Enterprise AI Analysis
The Use of Generative Artificial Intelligence for Upper Secondary Mathematics Education Through the Lens of Technology Acceptance
Unlocking the potential of GenAI in mathematics education requires understanding student perception and technology acceptance. This analysis dives into a Finnish case study, offering insights for strategic enterprise implementation.
Executive Impact: Quantifying AI's Potential
Measuring the readiness and impact of AI tools is crucial for successful integration. Our analysis reveals key metrics influenced by student acceptance and compatibility.
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ITU Variance Explained
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PU → ITU Path Coefficient
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PU Variance Explained
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Perceived Usefulness (PU) as Key Driver
80.4% Variance in Intention to Use (ITU) ExplainedThe model explained 80.4% of the variance in ITU, highlighting Perceived Usefulness as the strongest determinant for students' intention to use GenAI tools in upper secondary mathematics education.
Enterprise Process Flow
Finnish Educational Context
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Existing Digital Skills & Platforms
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GenAI Integration
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Improved Learning Experiences
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| Primary ITU Driver |
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| PE Influence on PU |
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| PE Influence on PEOU |
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| PEOU Impact on ITU |
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The Role of Compatibility in GenAI Adoption
Challenge: Integrating new AI tools into existing educational experiences and needs without disruption.
Solution: Introducing 'Compatibility' as a variable to measure alignment of AI tools with students' prior experiences, beliefs, and educational needs.
Outcome: Compatibility significantly predicted Perceived Usefulness (path coefficient 0.602, p < 0.001) and Perceived Enjoyment (path coefficient 0.808), enhancing the model's explanatory power for PU (R² from 0.609 to 0.732) and PE (R² from 0.511 to 0.521). While not directly influencing ITU, it indirectly strengthens adoption through usefulness and enjoyment.
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