Enterprise AI Analysis
Students' Continuance Intention to Use Generative AI in Higher Education
This deep dive explores the factors influencing Chinese university students' sustained use of generative AI, integrating the Technology Acceptance Model (TAM) and Information Success (IS) Model. Understanding these drivers is crucial for effective AI integration in educational pedagogy.
Key Insights for Enterprise AI Adoption in Education
Understanding user behavior and system effectiveness is paramount for successful AI deployment in higher education. This study provides critical data for educators, administrators, and AI developers.
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Continuance Intention Explained (R²)
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Perceived Usefulness Explained (R²)
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Attitude Towards AI Explained (R²)
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Chinese University Students Surveyed
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Impact of Generative AI System Quality on Users
The quality characteristics of AI systems directly influence user experience and future intention. This section details how Information Quality (IQ), System Quality (SYQ), and Service Quality (SEQ) interact with user perceptions.
Significant
Information Quality (IQ) Boosts Perceived Usefulness & Ease of Use
Information Quality (IQ), encompassing accuracy, relevance, and consistency, significantly drives both the perceived usefulness (PU) and perceived ease of use (PEOU) of generative AI systems among students. High-quality information directly enhances user perception and intent, indicating a critical area for AI development focus.
| Factor | Impact on Perceived Usefulness (PU) | Impact on Perceived Ease of Use (PEOU) | Supported Hypotheses |
|---|---|---|---|
| Information Quality (IQ) | Positive (β=0.331, p<0.001) | Positive (β=0.412, p<0.001) |
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| System Quality (SYQ) | No Direct Impact (β=-0.032, p=0.513) | Positive (β=0.252, p<0.001) |
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| Service Quality (SEQ) | No Direct Impact (β=0.101, p=0.052) | No Direct Impact (β=0.047, p=0.342) |
Key Takeaway: While Information Quality (IQ) directly enhances both perceived usefulness and ease of use, System Quality (SYQ) primarily influences ease of use. Service Quality (SEQ) did not show a direct significant impact on either PU or PEOU in this study, suggesting context-dependent factors and a need for further research into specific service-related features in educational AI.
Pathways to Sustained AI Use in Education
This section outlines how students' perceptions of usefulness and ease of use, as well as their general attitude, directly shape their intention to continue using generative AI.
Enterprise Process Flow: TAM Pathway to Continuance Intention
Perceived Ease of Use (PEOU)
→
Perceived Usefulness (PU)
→
Attitude (ATU)
→
Continuance Intention (CI)
The core TAM constructs show a clear pathway: a system perceived as easy to use boosts its perceived usefulness (H5a: β=0.336, p<0.001), which in turn fosters positive attitudes (H4a: β=0.427, p<0.001), ultimately leading to a higher continuance intention to use generative AI (H4b: β=0.426, p<0.001, H6: β=0.239, p<0.01).
Enhancing AI Adoption through User-Centric Design
Enterprises deploying AI in educational contexts can significantly boost adoption and sustained use by focusing on user perceptions. This study confirms that when generative AI is perceived as useful and easy to use, students develop more positive attitudes, which directly influences their continued engagement. Implementing robust information quality checks, intuitive user interfaces, and curriculum-relevant features are critical for long-term AI integration. For example, by ensuring AI provides highly accurate and relevant content that is simple to access, universities can foster a more positive learning environment and higher student retention of AI tools.
Critical Moderating Factors & Model Robustness
Beyond direct influences, this research also explored external factors like gender and assessed the overall fit and predictive power of the integrated model.
Gender Moderation
Gender Negatively Moderates Usefulness-Intention Link
Gender was found to negatively moderate the relationship between Perceived Usefulness (PU) and Continuance Intention (CI) (H7a: β=-0.279, p<0.01). Specifically, the effect of PU on CI is less pronounced in males than in females, implying that other factors might influence males' continuance intention, or that females are more directly influenced by perceived usefulness in their decision to continue using generative AI.
Model Fit & Predictive Power
The integrated TAM and IS model demonstrated strong statistical validity and moderate predictive power for key behavioral aspects:
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SRMR (Good Fit <0.08)
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NFI (Good Fit >0.8)
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Max VIF (No Multicollinearity)
The model exhibited good discriminant validity and reliability, with R² values indicating moderate explanatory power (CI=31.5%, PU=34.7%, ATU=37.5%). While robust, there's always room for future research to incorporate additional AI characteristics for enhanced predictive insights.
Calculate Your Potential AI Impact
Estimate the time and cost savings your institution could realize by effectively integrating generative AI, based on our research findings.
Estimated Annual Cost Savings
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Your Strategic AI Implementation Roadmap
Leverage insights from leading research to build a robust AI strategy within your organization. Here's a typical phased approach:
Phase 1: Discovery & Assessment (1-2 Months)
Conduct a comprehensive audit of existing educational processes, identify pain points, and define specific goals for AI integration. Assess current technology infrastructure and data readiness. Engage key stakeholders (faculty, students, IT) to gather requirements and build consensus.
Phase 2: Pilot & Validation (2-4 Months)
Implement generative AI solutions in a controlled environment with a select group of users (e.g., a specific department or course). Focus on validating the impact of Information Quality and Perceived Usefulness. Collect feedback, measure key metrics, and refine system parameters based on initial findings to maximize acceptance.
Phase 3: Scaled Deployment & Training (3-6 Months)
Roll out AI solutions across broader segments of the institution. Develop comprehensive training programs for faculty and students, emphasizing effective prompt engineering and ethical AI use. Continuously monitor system performance and user adoption, adjusting strategies as needed based on ongoing data analysis, especially considering factors like gender moderation.
Phase 4: Optimization & Future Vision (Ongoing)
Establish continuous improvement loops for AI systems, integrating the latest research on anthropomorphic AI characteristics and evolving user needs. Explore advanced applications and new AI models. Regularly evaluate the long-term sustainability and impact of AI on educational outcomes and student continuance intention.
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