Title: AI-Based Decision Support System for Personalized Student Academic Advising
Authors: Ahmad A. Musleh, Taif J. Abu-Musabeh, Samy S. Abu-Naser
Volume: 10
Issue: 7
Pages: 1-9
Publication Date: 2026/07/28
Abstract:
Background: Academic advising plays an important role in helping students choose suitable academic specializations according to their abilities, interests, and academic performance. However, students may face difficulties in selecting the most appropriate specialization due to the availability of multiple academic tracks and limited access to academic advisors. Objectives: The main objective of this research is to develop a Student Academic Advisor Expert System that assists students in selecting the most suitable academic specialization based on their Grade Point Average (GPA) and personal interests. Methods: The proposed expert system was designed and implemented using SL5 Object Expert System Language. The system uses a rule-based knowledge base that contains a set of IF-THEN rules representing academic advising knowledge. Students are asked a series of questions related to their GPA and interests in areas such as programming, databases, artificial intelligence, networking, and cyber security. The inference engine analyzes the provided information and generates an appropriate recommendation. Results: The proposed system was able to provide specialization recommendations that match the academic profile and interests of students. The system offers a simple and interactive environment that facilitates the academic decision-making process. Conclusions: The Student Academic Advisor Expert System provides an effective tool for supporting students in choosing academic specializations. The use of expert system technologies and knowledge representation techniques demonstrates the applicability of Knowledge-Based Systems in the educational domain and contributes to improving the quality of academic guidance.