## 2 Materials and Methods  
Before conducting this experiment, several key assumptions were clearly defined to ensure the study's validity. These assumptions included the chatbots' ability to interpret and solve statistical problems in an educational context, the direct comparison between different versions of the chatbots (GPT-3.5 vs. GPT-4.0) on specific tasks, and the methodological structure, which was organized into two stages, quantitative and qualitative, to ensure a comprehensive analysis of the chatbots' performance. The methodology encompasses quantitative and qualitative stages, each encompassing data collection and subsequent analysis. The methodology is shown in Figure 1.  
The GGT is based on semi-structured interviews. In this case, it starts with questions that have been tested in previous evaluations with statistics students. These questions were developed and validated by expert professors in the subject at the university and have been used as evaluation material in a traditional statistics course over 5 or 6 semesters. The answers to these questions were provided by subject matter expert professors and were compared with the responses given by the chatbots. The quantitative data were gathered by capturing responses generated by ChatGPT 3.5 for statistical problems; the exercises were designed for undergraduate university engineering students. This process was iterated three times to ensure robust representation and minimize bias; in each iteration, the exercises were randomly presented to ChatGPT 3.5. We used 54 questions from the Moodle platform that served as the final exam for engineering students in a regular undergraduate statistics course. The questions were checked for integrity, including missing special characters, verifying formulas and tables, and other details. Finally, we confirmed, by evaluating its initial answers, that the ChatGPT understood the questions in the first review. Once the initial round was completed, 52 questions--with a total of 156 items--were chosen from the initial 54 questions (excluding 27 and 28); the process was repeated twice (three times in total). Each question was meticulously copied and pasted into a word processor document, including the questions, and their respective responses provided by the ChatBot, leaving a total of 36 questions that participated in the interviews. For the Quantitative analysis, the questions were categorized using three criteria:  
1. By their type (logical, numerical, or theoretical):  
* Logical: 1 question with 5 items. * Numerical: 32 questions with 122 items. * Theoretical: 19 questions with 29 items. 2. By the statistical field:  
* Calculated: 5 questions and 5 items. * Cloze: 21 questions with 98 items. * Drag and Drop: 1 question with 1 item. Figure 1: Mixed data analysis methodology with chatbots. * Matching: 7 questions with 34 items. * Multiple Choice: 17 questions with 17 items. * Numerical: 1 question with 1 item. * By the type of question within Moodle:
* Probability: 2 questions with 5 items. * Descriptive: 13 questions with 39 items. * Inferential: 22 questions with 83 items. * Mathematical statistics: 15 questions with 29 items. All questions were processed using an spreadsheet. A value of 1 was assigned if the response to an item was correct, and a value of 0 was assigned if the item was incorrect. Questions that had any error were selected for qualitative analysis. The qualitative stage (described in Section 3.2) was conducted using GGT [15; 19; 20], which involved GGT data collection through semi-structured interviews with selected chatbots, followed by GGT data analysis. The semi-structured interviews begin with queries seeking solutions to specific problems, followed by subsequent questions emerging organically from the chatbots' responses. These interviews aimed to avoid biases or preconceived ideas and, ideally, identify any recurrent patterns.