The Use of Generative Artificial Intelligence for Upper Secondary Mathematics Education Through the Lens of Technology Acceptance

TL;DR

The paper addresses how upper-secondary students perceive GenAI in mathematics, using an extended Technology Acceptance Model (TAM) that includes Perceived Usefulness ($PU$), Perceived Ease of Use ($PEOU$), Perceived Enjoyment ($PE$), Intention to Use ($ITU$), and Compatibility ($COM$). Using data from Finnish students and a Hong Kong benchmark, the authors apply PLS-SEM with bootstrapping to show that $PU$ is the strongest predictor of $ITU$ in Finland, with $PE$ robustly shaping $PU$ and $PEOU$, while $PEOU$ has a weaker direct effect on $ITU$. Extending the model with $COM$ improves explanatory power for $PU$ and $PEOU$, though its direct impact on $ITU$ is limited; cross-cultural differences reveal that intrinsic motivation dominates adoption in Hong Kong, whereas practical usefulness drives adoption in Finland. The findings inform educators and policymakers about tailoring GenAI integration in mathematics education, considering cultural context and the alignment of GenAI tools with students’ prior digital experiences and needs.

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.

Figures (2)

• Figure 1: Structural Equation Model (SEM) for the adoption of Generative AI in upper secondary mathematics education based on the Technology Acceptance Model (TAM).
• Figure 2: Extended Structural Equation Model (SEM) incorporating Compatibility as an additional factor in the Technology Acceptance Model (TAM).