AdvisingWise: Supporting Academic Advising in Higher Education Settings Through a Human-in-the-Loop Multi-Agent Framework
Wendan Jiang, Shiyuan Wang, Hiba Eltigani, Rukhshan Haroon, Abdullah Bin Faisal, Fahad Dogar

TL;DR
AdvisingWise is a multi-agent system that enhances academic advising by automating tasks with LLMs while ensuring human oversight, leading to more accurate, personalized support and improved advisor perceptions.
Contribution
This paper introduces AdvisingWise, a novel human-in-the-loop multi-agent framework that combines AI automation with human validation for effective academic advising support.
Findings
AdvisingWise produces accurate, personalized responses.
Advisors' perceptions of the system improved after use.
The system effectively integrates authoritative resources and adaptive prompts.
Abstract
Academic advising is critical to student success in higher education, yet high student-to-advisor ratios limit advisors' capacity to provide timely support, particularly during peak periods. Recent advances in Large Language Models (LLMs) present opportunities to enhance the advising process. We present AdvisingWise, a multi-agent system that automates time-consuming tasks, such as information retrieval and response drafting, while preserving human oversight. AdvisingWise leverages authoritative institutional resources and adaptively prompts students about their academic backgrounds to generate reliable, personalized responses. All system responses undergo human advisor validation before delivery to students. We evaluate AdvisingWise through a mixed-methods approach: (1) expert evaluation on responses of 20 sample queries, (2) LLM-as-a-judge evaluation of the information retrieval…
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Taxonomy
TopicsOnline Learning and Analytics · Intelligent Tutoring Systems and Adaptive Learning · Expert finding and Q&A systems
