Design and Implementation of AI-Driven Solutions for Student Academic Success: Uncovering Patterns for undergraduate Students of UTAS-Salalah in Probation Status Prediction and Optimization

Abstract

This study investigates how UTAS academic probationary students assist, navigate, and interpret their experiences. The most overrepresented student traits on probation were identified using quantitative methods. The experiences, difficulties, and ways in which students were adjusting to their probationary placement were determined using qualitative methodologies. Under preparedness or a lack of academic success techniques fit for college-level academic work were the main causes of academic challenges reported. Other encounters that added to the general difficulty of college for students were difficulties with institutions and instruction and; the absence of academic assistance in terms of laying down different scenarios for students’ references for possible retention and success. 

It might not have been plausible a few years ago to think that machines would have a role in higher education. However, with the advancement of technology, artificial intelligence is now not just used in educational settings but is also necessary for efficient customer service and effective student retention. AI has emerged as the key for educational institutions to improve marketing strategies, enhance the customer experience, and reverse the trend of a large number of students quitting their courses midway through. 

With Artificial Intelligence we can recognize patterns and trends that the human eye might not notice right away. An AI system might discover, for instance, that students with a certain learning style or involvement in extracurricular activities have a higher chance of academic success. Artificial intelligence (AI)-driven prescriptive models are capable of processing far more data faster and more thoroughly than conventional what-if scenario planning such as analyzing the student’s transcript of records from their academic performances. Academic advisors can then utilize this data to customize their lesson plans and instructional materials to better meet the needs of their students. For instance, earlier this year, a university conducted a study on how AI and machine learning may benefit the pool of college applicants.