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Artificial Intelligence(AI) applications in Higher Education Business
Arkajyoti Chakraborty, Director of Budget and Analytics, University of Virginia
Some of the areas where data science can be used are student admissions cycle, student lifecycle management, career placements, donor relations, financial operations and research/publications. Research itself is a broad area where there are several application of AI including deep learning (natural language processing and computer vision).
School admissions is a very critical area for any higher-education institute. On one hand, the institute has to make sure that the quality of students who get admitted is very high and on other hand, confirm that there should be enough good candidates to fill up the class size. The offer/acceptance ratio is important to hit a target where the institute has enough high-quality students to fill up the class size but do not have many more than the class size.
Universities get several data points for each applicant/ student from beginning of the application cycle, during several years of the program until career placement. These data comprise of academic scores, demographic, academic interaction, performance, placement and many more aspects. All these data can be used comprehensively to review, monitor and advise each student for better academic outcome during the course of the program and also in the future.
Data science models can be used to predict who should be offered admissions, what is the chance of the admitted person to accept the offer and how much financial aid should be awarded for each potential offer to matriculate
Last but not the least; AI application can be used to streamline university financial operations. Machine Learning can be used to facilitate accounting in terms of coding, reconciliation and effective reporting.