.629	0.564	0.777	0.816
CO	0.848	0.764	0.579	0.611
∼CO	0.568	0.535	0.776	0.857
RE	0.805	0.725	0.603	0.636
∼RE	0.596	0.561	0.740	0.816
INVO	0.808	0.789	0.548	0.626
∼INVO	0.617	0.538	0.815	0.832
AIET	0.837	0.783	0.588	0.644
∼AIET	0.619	0.562	0.802	0.852
Title	0.645	0.616	0.653	0.731
∼Title	0.719	0.639	0.657	0.684

Note. AIET = AI ethics; Title = academic title.

Configurational Analysis

A truth table was constructed in fsQCA 3.0, with the consistency threshold set at 0.80, frequency threshold at 1, and PRI (Proportional Reduction in Inconsistency) at 0.70 (Greckhamer et al., 2018). Configurational analysis for high IWB yielded complex, parsimonious and intermediate solutions. The intermediate solution was adopted for interpretation, while the parsimonious solution was used to distinguish core and peripheral conditions. Symbols were used as follows: ▲ = core condition present; ● = peripheral condition present; ⊗ = core condition absent; ⨂ = peripheral condition absent; blank = “don’t care” condition.

As shown in Table 6, three configurations were identified, each with consistency above 0.90, indicating sufficiency for high IWB. The overall solution consistency was 0.935, meaning that 93.5% of cases meeting these configurations exhibited high IWB. The overall solution coverage was 0.624, suggesting that these three configurations explained 62.4% of high IWB cases.

Table 6.

Configuration of High IWB Related to Higher Vocational Education Teacher (N = 413).

Conditions	High innovative work behaviour
Conditions	Configuration 1	Configuration 2	Configuration 3
Perceived school learning support	▲		▲
Autonomy	▲	▲
Competence	▲	▲	▲
Relatedness		▲	•
AI involvement	▲	▲	▲
AI ethics	▲	▲	▲
Academic title			⊗
Consistency	0.947	0.942	0.952
Raw coverage	0.567	0.588	0.453
Unique coverage	0.024	0.044	0.013
Solution consistency	0.935
Solution coverage	0.624

Configuration 1: “Person–Organisation Goal Alignment”

When vocational colleges provide a supportive environment that couples organisational and personal goals, synergies emerge. With AI involvement and ethical governance in place, this leads to high IWB. This pathway indicates that schools should align institutional goals with teachers’ growth tendencies, satisfy autonomy and competence to trigger intrinsic motivation, and, coupled with AI involvement and ethical filtering, stimulate more sustainable and responsible pedagogical innovation.

Configuration 2: “Self-Determination”

When all three basic psychological needs are satisfied, teachers experience greater job satisfaction and autonomous motivation, voluntarily engaging in organisational citizenship behaviours. In combination with AI involvement and AI ethics, this generates professional value, career interest, and wellbeing, thereby enabling proactive, sustained and high-quality innovative behaviours.

Configuration 3: “Supply–Demand Alignment.”

This configuration reflects generally modest digital literacy among vocational teachers, alongside psychological dependence on institutional support. Driven by high competence needs, teachers aspire for training opportunities and financial support in AI and other digital technologies, to attain AI self-efficacy and achievement. With AI involvement and ethical regulation, supplemented by relatedness need satisfaction, innovation motivation is transformed, fostering more responsible educational innovation.

Robustness Test

To test robustness, the consistency threshold was raised to 0.90. The analysis again identified three configurations leading to high IWB, consistent with Table 6, confirming the robustness of the results. The robustness check is presented in Table 7.

Table 7.

Robustness Test Result (N = 413).

