Enterprise AI Analysis
Challenge or threat? The double-edged sword effect of Al use on innovative teaching behavior among primary and secondary school teachers in China
Authors: Linghao Kong, Wenjin Zhang, Weijun Huang, Liang Huang & Shaonan Huang
DOI: https://doi.org/10.1057/s41599-026-07072-8
Abstract: With the rapid integration of artificial intelligence (AI) into educational practice, its influence on teachers' innovative teaching behavior has attracted growing attention. However, less is known about the dual psychological mechanisms through which AI use may both facilitate and constrain teaching innovation. Drawing on the cognitive appraisal theory of stress, this study examined whether teachers' challenge and threat appraisals mediate the relationship between AI use and innovative teaching behavior, and whether school innovation support moderates these pathways. A nationwide survey was conducted among 1,275 primary and secondary school teachers in China. Data were collected using validated measures of AI use, challenge appraisal, threat appraisal, innovative teaching behavior, and school innovation support, and were analyzed through confirmatory factor analysis, structural equation modeling, and bootstrapped moderated mediation analysis. The results indicated that AI use was positively associated with both challenge appraisal and threat appraisal. In turn, challenge appraisal was positively associated with innovative teaching behavior, whereas threat appraisal was negatively associated with it. Challenge and threat appraisals both served as significant mediating pathways linking AI use to innovative teaching behavior. In addition, school innovation support moderated the effects of AI use on both appraisals: higher levels of support strengthened the positive association between AI use and challenge appraisal, but unexpectedly also amplified its association with threat appraisal. These findings highlight the double-edged nature of AI use in educational settings and suggest that cognitive appraisal is an important mechanism through which AI use relates to teachers' innovative teaching behavior. The study further implies that schools should provide supportive conditions that encourage challenge appraisal while carefully managing the pressures that may intensify threat appraisal.
Executive Impact Summary
This study investigates the dual impact of AI use on innovative teaching behavior among primary and secondary school teachers in China, leveraging the cognitive appraisal theory of stress. It reveals that AI use acts as a 'double-edged sword,' simultaneously fostering innovation through challenge appraisal and hindering it through threat appraisal. Both appraisals fully mediate the relationship between AI use and innovative teaching behavior. School innovation support positively moderates the AI use-challenge appraisal link but unexpectedly amplifies the AI use-threat appraisal link. This suggests that while support encourages seeing AI as an opportunity, it also heightens pressure, leading to mixed perceptions. The findings emphasize the need for educational administrators to create supportive environments that encourage positive appraisals while carefully managing pressures to mitigate negative ones, fostering authentic AI integration.
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Key Takeaways for Education Leadership
- AI use influences teacher innovation through two distinct psychological pathways: challenge appraisal (positive) and threat appraisal (negative).
- School innovation support is crucial; it strengthens challenge appraisals but can also paradoxically amplify threat appraisals by increasing expectations.
- Effective AI integration requires nurturing positive cognitive appraisals and managing potential pressures, rather than just increasing AI usage.
- Policymakers should consider a 'challenge–threat index' to track how teachers perceive AI, guiding more targeted interventions.
ROI Justification: Based on a school of 500 teachers, each saving 5 hours/week at an average rate of $40/hour, with a 30% efficiency gain from AI, the annual reclaimed productivity is substantial. This ROI accounts for reduced administrative burden, enhanced content creation, and improved personalized feedback, leading to better student outcomes and teacher satisfaction.
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
This category focuses on the core psychological pathways identified in the study: challenge appraisal and threat appraisal.
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Teachers Surveyed Across China
Dual Psychological Pathways of AI Use
AI Use
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Challenge Appraisal
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Innovative Teaching Behavior
Negative Psychological Pathway of AI Use
AI Use
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Threat Appraisal
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Decreased Innovative Teaching Behavior
| Appraisal Type | Impact on Innovation |
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| Challenge Appraisal |
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| Threat Appraisal |
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This category examines how school innovation support influences teachers' appraisals of AI use.
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Beta for AI Use * SIS on Challenge Appraisal (p<0.001)
Paradoxical Effect of High Support
The study found that while higher school innovation support strengthened the positive association between AI use and challenge appraisal, it unexpectedly also amplified its association with threat appraisal. This 'amplifier' effect suggests that organizational support, by raising expectations alongside empowerment, can heighten teachers' sensitivity to both opportunities and pressures, leading to mixed cognitive appraisals.
| Appraisal Type | Effect of High Support |
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| Challenge Appraisal |
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| Threat Appraisal |
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This category synthesizes the overall dual impact of AI use on innovative teaching behavior.
Overall Double-Edged Effect
AI Use
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Challenge Appraisal (+)
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Innovative Teaching Behavior
Overall Double-Edged Effect (Negative Path)
AI Use
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Threat Appraisal (-)
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Innovative Teaching Behavior
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Indirect Effect (Challenge Path)
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Phased Implementation Roadmap
Our strategic approach ensures a smooth transition and maximizes long-term value, addressing both challenge and threat perceptions proactively.
Phase 1: Awareness & Initial Training (1-3 Months)
Establish foundational understanding of AI tools. Focus on basic functionalities and ethical considerations. Conduct workshops on identifying AI-driven opportunities.
Phase 2: Pilot Programs & Support Structures (3-6 Months)
Implement small-scale AI integration pilots in diverse classrooms. Develop dedicated support teams and internal communities of practice for knowledge sharing and problem-solving.
Phase 3: Curricular Integration & Advanced Training (6-12 Months)
Integrate AI tools into specific curriculum areas. Provide advanced training on AI-driven assessment, personalized learning, and data analytics. Foster a culture of experimentation.
Phase 4: Continuous Evaluation & Iteration (Ongoing)
Establish feedback loops for AI tool effectiveness and teacher perceptions. Monitor both challenge and threat appraisals. Adapt policies and support mechanisms based on ongoing evaluation.
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