The Role of Artificial Intelligence-Based Recommendation Systems in Selection of Courses for Students - Zulfikri Akbar; Encep Sopandi; Badruzzaman Badruzzaman; Muh Fihris Khalik (Yayasan Adra Karima Hubbi, 2023)
Background. Modern higher education institutions are faced with complex challenges in developing curricula that suit students’ needs and interests. To overcome this challenge, artificial intelligence-based recommendation systems are an attractive alternative. This system can help students in selecting courses, providing suggestions that suit their interests and needs. Purpose. This research aims to understand students’ experiences and views on recommendation systems in selecting courses in higher education, with a focus on system effectiveness, level of student trust, and ease of use. The main objective is to identify the impact of recommendation systems on students’ academic decisions. Method. The research used a quantitative survey method of 20 students at universities by collecting data through online questionnaires. The results of the analysis show that the majority of respondents are experienced with the recommendation system, rely on it in selecting courses, and tend to follow the recommendations, as well as showing user satisfaction and the influence of the system on academic decisions. Results. The results of the study show that artificial intelligence-based recommendation systems play an important role in guiding students in their academic decision-making. However, there is a need for a deeper understanding of the factors that influence user satisfaction and system effectiveness. The interim conclusion emphasizes the need for further development and adjustment of the course recommendation system in order to increase its responsiveness to student needs. Conclusion. This conclusion is the basis for deeper reflection and the development of a course recommendation system that can more effectively meet student expectations and needs in the ever-developing era of higher education. In this way, this research has the potential to make a significant contribution to the development of more adaptive and responsive academic decision support systems.