Abstract:
The university admissions process is characterized by significant complexity, and these challenges often persist even for students seeking to transfer majors. This intricate system can be a source of considerable stress, anxiety, and frustration as students navigate the pathways to acceptance and solidify their chosen career trajectories (Mengash, 2020). Existing course advisor services, while helpful, are limited by availability and subjective feedback (Thottoli et al., 2023).
This study aims to develop an AI-based system to predict the likelihood of an application's success in university admissions. It is an exploratory study designed to assess the potential of Artificial Intelligence (AI) to guide students through the complexities and to understand the types and breadth of data necessary for accurate prediction. The project involves designing and testing an AI-based system that uses real-life university application data to predict the likelihood of university admission. Beyond prediction, the chatbot will prioritize a seamless and intuitive user experience. The methodology includes selecting a diverse group of applicants, collecting their application histories, and training an AI model with a deep neural network on this data. The model's predictions will be evaluated for accuracy, precision, and recall.
Mengash, H. A. (2020). Using data mining techniques to predict student performance to support decision making in university admission systems. Access, 8, 55462-55470.
Thottoli, M. M., Alruqaishi, B. H., & Soosaimanickam, A. (2023). Robo academic advisor: Can chatbots and artificial intelligence replace human interaction? Contemporary Educational Technology, 16(1), p485.
This study aims to develop an AI-based system to predict the likelihood of an application's success in university admissions. It is an exploratory study designed to assess the potential of Artificial Intelligence (AI) to guide students through the complexities and to understand the types and breadth of data necessary for accurate prediction. The project involves designing and testing an AI-based system that uses real-life university application data to predict the likelihood of university admission. Beyond prediction, the chatbot will prioritize a seamless and intuitive user experience. The methodology includes selecting a diverse group of applicants, collecting their application histories, and training an AI model with a deep neural network on this data. The model's predictions will be evaluated for accuracy, precision, and recall.
Mengash, H. A. (2020). Using data mining techniques to predict student performance to support decision making in university admission systems. Access, 8, 55462-55470.
Thottoli, M. M., Alruqaishi, B. H., & Soosaimanickam, A. (2023). Robo academic advisor: Can chatbots and artificial intelligence replace human interaction? Contemporary Educational Technology, 16(1), p485.