Conditions	High innovative work behaviour
Conditions	Configuration 1	Configuration 2	Configuration 3
Perceived school learning support	▲		▲
Autonomy	▲	▲
Competence	▲	▲	▲
Relatedness		▲	▲
AI involvement	▲	▲	▲
AI ethics	▲	▲	▲
Academic title	▲		⨂
Consistency	0.954	0.942	0.952
Raw coverage	0.389	0.588	0.453
Unique coverage	0.017	0.057	0.013
Solution consistency	0.934
Solution coverage	0.618

Conclusions and Discussion

Grounded in Attribution Theory and Self-Determination Theory, this study employed multiple regression and fsQCA to examine not only the “net effects” of individual variables but also the “interaction effects” among variables, thereby providing valuable insights into how to foster responsible IWB among higher vocational education teachers.

Basic Psychological Needs Exert a More Robust Influence on Teachers’ IWB

Regression results confirmed that internal factors (basic psychological needs) are the dominant drivers of both IWB and AI involvement, thereby replicating findings from basic psychological needs theory in the vocational education context (Ryan, 1995; Ryan & Deci, 2020). This corroborates the view that fulfilling psychological needs promotes positive individual behaviour (Dong et al., 2025; Gagné et al., 2010). Unlike Chiu et al. (2024) but consistent with Lee et al. (2020), our findings indicate that among the three needs, competence plays the most pivotal role in driving both AI involvement and innovation, whereas autonomy, and relatedness do not significantly drive IWB. This may reflect the comparatively lower overall proficiency of higher vocational education teachers than their counterparts in general higher education, highlighting their strong need for AI self-efficacy (Bergdahl & Sjöberg, 2025; Fryer et al., 2020), as well as the relatively limited social prestige of vocational education and teachers’ weaker identification with vocational institutes. In contrast, the external factor of perceived school learning support had only a limited effect on IWB (β = .119, p < .05) and no significant effect on AI involvement. This differs from Chiu et al. (2024; teachers in general) and Cai and Tang (2022; primary school teachers), but is consistent with Lee et al. (2020; primary teachers), confirming our conclusion that AI-driven educational innovation behaviours differ across teacher groups.

School Learning Support Requires Synergy with Other Factors to Release Its Enabling Value

The limited effects of school learning support on AI involvement and IWB in the regression analysis suggest that vocational institutes may still face constraints in digital readiness, institutional attractiveness, and the supply of AI-related learning resources; under such conditions, only the synergy between internal and external factors can effectively alleviate these challenges. Configurational analysis showed that perceived school learning support is a core condition for high IWB, deepening the regression results by capturing its interaction effects with other variables and identifying sufficient condition sets that unlock its enabling value. These findings affirm the validity of cognitive evaluation theory in vocational education (Deci & Ryan, 1985a): school support must be matched with psychological needs (Gagné & Deci, 2005), particularly competence, to enhance motivational internalisation (Ryan & Deci, 2017) and thereby activate innovation. Consistent with Lintner (2024), we found that the three equivalent pathways to innovation carry distinct group characteristics. For example, Configuration 2 suits individuals with independent self-construals, while Configuration 3 suits younger teachers (mainly junior/intermediate ranks), whose professional capabilities are lower than those of university counterparts and who exhibit interdependent self-construals. These findings extend Chiu (2022) and Chiu et al. (2024)’s findings and underscore the theoretical and practical value of teacher group classification in AI education research (Laupichler et al., 2022; Ng et al., 2024).

AI Involvement is an Effective Lever and Core Condition for Innovation

Mediation analysis showed that AI involvement significantly mediates the relationships between autonomy, competence and IWB, highlighting the importance of raising teachers’ awareness of AI as a pathway to innovation. This finding, consistent across contexts and over time, confirms AI involvement as a robust mediator (Fayyaz et al., 2025; Mou et al., 2020). However, mediation was non-significant in the cases of school support and relatedness, identifying the relative importance of different antecedents when driving innovation through AI involvement (Yang et al., 2023). In fsQCA results, AI involvement emerged as a core condition across all three configurations, clearly showing how it combines with both external and internal factors to generate innovation. The mixed-methods approach (regression + fsQCA) therefore extends prior studies (Mou et al., 2020; Zhu et al., 2019) and provides richer theoretical and practical insights.

