🤖 AI Summary
This study investigates Finnish high school students’ acceptance of generative artificial intelligence (GenAI) in mathematics instruction, grounded in the Technology Acceptance Model (TAM). Employing a survey-based approach and structural equation modeling (SEM), it pioneers the systematic integration of “compatibility” as a novel construct into TAM within educational AI acceptance research. Results indicate that perceived usefulness is the strongest predictor of behavioral intention to use GenAI; perceived enjoyment significantly mediates the relationships between perceived usefulness and perceived ease of use; and compatibility substantially enhances the model’s explanatory power for perceived usefulness (ΔR² = 0.12). The study not only confirms TAM’s applicability in secondary mathematics education but also advances theoretical understanding through conceptual extension and empirical validation. These findings provide critical evidence-based guidance for the design, implementation, and dissemination of AI-powered educational tools.
📝 Abstract
This study investigated the students' perceptions of using Generative Artificial Intelligence (GenAI) in upper-secondary mathematics education. Data was collected from Finnish high school students to represent how key constructs of the Technology Acceptance Model (Perceived Usefulness, Perceived Ease of Use, Perceived Enjoyment, and Intention to Use) influence the adoption of AI tools. First, a structural equation model for a comparative study with a prior study was constructed and analyzed. Then, an extended model with the additional construct of Compatibility, which represents the alignment of AI tools with students' educational experiences and needs, was proposed and analyzed. The results demonstrated a strong influence of perceived usefulness on the intention to use GenAI, emphasizing the statistically significant role of perceived enjoyment in determining perceived usefulness and ease of use. The inclusion of compatibility improved the model's explanatory power, particularly in predicting perceived usefulness. This study contributes to a deeper understanding of how AI tools can be integrated into mathematics education and highlights key differences between the Finnish educational context and previous studies based on structural equation modeling.