## 1 Introduction
By employing a mixed-methods approach, the research aims to assess these chatbots' effectiveness through quantitative analysis of numerical outcomes and qualitative insights gained from interviews conducted with the chatbots. The integration of artificial intelligence chatbots into education has been widely explored in recent literature. The study by [PERSON] et al. [14], for instance, offers a comprehensive review of the benefits and challenges of using chatbots in educational settings. Among the main advantages, they highlight personalized support and improved knowledge retention. However, they also identify difficulties such as the accuracy of responses, ethical concerns, and the lack of adequate training for educators. Although their research points out the great potential of these tools, it also emphasizes the barriers that must be overcome to achieve effective adoption. To achieve this, it was first aimed to assess how effectively ChatGPT could solve statistical problems by evaluating the accuracy of the responses. Subsequently, semi-structured interviews were conducted with a chatbot using qualitative data analysis techniques, primarily employing Grounded Theory (GT) [15; 16; 17], specifically the Glaserian version (GGT). An approach was embraced that enabled the analytical elements during interviews with the chatbots. This methodology resulted in compelling findings concerning the application of GGT for the qualitative analysis of the semi-structured interviews with the selected chatbots, devoid of predetermined categories or criteria imposed on the analysis. To categorize the qualitative process, a search was conducted for GT elements, as suggested by [PERSON] [18]. These elements include data collection, data analysis, basic coding, master core category, data constant comparative, and emerging theory. This approach allows a holistic exploration of the selected chatbots' responses, perceptions, and opinions, offering a more comprehensive and profound understanding of their interaction. Following the principles of GGT, concepts (basic coding) and emergent patterns (core category) were allowed to arise from the collected interview data rather than imposing a preconceived structure on the analysis. This approach laid a solid foundation for generating theories based on experiences with chatbots, exploring their effectiveness and the perceptions, attitudes, and challenges encountered while using various chatbots in educational contexts. This study represents a step forward in understanding the interaction between artificial intelligence, statistics education, and lessons learned from semi-structured interviews with chatbots. The findings from this mixed-method approach aim to provide valuable insights, informing future implementations of chatbots or other AI tools in education. It emphasizes the importance of considering numerical results and the lessons and learnings obtained from engaging with chatbots, their responses, errors, and differing outcomes and positions from other chatbots. It has enabled the formulation of several theories, from identifying the best chatbots suitable for engaging in semi-structured interviews to understanding the lessons educators can glean from data collection and qualitative analysis through GT. It also raises questions about the appropriateness of conducting semi-structured interviews with chatbots. This paper is structured as follows. The second section details the methodology, providing an in-depth overview of the research approach and techniques employed. The third section presents the Results, showcasing the findings. The fourth section develops the Discussion, offering a comprehensive analysis and interpretation of the findings. Finally, the paper concludes by summarizing the key insights and implications drawn from the study, providing a cohesive ending to the research exploration.