AI Ethics Acts as Both “Filter” and “Steering Wheel” for Educational Innovation

Regression analysis confirmed that AI ethics negatively moderates the second stage of the mediation model, weakening its positive effect of AI involvement on IWB, thus evidencing the regulatory role of AI ethics (Pinski & Benlian, 2024). This aligns with Ng et al. (2021a), Long and Magerko (2020), and Kong et al. (2024), which emphasise that the use of AI must be guided by moral principles and social values (Chen et al., 2026), and that teachers must approach AI tools critically (L. Zhao et al., 2022). Configurational analysis further confirmed AI ethics as a core condition for innovation, suggesting that AI ethics suppress AI technology abuse and low-ethical-standard innovations. This resonates with D’Este et al.’s (2012) concept of deterrent barriers, where ethics acts as a filter against inappropriate innovations. Reasonable ethical constraints therefore prevent trust collapse caused by irresponsible innovation, steering AI innovation towards social good and achieving a harmony of innovation and social value, thus ultimately fostering innovative activities for student well-being in vocational education.

Implications

Theoretical Implications

Building on the Attribution Theory and Self-Determination Theory, this study examines, within a moderated mediation framework, the differentiated effects of internal, and external attributions on higher vocational education teachers’ AI involvement and IWB. By identifying the relative importance of internal and external drivers, as well as their interaction, the study enriches the literature on Self-Determination Theory and extends its application to AI-driven educational change. The findings also provide theoretical guidance for promoting AI-enabled teaching transformation and teachers’ pedagogical innovation.

Second, the study identifies both facilitators and inhibitors of teachers’ IWB in AI contexts, and provides early empirical evidence of the “filter effect” of AI ethics. This contributes theoretical guidance for fostering a responsible AI-driven educational innovation ecosystem that balances human agency with technological development.

Third, by combining linear regression and fsQCA as complementary methods, the study not only tests symmetrical relationships and net effects among variables but also reveals configurational effects, thereby addressing the limitations of single-method approaches and enriching the analytical paradigm for complex causality in educational research. By focussing on higher vocational education teachers, the study avoids the fit problems that can arise from overly broad samples, while also offering insights relevant to secondary vocational, K-12, and research-oriented higher education teachers’ AI-related pedagogical innovation.

Practical Implications

More specifically, because internal drivers are central to AI-enabled educational transformation, greater attention should be paid to satisfying higher vocational education teachers’ basic psychological needs and strengthening their intrinsic motivation to master AI technologies. Given the challenges of professional identity and relatively low social prestige in vocational education, governments should raise the level and quality of vocational education provision, ensure that vocational institutions benefit from policy support comparable to that received by research universities, increase investment in digital teaching infrastructure, and create richer AI-enabled teaching scenarios so as to strengthen teachers’ sense of competence and relatedness. Vocational colleges and universities should also foreground teachers’ agency, rebuild their professional vision and identity, and encourage them to confront the paradigm shift and structural transformation brought by AI in vocational education as active leaders rather than passive recipients. Institutions should systematically cultivate teachers’ autonomy, reduce feelings of external control, enable them to experience greater enjoyment in AI-enabled educational change, protect their occupational mental health, and help them achieve stronger job satisfaction and AI self-efficacy. Anchored in professional responsibility, vocational education should return to value rationality and encourage teachers to enhance their AI literacy and engage in more IWB as promoters of new technologies, co-creators of skills-based knowledge, and disseminators of emerging technologies in the AI era.

