## 3 Results
### Qualitative Stage
This process resulted in five fundamental concepts, as observed in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Question Number** & **Question** & **Iterations** & **Qty ChatGPT** & **Qty Bing** \\
**Quantity** & **Quantity** & **Quantity** & **4 + Wolfram** & **Chatbot** \\ \hline
1, 12, 17, 19, 21, 23, 24, 26, 30, 31, 32, 37, 41, 42, 48, 52, 53 & 17 & 2 & 1 & 1 \\
15 & & & & & \\
7, 25, 34, 39 & 5 & 3 & 1 & 2 \\
8, 10 & 2 &  
Within the GGT and in the pursuit of the core category, the analysis of interviews conducted with the chatbots was further explored, considering the classification of questions into the previously obtained five concepts of basic coding, aiming to identify education-related patterns. The patterns observed in the chatbot's responses to questions classified under the concepts of \"Correct\", \"They match\", \"Opposites\", \"Differing viewpoint\", and \"Bing Error\" offer several significant implications for education. The following paragraphs detail these patterns:  
1. C1, labeled as Correct, corresponded to the scenario where both chatbot responses were equal to the response of the human expert (correct answer). This indicates consistency and precision in the responses provided by the chatbots, reflecting their ability to interpret and solve statistical questions correctly, as well as a good understanding of problem statements. 2. C2, labeled They match, occurred when discrepancies arose between the chatbot responses. However, after a process of clarification or revision, the chatbots agreed on the solution in a subsequent iteration. This suggests that, although the chatbots may have initially different interpretations or make errors in their initial responses, they can reach a consensus after reviewing the information or receiving additional feedback. This underscores the importance of \"confrontation\" between chatbots in the problem-solving process, as well as their capacity for adaptation and learning to improve the accuracy of their responses. 3. C3, labeled Opposites, occurred when the chatbots interpreted the problem statement differently and provided different solutions. After a review, the chatbots failed to \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Basic Coding** & **Concept** & **Quantity** & **Condition** & **Question Number** \\ \hline C1 & Correct & 17 & No confrontation & 1, 12, 17, 19, 21, 23, 24, 26, 30, 31, 32, 37, 41, 42, 48, 52, 53 \\ C2 & They match & 8 & Confrontation & 8, 18, 25, 29, 33, 35, 36, 39 \\ C3 & Opposites & 6 & Confrontation & 3, 7, 10, 15, 20, 22 \\ C4 & Differing viewpoint & 2 & Confrontation & 16, 34 \\ C5 & Bing Error & 3 & Confrontation & 5, 9, 11 \\ \hline \multicolumn{3}{c}{Total} & 36 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Basic coding in five conditions. Figure 9: Example question 25 (English translation). reach an agreement on the solution. This demonstrates discrepancies arising from calculation errors or the application of different mathematical theories or formulas. 4. C4, labeled Differing viewpoint, was similar to the previous scenario. However, due to the dynamics of the interview, the researcher decided to request the chatbot to improve the original statement for better clarity of the problem posed. 5. C5, labeled Bing Error, involved an error that the chatbot did not recognize and maintained its erroneous position throughout the iterations. This concept only involved the Bing chatbot; ChatGPT 4.0 + Wolfram did not present this error. Due to the relevance of this mistake, an example of the Bing error is shown below:  
Figure 10 illustrates that on some occasions, Bing maintains an intransigent stance despite the researcher's efforts to point out its errors, reaching a point where the chatbot contradicts itself. In addition to displaying an arrogant attitude, this creates a humorous and, to some degree, concerning situation. * Learning Tools: This pattern underscores the importance of employing technological tools capable of delivering precise and reliable solutions to well-defined problems.