## 5 Discussion and Conclusions
### Practical Implications  
This study, taking into account advancements in LLMs like ChatGPT, presents several practical implications. Our research highlights the need for AI systems to go beyond merely reacting to explicit student requests and to engage proactively during both the search and evaluation phases to provide genuinely personalized support. First, in the course-search phase, AI-based CRSs could benefit from offering a chat-based interactive platform to assist students. Rather than simply presenting a list of courses, the system could engage in conversational interactions to elucidate course topics, therefore aiding students in autonomously discovering courses that align with their interests. Through these narrative-driven recommendations [17], the AI would serve an assistive role by guiding students through sifting through and rectifying misinformation, highlighting crucial aspects of courses, and summarizing key information, thus empowering students to navigate their course search autonomously. Second, during the evaluation phase, the AI-based CRS should take on a more leading role, prioritizing explainability. Providing transparent and comprehensible explanations throughout the AI-assisted decision-making process can mitigate uncertainties and bolster confidence in the system [47]. For instance, in the scenario involving a Career interest-based CRS, the AI could explain to students: _\"This course is recommended because it aligns with your career interests in machine learning engineering, which could significantly benefit your future career path based on your current academic and extracurricular profile\"_. As students interact with the AI-based CRS's reasoning during evaluations, they will have the chance to develop their own perspectives and improve their capacity to assess and select courses autonomously [48]. Thirdly, AI-based CRSs should provide unexpected or serendipitous course suggestions. This strategy tackles the filter bubble problem by urging students, especially those uncertain about their academic path, to venture into new domains [48]. These recommendations have the potential to expand students' perspectives, exposing them to unfamiliar subjects and revealing interests they may not have previously considered. Future research endeavors should strive to identify the key characteristics of serendipitous recommendations and assess the capacity of LLMs to deliver such suggestions reliably and precisely, avoiding the propagation of misleading or erroneous information. There are several limitations that should be considered when interpreting our findings. First, the outcomes may vary based on participants' socio-demographic factors, such as their university affiliations, countries of residence, or other specific conditions. Future research should explore how these factors influence students' preferences and expectations regarding AI-based CRSs. Additionally, characteristics of the courses themselves, such as the academic domain, might affect the results. For instance, further studies could examine how CRS designs need to be customized for different fields, such as Engineering versus Medicine, to enhance their applicability and effectiveness. Second, while this research encompassed various types of AI-based CRS, further exploration into additional types of systems could provide a more comprehensive understanding of how students navigate their course-selection decisions. Thirdly, although speed dating with storyboards proved effective in assessing user experience, it may not fully capture the intricacies of real-time interactions with AI-based CRSs in authentic educational environments. Therefore, future research endeavors should focus on developing and empirically testing genuine AI-based CRS models. Lastly, this study does not address the evolution of students' expectations regarding AI-based CRSs over time. As students become more accustomed to interacting with AI in their course-selection process, their expectations and reliance on AI may change.