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VOL. 10, ISSUE 3 (2025)
AI-based predictive analytics for admission and enrollment management in colleges
Authors
Prakash Balasaheb Mande, Saloni Ulhas Katore
Abstract
AI-based predictive analytics is
revolutionizing admission and enrollment management in colleges by leveraging
machine learning algorithms and data-driven insights to enhance
decision-making. Institutions can analyze historical student data, demographic
trends, academic performance, and behavioral patterns to predict enrollment
probabilities, student success rates, and retention risks. These predictive
models enable colleges to personalize recruitment strategies, optimize financial
aid distribution, and improve student engagement, ultimately enhancing
institutional efficiency and student outcomes. By integrating AI with real-time
data analytics, colleges can forecast demand for specific programs, identify
at-risk students, and implement proactive interventions to improve retention
and graduation rates. Moreover, AI-driven chatbots and recommendation systems
can enhance the student experience by providing personalized guidance during
the application process. The adoption of AI in admission management minimizes
biases, streamlines administrative workflows, and supports data-driven
decision-making, leading to increased institutional competitiveness and
sustainability. As AI technology continues to evolve, its application in higher
education is expected to become even more sophisticated, fostering a more
efficient, inclusive, and student-centric approach to enrollment management.
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Pages:150-154
How to cite this article:
Prakash Balasaheb Mande, Saloni Ulhas Katore "AI-based predictive analytics for admission and enrollment management in colleges". International Journal of Advanced Scientific Research, Vol 10, Issue 3, 2025, Pages 150-154
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