## 5 Experimental Results
### Learning Behavior  
To further explore the learning processes of learners supported by AI chatbots in digital game-based learning environments, this study recorded learners' behaviors during gameplay and employed the behavior coding method proposed by [PERSON] et al. [2020]. The specific coding descriptions are detailed in Table 1. The experimental group was divided into high-achievement and low-achievement groups to investigate the learning processes of students with varying performance levels. This grouping helps us to understand the behavioral patterns of high and low achievers in AI chatbot-supported game-based learning, enabling more targeted educational interventions and support. The median pre-test score of 55 points was used as the grouping criterion, as the median is an effective dividing point that splits the dataset into two roughly equal parts, enhancing the stability and reliability of statistical analyses [61; 62]. Students scoring below 55 points were classified as the low-achievement group, comprising 21 students (scoring between 30 and 50 points), generating 589 behavior codes. Those scoring above 55 points were classified as the high-achievement group, consisting of 17 students (scoring between 55 and 75 points), generating 827 behavior codes. As this was the introduction of new content, students had not previously learned the relevant knowledge, resulting in generally low pre-test scores. Lag sequential analysis was employed to reveal the differences in behavioral patterns between the high-achievement and low-achievement groups in AI chatbot-supported digital game-based learning. Table 6 presents the frequencies and ratios of behaviors in both groups. Descriptive statistical analysis first showed that for the behavior of engaging in free dialogue with the AI chatbot (AF), the high-achievement group had a proportion of 18.9%, while the low-achievement group had a proportion of 14.1%. This indicates that both high and low achievers actively engaged in free dialogue with the AI chatbot, which could enhance learning motivation and efficiency for all students. Secondly, comparing the frequency of skipping videos (K), it was found that low-achievers more frequently chose to skip videos. Furthermore, in behaviors coded as submitting correct answers (C) and engaging in selective dialogue with the AI chatbot (AS), high achievers exhibited higher frequencies compared to low achievers. This reflects higher problem-solving efficiency and more precise information-filtering abilities in the high-achievement group. Finally, to determine whether there were significant differences in learning behaviors between high and low achievers under AI chatbot support, a chi-square analysis was conducted on the frequency of learning behaviors. The results showed \(X^{2}(10)=25.712\), \(p=0.004\), indicating significant differences in behavior distribution between the high- and low-achievement groups. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline  & **Group** & **N** & **Mean** & **Mean Rank** & **U** & **Z** & \(p\) \\ \hline learning achievement & CG & 39 & 3.57 & 37.32 & 675.5 & \(-6.7\) & 0.503 \\ (pre-test) & EG & 38 & 3.71 & 40.72 & & & \\ \hline learning achievement & CG & 39 & 3.64 & 33.32 & 519.5 & \(-2.27\) & 0.023 * \\ (post-test) & EG & 38 & 4.08 & 44.83 & & & \\ \hline \hline \end{tabular}
*\(p<0.05\)  
\end{table} 
Table 5: The Mann–Whitney U test results for the two groups’ flow experience. To further explore the statistical significance and underlying dynamics of the observed behavior frequencies, adjusted residual analysis was performed on the transition frequencies of behaviors between the high- and low-achievement groups. This analysis helps identify behavior transitions significantly higher or lower than expected, providing deeper insights into the behavioral differences between the two groups. Table 7 presents the residuals for the high-achievement group, with rows representing the initial behaviors and columns representing subsequent behaviors.