ENTERPRISE AI ANALYSIS
The Use of Generative Artificial Intelligence for Upper Secondary Mathematics Education Through the Lens of Technology Acceptance
This study explores how Finnish upper secondary students perceive Generative AI (GenAI) in mathematics education, extending the Technology Acceptance Model (TAM) with Compatibility. It found that Perceived Usefulness (PU) strongly predicts Intention to Use (ITU) GenAI, with Perceived Enjoyment (PE) significantly influencing both PU and Perceived Ease of Use (PEOU). Compatibility further enhances the model's explanatory power for PU. Cultural differences are highlighted, showing Finnish students prioritize practical benefits for academic success, while Hong Kong students (from a comparative study) valued intrinsic motivation more.
Executive Impact & Business Metrics
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Enhanced Usefulness Perception
The model explained 80.4% of the variance in Intention to Use (ITU) GenAI, primarily driven by Perceived Usefulness (PU).
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Enjoyment's Indirect Influence
Perceived Enjoyment (PE) had a strong influence on Perceived Usefulness (β = 0.652, p < .001), indicating that enjoyable experiences indirectly boost perceived utility.
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Compatibility Boost
Inclusion of Compatibility improved the model's explanatory power for Perceived Usefulness, increasing R² from 0.609 to 0.732 (a 13.2% increase).
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Finnish Student Focus
Perceived Usefulness (PU) had a strong and significant effect on Intention to Use (ITU) (β = 0.737, p < .001) in Finnish context, emphasizing practical benefits.
Deep Analysis & Enterprise Applications
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60.9%
Variance in Perceived Usefulness (PU) explained by PE and PEOU
GenAI Adoption Pathway in Finnish Education
Perceived Enjoyment (PE)
→
Perceived Ease of Use (PEOU)
→
Perceived Usefulness (PU)
→
Compatibility (COM)
→
Intention to Use (ITU)
| Factor | Finnish Context | Hong Kong Context |
|---|---|---|
| Primary ITU Driver | Perceived Usefulness (PU) | Intrinsic Motivation (IM, similar to PE) |
| PE's Influence on PU | Strong (β = 0.652, p < .001) | Weak (β = 0.091, p = 0.053) |
| PE's Influence on PEOU | Strong (β = 0.715, p < .001) | Small (β = 0.152, p = 0.001) |
| PEOU's Influence on ITU | Weak (non-significant) | No significant impact |
| Cultural/Academic Focus | Practical benefits, exam prep | Intrinsic motivation, diverse backgrounds |
Integration Challenges & Pedagogical Support
Ease of Use (PEOU) Challenges: While GenAI interfaces are technically efficient (comprehend mathematical language, generate expressions), students face challenges in guiding GenAI towards appropriate mathematical methods and tools. This indicates that ease of use is less about the interface and more about strategic interaction.
Learning vs. Problem Solving: A critical challenge is ensuring GenAI facilitates genuine learning and conceptual understanding, rather than merely providing quick answers. This requires teachers to actively support students in developing 'pedagogical prompting techniques' to optimize educational interactions.
Future Research Need: There is a need for further research to explore the long-term impact of GenAI on learning outcomes, especially for students with skill gaps or advanced mathematical needs, and to investigate multimodal approaches beyond text-to-text interactions.
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Implementation Roadmap
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Strategic Planning & Pilot Implementation
Conduct pilot programs in advanced mathematics courses, similar to the Finnish study, focusing on GenAI tools like Copilot. Prioritize integrating GenAI for practical benefits in problem-solving and conceptual understanding, aligning with Finnish students' preference for 'Perceived Usefulness'.
Curriculum Integration & Teacher Training
Develop curriculum modules that integrate GenAI for personalized feedback and as a knowledge base. Provide comprehensive teacher training on 'pedagogical prompting techniques' to guide students in effective GenAI interaction, ensuring learning outcomes are prioritized over mere answer generation.
Monitoring & Continuous Improvement
Establish metrics to monitor the impact of GenAI on student learning outcomes and 'Perceived Enjoyment'. Regularly assess 'Compatibility' with existing digital learning environments and student needs, using feedback to refine GenAI integration strategies and address ease-of-use challenges.
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