Artificial Intelligence–Based Academic Advising in Higher Education

Daisuke Akiba

Artificial Intelligence–Based Academic Advising in Higher Education

Author(s)	Dr. Daisuke Akiba
Country	United States
Abstract	Academic advising plays a central role in helping university students select courses, understand degree requirements, respond to academic difficulties, and make informed educational decisions. Traditional advising, however, is frequently constrained by increasing student-to-advisor ratios, fragmented institutional information, limited appointment availability, and the difficulty of providing individualized guidance at scale. Artificial intelligence offers an alternative model in which academic records, learning patterns, program requirements, student preferences, and risk indicators can be analyzed to support timely and personalized advising decisions. This study examines the potential contribution of artificial intelligence–based academic advising to higher education through a simulation-based comparative framework. The proposed advising architecture integrates predictive analytics, rule-based curriculum verification, recommendation mechanisms, student-risk identification, and human advisor oversight. A synthetic dataset containing 300 advising scenarios was developed to evaluate the conceptual performance of conventional and AI-assisted advising across response timeliness, recommendation consistency, identification of academically vulnerable students, personalization quality, and advisor workload efficiency. Because the dataset is simulated, the reported results represent analytical demonstrations rather than observed institutional outcomes. The simulated analysis indicates that AI-assisted advising can improve the speed and consistency of routine advising processes while strengthening early identification of students who may require additional academic support. The largest modeled advantage was observed in advisor workload efficiency, followed by at-risk student identification and response timeliness. Nevertheless, effective implementation depends on data quality, explainability, privacy protection, algorithmic fairness, institutional governance, and meaningful human supervision. The study therefore argues for a hybrid advising model in which artificial intelligence manages data-intensive and repetitive tasks while professional advisors retain responsibility for complex, sensitive, and consequential decisions. The paper contributes an academically transparent framework for examining AI-enabled advising without treating artificial intelligence as a substitute for human educational judgment. It concludes that the strongest institutional value is likely to arise when AI is designed as an advisory decision-support infrastructure embedded within broader student-success systems.
Keywords	Artificial intelligence, academic advising, higher education, predictive analytics, student success, recommendation systems, educational technology, personalized learning
Field	Engineering
Published In	Volume 1, Issue 2, March-April 2019
Published On	2019-04-04

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American International Journal of Research and Innovation

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Artificial Intelligence–Based Academic Advising in Higher Education

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