## 4 Results
### Mental Workload Evaluation  
Mental models were evaluated separately for both tasks (categorization and sentiment analysis) using the NASA-RTLX. An overview of the resulting values (sum values of six subscales) by tools and tasks is presented in Figure 9. It is visible that users reported a higher mental workload when using the Taguette tool in both tasks. The mean NASA-RTLX \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Sample 1-Sample 2** & **Test Statistic** & **Std. Error** & **Std. Test Statistic** & **Sig.** \\ \hline \multicolumn{5}{c}{Understanding subscale} \\ \hline Gemini-Taguette & 1.752 & 11.086 & 0.158 & 0.874 \\ Gemini-ChatGPT & 28.733 & 11.052 & 2.600 & **0.009** \\ Taguette-ChatGPT & \(-\)26.981 & 11.119 & \(-\)2.427 & **0.015** \\ \hline \multicolumn{5}{c}{Performance subscale} \\ \hline Gemini-ChatGPT & 5.107 & 11.116 & 0.459 & 0.646 \\ Gemini-Taguette & 22.630 & 11.150 & 2.030 & **0.042** \\ ChatGPT-Taguette & 17.524 & 11.183 & 1.567 & 0.117 \\ \hline \hline \end{tabular} _Note: Bold values indicate statistical significance at the 0.05 level._  
\end{table} 
Table 7: Pairwise comparison of TOAST scores for tools with Dunn’s Test. Figure 8: TOAST values for Taguette, ChatGPT and Gemini. value for Taguette was \(57\) (\(min=17\), \(max=91\)) for the first task and \(49\) (\(min=7\), \(max=91\)) for the second. Meanwhile, the mean value for other tools was between \(26\) and \(32\) for both tasks (\(min=6\), \(max=92\)). The mean values for the tools remained quite consistent between the first and second tasks. The difference between them for observed tools was further confirmed with a Kruskal-Wallis H test. It showed a statistically significant difference in the NASA-RTLX score between the different tools used for the UX evaluation on both tasks. For the first task, this was confirmed with \(\chi^{2}(2)=36.607,\ p<0.001\), with a mean NASA-RTLX score of \(69.99\) for ChatGPT, \(103.20\) for Gemini and \(189.16\) for Taguette. The effect size, calculated using eta-squared \(\eta^{2}\) was \(0.136\), indicating a small effect. And for the second with \(\chi^{2}(2)=81.373,\ p<0.001\), with a mean NASA-RTLX score of \(114.18\) for ChatGPT, \(99.90\) for Gemini, and \(164.90\) for Taguette. The effect size for the second task was very large (\(\eta^{2}=0.319\)), indicating a substantial difference in perceived workload across the three systems. Additional pairwise comparison, conducted with Dunn's Test indicated a clear difference between the AI and non-AI tools. For both tasks, the difference was statistically significant in comparison of ChatGPT and Taguette (\(p_{\textit{Task1}}<0.001\), \(p_{\textit{Task2}}<0.001\)) and in comparison of Gemini and Taguette (\(p_{\textit{Task1}}<0.001\), \(p_{\textit{Task2}}<0.001\)). The results of Dunn's Test are presented in Table 8.  
The detailed overview of the subscale values is presented in Table 9. Taguette consistently shows higher mental demand compared to the AI tools, especially in the first task (\(\overline{x}_{\textit{Taguette1}}=8.84,\overline{x}_{\textit{ChatGPT1}}=2.24, \overline{x}_{\textit{Gemini1}}=3.07\)). Both ChatGPT and Gemini present significantly lower mental demands, with ChatGPT reaching the lowest mean value for the first and Gemini for the second task (\(\overline{x}_{\textit{Gemini2}}=3.52\)). With the use of Taguette users experienced higher temporal demands, particularly in the second task, suggesting that AI tools helped