## 5 Conclusions  
This research not only analyzes the effectiveness of chatbots in educational contexts but also proposes an innovative methodology by employing the confrontation between chatbots. This approach allows for a deeper comparison of their responses, something that has not been extensively explored in previous studies. Integrating quantitative and qualitative processes has yielded a rich and diverse methodology, enabling a thorough and detailed analysis of the results. This integration has facilitated important evidence-based decisions, such as transitioning from GPT-3.5 for quantitative analysis to GPT-4 for qualitative analysis. Moreover, Chatbots 4.0 are significantly more reliable than Chatbots 3.5. Additionally, Chatbots 4.0 maintain more consistent stances throughout conversations, which is much more desirable than the overly accommodating stances of the 3.5 versions. The quantitative analysis of ChatGPT's performance in solving statistics exercises reveals several key findings. Calculation errors constitute the majority, comprising 65% of total errors as shown in Figure 2, with cloze-type questions exhibiting the highest error rates exceeding 70% as shown in Figure 3. Across different statistical fields, probability, and inferential statistics show the highest error rates, surpassing 70% as shown in Figure 4. Sub-fields such as binomial/hypergeometric and continuous random variables demonstrate the highest probability of error, exceeding 70%, while descriptive statistics present the least errors, below 25% as shown in Figure 5. Furthermore, error rates vary according to the type of exercise, with logical and numerical exercises having around 65% error rates, while theoretical exercises remain below 6%, as shown in Figure 6.  
Qualitative research with GGT consistently compares data within the data collection process. To achieve this, two considerations must be taken into account. Firstly, specific criteria regarding the selection of chatbots for use in semi-structured interviews must be considered. Secondly, the interview approach, including the selection of questions, should consider the absence of pre-established opinions and criteria. Another conclusion drawn from the data collection arose from the need to challenge the chatbots on their positions and results, further enriching the interviews. The confrontation is a result that arises in this research through the constant comparison of data obtained from interviews conducted with chatbots using the GT methodology. These interviews, which are semi-structured, allow questions to be formulated based on the interviewee's (in this case, the chatbot's) responses. It is from this approach that the idea of comparing the responses between different chatbots emerged. This could be applied not only in the educational field but also in various other sectors, enabling comparative analysis and the development of innovative solutions in areas such as customer service, scientific research, process automation, and more. The data analysis arises from the five concepts of basic coding and their eight patterns of core category. Chatbots offer a rigorous way to present calculation processes and results, which can match or even surpass the quality of those performed by meticulous statistics educators when preparing study materials. Moreover, all solutions follow a structured order, starting with the enumeration of data, followed by the application of formulas and, finally, the solution. It is important to highlight that in a few instances, though they are notable, chatbots can maintain entirely inflexible positions even when they are wrong, leading to responses that can be amusing and arrogant. Furthermore, using the Wolfram Alpha plugin is emphasized for its ability to overcome certain inherent limitations in the calculation process of ChatGPT 4.0. It is essential to highlight the lessons learned, which were categorized into three groups: academia, students, and teachers.