Artificial Intelligence(AI) Applications In Higher Education Business – CIO Applications

If too many applicants accept offers it will be a problem for Facilities Management to fit the class and then there can be a potential resource constraint issue. Financial aid/scholarship is offered to many applicants to entice them to accept the offer and usually, there is a fixed budget for financial aid. As seen above, the admission decision making has to include several moving parts and multiple constraints. This creates a great opportunity to use data science to solve many of these problems. 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.

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.

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Artificial Intelligence(AI) Applications In Higher Education Business - CIO Applications