SmartCourse: A Contextual AI-Powered Course Advising System for Undergraduates
- URL: http://arxiv.org/abs/2507.22946v1
- Date: Sat, 26 Jul 2025 13:49:41 GMT
- Title: SmartCourse: A Contextual AI-Powered Course Advising System for Undergraduates
- Authors: Yixuan Mi, Yiduo Yu, Yiyi Zhao,
- Abstract summary: SmartCourse addresses the limitations of traditional advising tools by integrating transcript and plan information for student-specific context.<n>The system combines a command-line interface (CLI) and a Gradio web GUI for instructors and students, manages user accounts, course enrollment, grading, and four-year degree plans, and integrates a locally hosted large language model (via Ollama) for personalized course recommendations.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We present SmartCourse, an integrated course management and AI-driven advising system for undergraduate students (specifically tailored to the Computer Science (CPS) major). SmartCourse addresses the limitations of traditional advising tools by integrating transcript and plan information for student-specific context. The system combines a command-line interface (CLI) and a Gradio web GUI for instructors and students, manages user accounts, course enrollment, grading, and four-year degree plans, and integrates a locally hosted large language model (via Ollama) for personalized course recommendations. It leverages transcript and major plan to offer contextual advice (e.g., prioritizing requirements or retakes). We evaluated the system on 25 representative advising queries and introduced custom metrics: PlanScore, PersonalScore, Lift, and Recall to assess recommendation quality across different context conditions. Experiments show that using full context yields substantially more relevant recommendations than context-omitted modes, confirming the necessity of transcript and plan information for personalized academic advising. SmartCourse thus demonstrates how transcript-aware AI can enhance academic planning.
Related papers
- Graph Retrieval-Augmented LLM for Conversational Recommendation Systems [52.35491420330534]
G-CRS (Graph Retrieval-Augmented Large Language Model for Conversational Recommender Systems) is a training-free framework that combines graph retrieval-augmented generation and in-context learning.<n>G-CRS achieves superior recommendation performance compared to existing methods without requiring task-specific training.
arXiv Detail & Related papers (2025-03-09T03:56:22Z) - Recommending the right academic programs: An interest mining approach using BERTopic [46.133648730062035]
This paper presents the first information system that provides students with efficient recommendations based on both program content and personal preferences.<n>BERTopic, a powerful topic modeling algorithm, is used that leverages text embedding techniques to generate topic representations.<n>A case study at a post-secondary school shows that the system provides immediate and effective decision support.
arXiv Detail & Related papers (2025-01-11T16:34:10Z) - From Interests to Insights: An LLM Approach to Course Recommendations Using Natural Language Queries [0.0]
This paper describes a novel Large Language Model (LLM) course recommendation system.<n>It applies a Retrieval Augmented Generation (RAG) method to the corpus of course descriptions.<n>The system first generates an 'ideal' course description based on the user's query.<n>This description is converted into a search vector using embeddings, which is then used to find actual courses with similar content.
arXiv Detail & Related papers (2024-12-26T18:19:53Z) - CourseAssist: Pedagogically Appropriate AI Tutor for Computer Science Education [1.052788652996288]
This poster introduces CourseAssist, a novel LLM-based tutoring system tailored for computer science education.
Unlike generic LLM systems, CourseAssist uses retrieval-augmented generation, user intent classification, and question decomposition to align AI responses with specific course materials and learning objectives.
arXiv Detail & Related papers (2024-05-01T20:43:06Z) - Helping university students to choose elective courses by using a hybrid
multi-criteria recommendation system with genetic optimization [0.0]
This paper presents a hybrid RS that combines Collaborative Filtering (CF) and Content-based Filtering (CBF)
A Genetic Algorithm (GA) has been developed to automatically discover the optimal RS configuration.
Experimental results show a study of the most relevant criteria for the course recommendation.
arXiv Detail & Related papers (2024-02-13T11:02:12Z) - Knowledge Graphs and Pre-trained Language Models enhanced Representation Learning for Conversational Recommender Systems [58.561904356651276]
We introduce the Knowledge-Enhanced Entity Representation Learning (KERL) framework to improve the semantic understanding of entities for Conversational recommender systems.
KERL uses a knowledge graph and a pre-trained language model to improve the semantic understanding of entities.
KERL achieves state-of-the-art results in both recommendation and response generation tasks.
arXiv Detail & Related papers (2023-12-18T06:41:23Z) - ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback [54.142719510638614]
In this paper, we frame the problem of providing feedback as few-shot classification.
A meta-learner adapts to give feedback to student code on a new programming question from just a few examples by instructors.
Our approach was successfully deployed to deliver feedback to 16,000 student exam-solutions in a programming course offered by a tier 1 university.
arXiv Detail & Related papers (2021-07-23T22:41:28Z) - KnowledgeCheckR: Intelligent Techniques for Counteracting Forgetting [52.623349754076024]
We provide an overview of the recommendation approaches integrated in KnowledgeCheckR.
Examples thereof are utility-based recommendation that helps to identify learning contents to be repeated in the future, collaborative filtering approaches that help to implement session-based recommendation, and content-based recommendation that supports intelligent question answering.
arXiv Detail & Related papers (2021-02-15T20:06:28Z) - UniNet: Next Term Course Recommendation using Deep Learning [0.0]
We propose a deep learning approach to represent how chronological order of course grades affects the probability of success.
We have shown that it is possible to obtain a performance of 81.10% on AUC metric using only grade information.
This is shown to be meaningful across different student GPA levels and course difficulties.
arXiv Detail & Related papers (2020-09-20T00:07:45Z) - Attentional Graph Convolutional Networks for Knowledge Concept
Recommendation in MOOCs in a Heterogeneous View [72.98388321383989]
Massive open online courses ( MOOCs) provide a large-scale and open-access learning opportunity for students to grasp the knowledge.
To attract students' interest, the recommendation system is applied by MOOCs providers to recommend courses to students.
We propose an end-to-end graph neural network-based approach calledAttentionalHeterogeneous Graph Convolutional Deep Knowledge Recommender(ACKRec) for knowledge concept recommendation in MOOCs.
arXiv Detail & Related papers (2020-06-23T18:28:08Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.