## 4 Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Cronbach’s Alpha** & **Composite Reliability** & **Average Variance Extracted (AVE)** \\ \hline BI & 0.906 & 0.908 & 0.843 \\ EE & 0.882 & 0.889 & 0.738 \\ FC & 0.800 & 0.923 & 0.618 \\ PE & 0.877 & 0.884 & 0.730 \\ SI & 0.930 & 0.936 & 0.877 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Internal consistency. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **BI** & **EE** & **FC** & **PE** & **SI** & **UB** \\ \hline BI & 0.918 & & & & & \\ EE & 0.580 & 0.859 & & & & \\ FC & 0.511 & 0.662 & 0.786 & & & \\ PE & 0.757 & 0.550 & 0.543 & 0.854 & & \\ SI & 0.483 & 0.381 & 0.283 & 0.584 & 0.937 & \\ UB & 0.613 & 0.498 & 0.432 & 0.534 & 0.359 & 1.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Cornell-Larcker criterion. In this case, all diagonal values exceed the correlations with other constructs, indicating adequate discriminant validity. In particular, behavioral intention (BI) presents a diagonal value of 0.918, which is higher than its correlations with the other variables, with the highest correlation being with performance expectancy (PE) (0.757). Similarly, effort expectancy (EE) has a square root of AVE of 0.859, with moderate correlations with the other variables, particularly its relationship with facilitating conditions (FC) (0.662). Likewise, the social influence (SI) construct exhibits the highest level of independence (0.937), with relatively low correlations with the other dimensions. Once the measurement model was validated, we proceeded to analyze the structural model to test the hypothesized relationships between constructs using structural equation modeling (PLS-SEM) (Figure 1). This analysis examines the strength and significance of path coefficients, allowing for us to evaluate the model's predictive capability. The most relevant result is the strong influence of performance expectancy (PE) on behavioral intention (BI), with a coefficient of 0.596, suggesting that students perceive the usefulness of AI as a key factor in its adoption. Similarly, behavioral intention (BI) has a significant impact on actual use (UB), with a coefficient of 0.530, confirming that intention is a direct predictor of usage behavior. Effort expectancy (EE) also shows a positive relationship with behavioral intention (BI) (0.214), although to a lesser extent than performance expectancy. On the other hand, social influence (SI) and facilitating conditions (FC) have a much lower impact on behavioral intention (0.044 and 0.034, respectively), suggesting that these factors are not primary determinants in students' intention to adopt AI. However, facilitating conditions (FC) exhibit a more relevant effect on actual use (UB) (0.161), indicating that access to resources and technical support may influence the effective adoption of AI in the academic environment. Additionally, the model explains 64.4% of the variance in behavioral intention (BI) and 39.5% of the variance in actual use (UB), indicating a moderate-to-high explanatory power in predicting AI adoption in the university context. To evaluate the statistical significance of the path coefficients, the bootstrapping method with 5000 resamples was applied (Table 6). Path coefficients (\(\beta\)) represent the strength and direction of the relationships between UTAUT2 model constructs, while the  
Figure 1: Structural model. \(t\)-values and \(p\)-values indicate the statistical significance of these relationships. A higher \(\beta\) coefficient suggests a stronger influence of the predictor variable on the outcome variable. The results confirm that most path coefficients are statistically significant, as their \(p\)-values are below 0.05. In particular, the strong influence of performance expectancy (PE) on behavioral intention (BI) stands out, with a coefficient of 0.596 and a \(t\)-value of 5.999, indicating a robust and highly significant relationship (\(p\) = 0.000).