Second, vocational colleges and universities should strengthen organisational support and revitalise organisational capacity. They need to optimise reciprocity between teachers and institutions, enhance psychological empowerment and organisational commitment, and release the synergistic effects of internal and external drivers in AI-enabled educational change and responsible innovation. In practical terms, institutions should integrate fragmented support policies into a more systematic framework, coordinate hardware, software, and data resources more effectively, and improve the concentration and accessibility of digital resources. Innovation-oriented evaluation and incentive mechanisms should also be designed by adding dedicated indicators of “AI teaching innovation” to promotion, appointment, and annual appraisal systems, and by establishing special funds to support teachers’ AI-related educational innovation. Alongside expanded opportunities for AI-skills training, institutes should foster an inclusive, open, and fault-tolerant culture of human-machine collaboration so as to stimulate teachers’ AI creativity.

Third, enhancing AI involvement is an effective means of stimulating IWB. Satisfying autonomy, competence and relatedness helps sustain autonomous motivation, enabling teachers to embrace AI challenges, continuously upgrade AI literacy, and innovate in teaching. Management should move beyond reliance on quantitative performance metrics, which risk eroding wellbeing, and instead balance short-term accountability with sustainable development, using supportive external incentives while maintaining satisfaction of psychological needs. Cultivating a sense of belonging and framing schools as professional growth platforms can further sustain innovation.

Finally, a responsible innovation ecosystem should be built within an AI ethics framework. Teachers’ agency should be foregrounded and a human-centred, responsibility-oriented AI ethics framework should be established. The negative moderation effect of AI ethics is not a restriction on innovation per se, but rather a calibration of innovation direction and content and a safeguard for innovation quality. This requires the cultivation of appropriate values regarding AI, the alleviation of resistance arising from AI-related replacement anxiety, and the promotion of human-machine collaboration rather than human-machine competition. In carrying out AI-enabled teaching innovation, teachers should honour value commitments and pay particular attention to human agency, fairness, transparency, privacy, and data security, embedding AI ethics systematically throughout the entire innovation process. More importantly, it is necessary to strengthen teachers’ own AI ethics literacy, build their capacity for responsible innovation, enhance students’ awareness of AI ethics, and cultivate managers’ ethical leadership in AI governance, thereby forming a multi-actor system of collaborative governance for responsible innovation and avoiding moral suspension (Chen et al., 2026). Institutions should develop clear responsibility maps for AI-enabled teaching innovation and establish school-level AI ethics committees so that AI returns from technical display to its educational mission, and innovation evaluation shifts from a narrow focus on efficiency to a broader concern for holistic human development and social wellbeing. Attention should also be paid to public and student resistance to AI in education. In line with the logic of innovation diffusion, innovative proposals should initially remain within the acceptance threshold of general audiences, so as to prevent the emergence of negative attitudes, and emotions at the source and reduce the risks associated with AI misuse.

Limitation and Future Direction

First, the generalisability of the findings requires further validation. The data were collected in China, and some findings may reflect country-specific factors. Future research could adopt cross-national comparative designs to broaden applicability. Second, the study relied on cross-sectional self-reported data, which may introduce collinearity and common method bias, despite mitigation measures. Longitudinal designs are recommended to test causal relationships more robustly. Third, focussing solely on vocational teachers enhanced depth but limited transferability. Future studies should include diverse education levels to allow comparative insights.Finally, extant research commonly employs general definitions and scales of AI ethics. However, ethical norms vary across cultures, institutions, groups and religions. Future research should refine the conceptualisation of AI ethics in specific contexts and develop corresponding measurement instruments.

Footnotes

Appendix 1

Table A-1.

Construct and Items.

