## 5 Discussion and Conclusions  
In this study, we explored how AI-based CRSs influence students' course-selection process to identify the most effective support for their decision-making. In the search phase, students anticipated the AI-based CRS to retrieve, filter, and visualize course-related information to facilitate personalized course searches. In the evaluation phase, students expected the AI-based CRS to quantify difficult-to-evaluate qualitative information, including course suitability, learning value, and serendipity. These findings underscore the variability in students' interactions with AI-based CRSs based on the particular phase of their course-selection decision-making process. Interestingly, students anticipated a change in the role of the AI-based CRS corresponding to each phase of the course-selection decision-making process, namely the search and evaluation phases. For instance, research by [PERSON] and colleagues [45] highlights a personalized hybrid CRS designed to dynamically adapt to the diverse needs of students throughout their course-selection journey. During the search phase, students envisioned the AI-based CRS playing an assistive role in gathering course-related information. This limited assistive role arose from concerns regarding the AI's ability to fully grasp complex factors, such as academic interests and preferences, based solely on semiannual course history data. Students expressed skepticism about fully automated recommendations during this phase, featuring they might not accurately reflect their evolving academic contexts, reminiscent of the 'cold-start problem' in AI. Students believed that the search phase could be improved when the AI-based CRS assisted by retrieving, filtering, and visualizing relevant data from diverse, text-heavy sources, with students taking the lead in utilizing this information. Conversely, during the evaluation phase, students envisioned the AI-based CRS taking on a more prominent role, serving as a benchmark for quantitatively analyzing difficult-to-evaluate information. This expectation stemmed from their difficulty in objectively weighing the advantages and disadvantages of various courses, which often eroded their confidence. Research by [PERSON] and colleagues [46] suggests that individuals tend to lean more on AI recommendations when their confidence in decision-making wanes. Consequently, students who felt overwhelmed during the evaluation phase tended to rely more on AI suggestions, expecting the AI-based CRS to assume a primary role. Students believed that AI's quantified assessments would assist them in overcoming uncertainties in assessing course suitability, learning value, and serendipity. In summary, to effectively and efficiently support students in course selection, the AI-based CRS must adapt its role throughout the students' decision-making process, acting as an assistant during the search phase and as a leader in the evaluation phase. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**ID** & **Scenario Title** & **Preferred Students** & **Total** \\ \hline
1 & Community-based CRS & S1, S2, S3, S5, S7, S11, S13, S16, S22, S24 & 10 \\
2 & Academic interest-based CRS & S1, S7, S8, S12, S14, S15, S22, S23 & 8 \\
3 & Career interest-based CRS & S2, S3, S4, S5, S6, S11, S13, S14, S16, S18, S23 & 11 \\
4 & Grade prediction-based CRS & S1, S7, S10, S17, S18 & 5 \\
5 & Serendipity-based CRS & S6, S9, S10, S16, S19, S20, S21, S24 & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Students’ preferences for different types of AI-based CRS. As indicated in Table 5, the distribution of students' preferences shows no discernible pattern, reflecting the diverse backgrounds and unique needs of each student. This observation underscores the significance of recognizing individual variations in preferences and needs when providing personalized course recommendations rather than adopting a one-size-fits-all approach.