# Artificial Intelligence (ChatGPT) and Bloom's Taxonomy in Theoretical Computer Science Education
## 4 Research and Methods
### Quantitative Analysis--Phase 1  
The standard data analysis of ET 1 was performed using the JASP tool including ANOVA analysis with additional Post Hoc tests. **Hypothesis 1**. : The overall results of the educational performance of AI and students are equal. ANOVA of overall results in ET 1 are stated in Table 2. Descriptives of overall results in ET 1 are stated in Table 3.  
Descriptive plots of overall results in ET 1 are shown in Figure 2. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Cases** & **Sum of Squares** & **Df** & **Mean Square** & **F** & \(p\) & \(\eta^{2}\) \\ \hline Residuals & 849.567 & 43 & 19.757 & & \\ \hline \hline \end{tabular} Note. subjectPrec factor has the values HU\({}^{\circ}\)X\({}^{\circ}\), notation refers to students’ grades in previous 7 ZAIN course (A-D, A = excellent and D = satisfactory), GPT3 and GPT4, ChatGPT ver. 3.5 and 4. ANOVA test values for ET1 score.  
\end{table} 
Table 2: ANOVA—overall results in ET1. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**95\% CI for Mean Difference**} \\ \cline{3-10}  & \multicolumn{2}{c}{**Mean Difference**} & \multicolumn{1}{c}{**Lower**} & \multicolumn{1}{c}{**Upper**} & \multicolumn{1}{c}{**SE**} & \multicolumn{1}{c}{**T**} & \multicolumn{1}{c}{\(\boldsymbol{P_{\text{Tukey}}}\)} & \multicolumn{1}{c}{\(\boldsymbol{p_{\text{bonf}}}\)} \\ \hline GPT3 & GPT4 & \(-2.200\) & \(-8.128\) & \(3.728\) & \(1.988\) & \(-1.107\) & \(0.876\) & \(1.000\) & *** \\  & HUA & \(-11.833\) & \(-17.245\) & \(-6.422\) & \(1.815\) & \(-6.521\) & \(<\)0.001 & *** & \(<\)0.001 & *** \\  & HUB & \(-8.100\) & \(-15.360\) & \(-0.840\) & \(2.435\) & \(-3.327\) & \(0.021\) & * & \(0.027\) & * \\  & HUC & \(-6.733\) & \(-13.578\) & \(0.111\) & \(2.295\) & \(-2.933\) & \(0.056\) & \(0.080\) & \\  & HUD & \(-5.900\) & \(-14.625\) & \(2.825\) & \(2.926\) & \(-2.016\) & \(0.350\) & & \(0.750\) & \\ \hline GPT4 & HUA & \(-9.633\) & \(-15.045\) & \(-4.222\) & \(1.815\) & \(-5.309\) & \(<\)0.001 & *** & \(<\)0.001 & *** \\  & HUB & \(-5.900\) & \(-13.160\) & \(1.360\) & \(2.435\) & \(-2.423\) & \(0.171\) & & \(0.295\) \\  & HUC & \(-4.533\) & \(-11.378\) & \(2.311\) & \(2.295\) & \(-1.975\) & \(0.373\) & & \(0.821\) \\  & HUD & \(-3.700\) & \(-12.425\) & \(5.025\) & \(2.926\) & \(-1.265\) & \(0.802\) & & \(1.000\) \\ \hline HUA & HUB & \(3.733\) & \(-3.111\) & \(10.578\) & \(2.295\) & \(1.626\) & \(0.586\) & & \(1.000\) \\  & HUC & \(5.100\) & \(-1.303\) & \(11.503\) & \(2.147\) & \(2.375\) & \(0.188\) & & \(0.331\) \\  & HUD & \(5.933\) & \(-2.450\) & \(14.316\) & \(2.811\) & \(2.111\) & \(0.301\) & \(0.610\) \\ \hline HUB & HUC & \(1.367\) & \(-6.660\) & \(9.393\) & \(2.692\) & \(0.508\) & \(0.996\) & \(1.000\) \\  & HUD & \(2.200\) & \(-7.480\) & \(11.880\) & \(3.246\) & \(0.678\) & \(0.983\) & \(1.000\) \\ \hline HUC & HUD & \(0.833\) & \(-8.539\) & \(10.206\) & \(3.143\) & \(0.265\) & \(1.000\) & \(1.000\) \\ \hline \hline \end{tabular}  
* * \(p<0.05\), *** \(p<0.001\). Note. Post hoc tests (\(p\)-value for Tukey test = \(p_{\text{Tukey}}\)) and (\(p\)-value Bonferroni correction = \(p_{\text{bonf}}\)), Response variable: ET1 test score, factor values: HU”X” notation means students’ grades in previous 72 AIN course (A-D; A = excellent and D = satisfactory), GPT3 and GPT4, ChatGPT ver. 3.5 and 4. Descriptives for ET1 score.  
\end{table} 
Table 4: Post Hoc Tests of overall results in ET 1. Figure 2: Descriptives plots of overall results in the ET 1. (**a**) Box plot, (**b**) raincloud plot. Based on the ANOVA and Post hoc tests, we can reject the main hypothesis 1. The overall results in ET 1 in precise categories (HUA, HUB, HUC, HUD, GPT3, GPT4) differ. The Post hoc tests show statistically significant differences, especially between group **HUA (students with grade A) and both GPT3 and GPT4 bots**. An additional difference can be observed between **HUB (students with grade B) and GPT3 bot**. The plots also reveal that the overall results are better between humans of all categories compared to both GPT 3 and GPT4.