Construct	Item	Loading
Perceived school learning support (AVE = 0.656, Cronbach’s alpha = .881)	My institute helps me see areas in which I need more training on digital technologies such as AI.	0.659
	My institute suggests ways to improve my digital competence.	0.827
	My institute provides me with frequent opportunities to develop new skills for teaching with digital technologies such as AI.	0.881
	My institute teaches me how to solve problems on my own when teaching with digital technologies such as AI.	0.853
Autonomy (AVE = 0.641, Cronbach’s alpha = 0.871)	When teaching with technologies such as AI in the classroom …
	I feel a sense of choice and freedom in the things I undertake.	0.681
	I feel that my decisions reflect what I really want.	0.862
	I feel my choices express who I really am.	0.840
	I feel I have been doing what really interests me.	0.806
Competence (AVE = 0.643, Cronbach’s alpha = .863)	When teaching with technologies such as AI in the classroom …
	I can select technologies to use in my classroom that enhance what I teach, how I teach and what students learn.	0.804
	I can choose technologies that enhance my teaching subject content for a lesson.	0.863
	I can teach lessons that appropriately combine my teaching subject, technologies and teaching approaches.	0.827
	I can provide leadership in helping others to coordinate the use of subject content, technologies and teaching approaches at my school.	0.704
Relatedness (AVE = 0.743, Cronbach’s alpha = .921)	When teaching with technologies such as AI in the classroom …
	I feel that the people I care about also care about me.	0.896
	I feel connected with the people who care for me and for whom I care.	0.875
	I feel close and connected with other people who are important to me.	0.856
	I experience a warm feeling with the people I spend time with.	0.818
Teacher AI involvement (AVE = 0.613, Cronbach’s alpha = .901)	AI in teaching is something that really matters to me.	0.750
	The use of AI in education is something to which I attach special importance.	0.828
	I particularly like to talk about AI applications at work.	0.735
	You can say that AI in education is something that interests me.	0.807
	I am particularly attracted by AI tools for teaching.	0.794
	The simple fact of learning about AI in education is a pleasure.	0.779
Innovative work behavior (AVE = 0.593, Cronbach’s alpha = .885)	I actively seek out new AI technologies, tools, or methods to improve my teaching in higher vocational education.	0.762
	I generate creative ideas for integrating AI into vocational curriculum design or student skills development.	0.811
	I promote and advocate my AI-based teaching innovations to colleagues.	0.872
	I investigate and secure resources (e.g., AI applications, tools, training, funding) needed to implement AI-driven teaching innovations.	0.847
	I develop adequate plans and schedules for the implementation of AI-supported teaching practices.	0.664
	Overall, I am innovative.	0.633
AI ethics (AVE = 0.604, Cronbach’s alpha = .876)	I always follow ethical principles when using educational AI products.	0.724
	I am alert to privacy and information security issues when using educational AI products.	0.835
	I am alert to the misuse of educational AI.	0.807
	I always consider ethical and security issues when applying educational AI technologies.	0.818
	I am able to detect ethical and moral violations during the application of educational AI in a timely manner.	0.691

ORCID iDs

Xianchuan Yang

Li Ma

Ethical Considerations

This study was conducted in accordance with the principles of the Declaration of Helsinki. Ethical approval was obtained from the Academic Committee of Wuxi Vocational Institute of Commerce (Approval ID: 2025-03). The study procedures were designed to ensure compliance with ethical standards and to protect participants’ rights and privacy. During the study period, electronic informed consent was obtained from all participants through the online survey introduction page prior to participation. The introduction page provided detailed information about the study, including the intended use of the collected data (i.e., analysed in aggregate form and published without any personally identifiable information). Participants were informed of their right to withdraw from the study at any time, both during and after completing the survey. Consent was confirmed when participants clicked the “Agree” button on the introduction page before proceeding to the survey questions.

Consent to Participate

During the study period (from April to July 2025), electronic informed consent was obtained from all participants through the online survey introduction page prior to participation. The introduction page provided detailed information about the study, including its purpose, the intended use of the collected data (i.e., analysed in aggregate form, and published without any personally identifiable information). Participants were informed of their right to withdraw from the study at any time, both during and after completing the survey. Consent was confirmed when participants clicked the “Agree” button on the introduction page before proceeding to the survey questions.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This paper is financially supported by the 2024 Humanities, and Social Sciences Planning Fund Project of Chinese Ministry of Education (24YJA880078), 2023 Key Project of Jiangsu Provincial Education Science Planning (B/2023/02/96), Middle-aged academic leaders of “Qinglan” project in Jiangsu (RS24QL05).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.*

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