## 3 Results
Finally, a bivariate correlation analysis was conducted between the subscales of perceptions of AI use in higher education and variables such as age, frequency or AI use, and AI experience. The analysis revealed significant relationships between age and both attitudes \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Subscale** & **Students** & \multicolumn{2}{c}{**Teachers**} & \multicolumn{2}{c}{**Administatives**} & **F(2427)** & \(p\) & \(\mathbf{\eta^{2}}\) \\ \cline{2-10}  & **M** & **DE** & **M** & **DE** & **M** & **DE** & & & \\ \hline Attitudes and perceptions & 3.92 \({}^{\text{a}}\) & 0.78 & 3.65 \({}^{\text{a}}\) & 0.79 & 3.81 & 0.91 & 3.87 & 0.022 \({}^{\text{*}}\) & 0.018 \\ Role of AI in the teaching-learning process & 3.67 & 0.79 & 3.50 & 0.70 & 3.78 & 0.73 & 2.47 & 0.086 & 0.011 \\ Ethical and social implications & 4.18 & 0.60 & 4.35 & 0.64 & 4.31 & 0.61 & 2.90 & 0.056 & 0.013 \\ Future role of AI & 4.11 & 0.55 & 4.08 & 0.56 & 4.14 & 4.05 & 0.712 & 0.755 & 0.001 \\ \hline \hline \end{tabular} Note. \({}^{\text{*}}\)\(p\) < 0.05, measures sharing \({}^{\text{a}}\) supscript indicate significant differences.  
\end{table} 
Table 6: Differences in perceptions of AI use in higher education by current occupation. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline
**Subscale** & \begin{tabular}{c} **Art, Design \&** \\ **Communication** \\ \end{tabular} & \begin{tabular}{c} **Health** \\ **Sciences** \\ \end{tabular} & \
\begin{tabular}{c} **Business and** \\ **Hospitality** \\ \end{tabular} & **Social Sciences** &
\begin{tabular}{c} **Engineering** \\ **and Information** \\ **Technology** \\ \end{tabular} & **F(2425)** & \(p\)** & \(\mathbf{\eta^{2}}\) \\ \cline{2-10}  & **M** & **DE** & **M** & **DE** & **M** & **DE** & **M** & **DE** & & \\ \hline Attitudes and perceptions & 3.93 & 0.70 & 3.86 & 0.73 & 3.86 & 0.83 & 3.72 & 0.80 & 3.94 & 0.92 & 1.02 & 0.397 & 0.009 \\ Role of AI in the teaching-learning process & 3.56 & 0.79 & 3.65 & 0.72 & 3.67 & 0.77 & 3.59 & 0.77 & 3.75 & 0.80 & 0.85 & 0.495 & 0.008 \\ Ethical and social implications & 4.37 & 0.61 & 4.20 & 0.59 & 4.20 & 0.51 & 4.30 & 0.61 & 4.11 & 0.70 & 2.19 & 0.069 & 0.020 \\ Future role of AI & 4.11 & 0.51 & 4.13 & 0.53 & 4.09 & 0.57 & 3.99 & 0.62 & 4.15 & 0.61 & 1.05 & 0.383 & 0.010 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Differences in perceptions of AI use in higher education by disciplinary area. and perceptions (r = \(-0.10\), \(p\) = 0.4) and ethical and social implications (r = \(-0.10\), \(p\) = 0.03), indicating that higher scores on these subscales are associated with younger participants. Significant correlations were also found between AI experience and the subscales of attitudes and perceptions (r = 0.31; \(p\) < 0.001), the role of AI in the teaching-learning process (r = 0.26, \(p\) < 0.001), and the role of AI in the future (r = 0.19, \(p\) < 0.001), showing that greater experience with AI is linked to higher scores on these subscales. Additionally, a negative relationship was observed between AI experience and age (r = \(-0.20\), \(p\) < 0.001), indicating that younger participants tend to have more AI experience. The frequency of AI use was positively correlated with attitudes and perceptions (r = 0.40, \(p\) < 0.001), the role or AI in the teaching-learning process (r = 0.35, \(p\) < 0.001), and the role of AI in the future (r = 0.31, \(p\) < 0.001), suggesting that higher scores on these subscales are associated with more frequent AI use. However, a negative correlation was found between AI experience and frequency of use (r = \(-0.60\), \(p\) < 0.001), indicating that greater AI experience is associated with less frequent use of AI.