Education Technology
Sustainable AI Integration in Education: Factors Influencing Pre-Service Teachers' Continuance Intention to Use Generative AI
Executive Impact & Key Findings
As artificial intelligence (AI) changes educational practices, understanding what sustains pre-service teachers' generative AI use beyond initial adoption becomes important. However, existing research mainly focuses on initial acceptance rather than continuance intention, which is a more realistic indicator for sustainable technology integration. This study drew on an integrated framework including psychological (GAI anxiety, GAI self-efficacy), contextual (facilitating conditions, social influence), and perceptual factors (perceived ease of use, perceived usefulness) to examine pre-service teachers' continuance intention toward GAI in future teaching. Survey data from 549 Chinese pre-service teachers were analyzed using structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA). Results showed that GAI self-efficacy had the strongest positive associations with both perceived ease of use and perceived usefulness. GAI anxiety negatively influenced both perceptions. However, facilitating conditions did not significantly relate to perceived usefulness. The fsQCA identified six configurational pathways clustered into the following three patterns: intrinsic value driven, efficacy capability driven, and external support driven. These findings suggest that teacher education programs should prioritize building GAI self-efficacy and supportive peer environments and not focus solely on infrastructure provision.
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Model Fit (CMIN/DF)
Deep Analysis & Enterprise Applications
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Psychological Factors
GAI Anxiety and GAI Self-Efficacy are individual psychological states influencing how pre-service teachers perceive and intend to use AI. Anxiety creates barriers, making AI seem harder to use and less helpful, while high self-efficacy increases confidence, reduces perceived difficulty, and promotes long-term engagement.
Contextual Factors
Facilitating Conditions (organizational support, resources) and Social Influence (peer/professor endorsement) shape teachers' perceptions. While technical support can ease AI use, social influence often drives the perception of AI's usefulness, especially in collective cultures like China.
Perceptual Factors & Continuance Intention
Perceived Usefulness (PU) and Perceived Ease of Use (PEoU) directly impact continuance intention. Ease of use makes AI less effortful, leading to higher perceived usefulness. Ultimately, perceived usefulness is a stronger predictor of sustained AI adoption, as teachers continue using AI if it helps achieve teaching goals.
Integrated Framework
This study combines TAM and UTAUT, extending to ECM's concept of continuance intention, to offer a holistic view of sustainable AI integration. It integrates psychological (GAI anxiety, GAI self-efficacy), contextual (facilitating conditions, social influence), and perceptual factors (perceived ease of use, perceived usefulness) to understand factors influencing pre-service teachers' sustained use of generative AI in future teaching practices.
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Strongest Positive Association (GAI Self-efficacy to PEoU)
Enterprise Process Flow
Intrinsic-value driven (C1, C2, C3)
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Efficacy-capability driven (C4)
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External-support driven (C5, C6)
| Factor | Impact on Perceived Usefulness (PU) | Impact on Perceived Ease of Use (PEoU) |
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| GAI Anxiety |
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| GAI Self-Efficacy |
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| Facilitating Conditions |
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| Social Influence |
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Teacher Education Program Recommendations
The findings suggest that teacher education programs should focus on building GAI self-efficacy through hands-on practice while also creating environments where teachers and peers openly support AI use. Universities should recognize that providing equipment alone is not enough; they also need to show pre-service teachers how generative AI can help with actual teaching tasks. The fsQCA findings further suggest that helping pre-service teachers move from relying on external support to developing internal motivation is important for long-term AI use.
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Your AI Implementation Roadmap
A phased approach to integrate AI sustainably and effectively into your operations, informed by best practices.
Phase 01: Strategic Assessment & Planning
Identify key pain points, define AI objectives, and select pilot areas. Conduct a readiness assessment and develop a tailored implementation strategy.
Phase 02: Pilot Program & Iteration
Implement AI tools in a controlled environment. Gather feedback from early adopters (e.g., pre-service teachers), measure initial impact, and refine workflows.
Phase 03: Scaled Integration & Training
Expand AI adoption across relevant departments. Provide comprehensive training focusing on GAI self-efficacy and practical application in teaching tasks. Foster a supportive environment.
Phase 04: Performance Monitoring & Optimization
Continuously monitor AI system performance, gather user feedback, and measure ROI. Identify opportunities for further AI enhancements and process automation.
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