## 7 Conclusions  
In this study, a digital game-based AI chatbot was developed and quasi-experiments were conducted to evaluate the effectiveness of a digital game-based AI chatbot in an IT course. It was found that the digital game-based AI chatbot significantly improved learners' academic performance compared to the traditional AI chatbot. Second, in terms of higher-order thinking development, it was able to significantly improve learners' problem-solving tendencies and computational thinking compared to traditional AI chatbots, but there was no significant difference in terms of creativity. In addition, the digital game-based AI chatbot is significantly more effective than the traditional AI chatbot in improving learners' motivation and learning mind-flow. Moreover, the results of the learning behavior analysis of the experimental group showed that both the high and low subgroups actively interacted with the AI chatbot during the problem-solving process, which in turn facilitated the understanding of the knowledge points and improved the ability to ask questions. Therefore, the digital game-based AI chatbot developed in this study is effective when applied to an IT course. These findings offer valuable insights for educators and educational game designers. First, we encourage teachers to incorporate AI chatbots into their teaching practices. AI chatbots can provide immediate responses to students' questions, helping them overcome difficulties encountered during learning and offering personalized support and feedback. Second, AI chatbots should be deeply integrated with digital game-based learning strategies. Gamified design can stimulate students' exploratory spirit and willingness to engage in active learning. Moreover, this study found that unfolding instructional content through game narratives can better guide learners to achieve their learning objectives. Therefore, future educators should integrate clear learning tasks with story backgrounds when using AI chatbots to assist teaching. By combining narrative elements with real-world problems, educators can enhance student engagement and motivation. Finally, instruction should be learner-centered, taking into account students' cognitive levels and the behavioral differences exhibited in AI chatbot-supported digital game-based learning. This approach involves providing customized scaffolding resources to more effectively assist various types of students in achieving their learning goals. For high achievers, more challenging tasks and open-ended questions can be offered to encourage deeper thinking. Conversely, for low achievers, more specific task cues and engaging micro-videos can be provided to help them build a knowledge framework and enhance understanding. However, this study has certain limitations. First, the duration of the experiment was relatively short. Future research should collect more evidence to demonstrate the impact of AI chatbot-supported digital game-based learning on students' learning outcomes. Second, the sample size used in analyzing the learning behavior paths of the experimental group was relatively small, which might limit the generalizability and statistical power of our findings. Although we enhanced the reliability of the results through statistical methods such as the chi-square test, future studies should consider using larger sample sizes to validate our preliminary findings and increase the complexity of the analysis. Additionally, developing such educational games is time-consuming and requires a high level of systematic thinking from designers. Lastly, this study only explored the application of AI chatbots in digital game-based learning within the context of information technology. It remains to be seen whether this approach can be applied to other subjects.