Prevention of Errors in "Real Time" using Model AI: Improving Operational Efficacy of Student Information Systems (SIS) in an Educational Institution

Abstract

The primary objective of this project is to evaluate whether implementing GoodRepsAI’s product, known as ModelAI or ModelAI tool, can enhance operational efficiency by reducing errors within GAMA’s (educational institution) operations. Specifically, this involves integrating ModelAI with the student information system (SIS) managed by GEGI, an educational technology company. GAMA relies heavily on the system for necessary daily tasks to complete operations, including admissions, student services, academics, registrar functions, accounting, and financial aid. Unfortunately, GAMA administrators have consistently encountered operational errors while using GEGI SIS. Notably, GEGI’s customer service representatives handle many support requests from user errors within GAMA’s administration. Both companies grapple with managing repetitive errors impacts encountered while using GEGI SIS. The focus of my investigation centers around determining if the ModelAI can effectively tackle the stated issue by seamlessly integrating into GEGI SIS to alleviate the reported operational challenges. The proposed solution entails leveraging the Model AI as a measure to recognize patterns and prevent errors before they arise. The expected outcome should provide recommendations regarding the feasibility and desirability of the implementation by stakeholders and partner organization as a solution.