# SmartCourse: A Contextual AI-Powered Course Advising System for Undergraduates

arXiv: https://arxiv.org/abs/2507.22946 · HTML: https://mlsanity.com/paper/2507.22946/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 26 Jul 2025.

## Builds on (most cited first, 6 of 6)

- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [Personalized Course Sequence Recommendations](https://mlsanity.com/paper/1512.09176/) (2015)
- [Which Type of Students can LLMs Act? Investigating Authentic Simulation with Graph-based Human-AI Collaborative System](https://mlsanity.com/paper/2502.11678/) (2025)
- [Helping university students to choose elective courses by using a hybrid multi-criteria recommendation system with genetic optimization](https://mlsanity.com/paper/2402.08371/) (2024)
- [From Interests to Insights: An LLM Approach to Course Recommendations Using Natural Language Queries](https://mlsanity.com/paper/2412.19312/) (2024)

## Built on by (most cited first, 1 of 1)

- [A Hybrid AI Framework for Academic Advising: Integrating Ensemble-Based Grade Prediction and a Rule-Based Expert System](https://mlsanity.com/paper/2609.26243/) (2026)

## Abstract (arXiv)

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.