Pathways: Exploring Academic Interests with Historical Course Enrollment Records

Chen, Youjie; Fu, Annie; Lee, Jennifer Jia-Ling; Tomasik, Ian Wilkie; Kizilcec, René F.

doi:10.1145/3491140.3528270

Cited by 3 publications

(1 citation statement)

References 34 publications

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“…For example, collaborative filtering, widely employed in AI-based CRSs, suggests courses based on community records of peers with similar characteristics [33,34]. Similarly, content-based filtering techniques aid in recommending courses aligning with students' academic interests [35,36]. Moreover, AI-based CRSs extend their utility to suggest courses facilitating the acquisition of competencies relevant to students' future careers [37,38].…”

Section: Ai-based Course-recommender Systemsmentioning

confidence: 99%

The Impact of AI-Based Course-Recommender System on Students’ Course-Selection Decision-Making Process

Cha,

Loeser,

Seo

2024

Applied Sciences

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The course-recommender system (CRS), designed to aid students’ course-selection decision-making process by suggesting courses aligned with their interests and grades, plays a crucial role in fulfilling curricular requirements, enhancing career opportunities, and fostering intellectual growth. Recent advancements in artificial intelligence (AI) have empowered CRSs to deliver personalized recommendations by considering individual contexts. However, the impact of AI-based CRS on students’ course-selection decision-making process (inter alia, search and evaluation phases) is an open question. Understanding student perceptions and expectations of AI-based CRSs is key to optimizing their decision-making process in course selection. For this purpose, we employed speed dating with storyboards to gather insights from 24 students on five different types of AI-based CRS. The results revealed that students expected AI-based CRSs to play an assistive role in the search phase, helping them efficiently complete time-consuming search tasks in less time. Conversely, during the evaluation phase, students expected AI-based CRSs to play a leading role as a benchmark to address their uncertainty about course suitability, learning value, and serendipity. These findings underscore the adaptive nature of AI-based CRSs, which adjust according to the intricacies of students’ course-selection decision-making process, fostering fruitful collaboration between students and AI.

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