@inproceedings{bd2d4509481f4dec914c7062b770ed6a,
title = "AARA: An AI Registration Assistant",
abstract = "An Artificial Intelligence (AI) agent is an expert that can handle tasks efficiently and accurately, and can execute specific tasks automatically. In this paper, we develop an AI class scheduling assistant system. This system consists of a large language model (LLM) which serves as an interface to the user, and an agent. This agent contains several sub-agents to perform the scheduling task. The data used by the agent was acquired from the University website. It was pre-processed and stored on an excel file that is accessible to the agent. One of the sub-agents will load the file and convert it into Python Pandas data frame. Another sub-agent has the function of finding all the available class sections based on the user{\textquoteright}s input from the data frame. This available section information can be used later, along with the user{\textquoteright}s data by other sub-agents. OpenAI GPT-4 model is used for the LLM. We implemented the system using Python programming language. According to our experimental results, the agent workflow runs smoothly and quickly, and the system performs the scheduling task accurately.",
keywords = "AI Agent, Artificial Intelligence, LLM, OpenAI API, Scheduling",
author = "John Jenq",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.; 4th World Conference on Information Systems for Business Management, ISBM 2025 ; Conference date: 24-09-2025 Through 26-09-2025",
year = "2026",
doi = "10.1007/978-3-032-13419-6\_39",
language = "English",
isbn = "9783032134189",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "410--418",
editor = "Andres Iglesias and Jungpil Shin and Nityesh Bhatt and Amit Joshi",
booktitle = "Information Systems for Intelligent Systems - Proceedings of ISBM 2025",
}