Abstract
Course recommendation systems play an essential role in academic institutions for students to find courses that align with their interests and graduation requirements. However, due to their 'black-box' nature, recommendation systems often lack transparency and interpretability, leading to challenges in trust and usability. Our proposed framework leverages Large Language Models (LLMs) to generate clear, human-readable explanations based on course content by drawing connections between the existing courses taken by the student and recommended courses.
| Original language | English |
|---|---|
| Title of host publication | 2024 IEEE 67th International Midwest Symposium on Circuits and Systems, MWSCAS 2024 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1408-1412 |
| Number of pages | 5 |
| ISBN (Electronic) | 9798350387179 |
| DOIs | |
| Publication status | Published - 2024 |
| Externally published | Yes |
| Event | 67th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2024 - Springfield, United States Duration: Aug 11 2024 → Aug 14 2024 |
Publication series
| Name | Midwest Symposium on Circuits and Systems |
|---|---|
| ISSN (Print) | 1548-3746 |
Conference
| Conference | 67th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2024 |
|---|---|
| Country/Territory | United States |
| City | Springfield |
| Period | 8/11/24 → 8/14/24 |
Bibliographical note
Publisher Copyright:© 2024 IEEE.
ASJC Scopus Subject Areas
- Electronic, Optical and Magnetic Materials
- Electrical and Electronic Engineering
Keywords
- Course Recommendation Systems
- Educational Technology
- Large Language Models