## 1 Introduction  
In higher education, university students' course-selection decision-making is an important process that has a profound impact on their curricular requirements, career opportunities, and intellectual growth [1; 2]. However, students often grapple with the challenge of navigating course listings, overwhelmed by the abundance of information related to each course, making it difficult to quantitatively assess which course aligns best with their needs and aspirations [3]. Although academic advisors can help students select appropriate courses, it is difficult to give this support to all students as it requires a lot of time and effort from the human advisor [4]. In this context, the implementation of artificial intelligence (AI)-based course-recommender systems (CRS) emerges as a solution to these issues [5]. These systems can automatically provide personalized course recommendations to individual students in real time, leveraging their historical data [6; 7]. Historically, CRSs have predominantly relied on conventional data-driven recommendation algorithms, including content-based, collaborative filtering-based, and hybrid approaches [8]. While effective in maintaining students' existing course-selection patterns by offering data-driven recommendations, these approaches are not without their shortcomings [9]. They can inadvertently introduce problems like filter bubbles and algorithmic bias, potentially preventing students from discovering valuable yet unpopular courses that could enrich their intellectual growth [10; 11]. To prevent these issues and ensure a more balanced course-selection decision-making process, there is a growing need for a new breed of AI-based CRS that departs from the traditional data-driven model. In recent times, efforts have intensified to address these limitations by integrating Large Language Models (LLMs), like ChatGPT, into recommender systems and exploring novel recommendation methodologies [12; 13]. LLM-powered recommender systems have the potential to decipher intricate patterns within students' textual interactions, discern nuanced preferences, and align with their learning objectives [14; 15]. Leveraging the capabilities of LLM in recommender systems is anticipated to deliver adaptive and context-aware recommendations, therefore tackling the challenges associated with evolving student needs and mitigating issues related to a cold start and data sparsity [16; 17]. However, it is equally important to consider how these systems can foster meaningful collaboration between students and AI, with a focus on user-centered design [18; 19]. Establishing a harmonious partnership between students and AI in the realm of education is critical for ensuring a seamless and effective personalized learning experience that aligns with the diverse needs of students [20]. From this perspective, it is important to understand the impact of AI-based CRSs on students' course-selection decision-making process [21]. To address this need, we employed the speed dating research method [22] to expose students to different types of AI-based CRSs and subsequently assess their perceptions and expectations of AI-based CRSs during the course-selection decision-making process [23]. The results of the speed dating activity were analyzed through a thematic analysis approach [24]. We found that students wanted AI-based CRSs to retrieve, filter, and visualize high-quality information related to their courses during the course-search phase and quantify difficult-to-evaluate qualitative information during the course-evaluation phase. These findings showed that students expect AI-based CRSs to play different roles at each phase of the course-selection decision-making process. This paper is organized as follows: Section 2 delves into the course-selection decision-making process and reviews literature related to AI-based CRSs. Section 3 outlines the experimental procedure employing the speed dating research method.