# AI-Powered Academic Guidance and Counseling System Based on Student Profile and Interests
###### Abstract
Since then, it has become necessary to adapt the counseling process to these changing times and encourage counselors to embrace technological tools [16] in order to help effectively meet the evolving needs of students in the current educational landscape and provide them with support that aligns with the contemporary educational paradigm. Moreover, the use of technology in educational counseling services and tutoring functions has been widely studied, with numerous research [13; 14; 15; 16; 17; 18] indicating that it can significantly enhance their effectiveness and accessibility. As a result, many online counseling platforms have been designed and created [19; 20; 21] that allow students to connect with educational counselors to receive personalized career guidance and information about universities, programs, degrees, scholarships, and admissions. Certain reputed universities even offer online virtual tours of their campuses [22], and various platforms provide comprehensive information about colleges, universities, and admissions processes to help students make informed decisions about their academic and career paths [23]. These platforms also keep students updated with current educational trends and career opportunities. However, it is essential to mention that, in most cases, and due to the lack of human and financial resources, the educational counseling process still takes place in group sessions at schools [24; 25; 26], and some services, like online virtual tours, are only available for a few popular universities worldwide. Additionally, the existing counseling platforms involve certain drawbacks that need to be considered, such as:  
* Specialists and certified counselors are not typically involved in non-governmental educational counseling platforms. * The lack of a free online educational counseling service provided by the government. * The current platforms operate conventionally and do not incorporate web intelligence techniques [26]. * The number of educational counselors is still insufficient, given the number of students' needs [25]. As part of the ongoing efforts to improve the efficacy of educational counseling, particularly in terms of career and academic guidance [27; 28; 29], a novel web-based counseling system has been designed and developed as a supportive resource to aid students in making informed decisions pertaining to their university and major preferences. The system is aimed to support and help high school students who are navigating college applications in selecting the most appropriate college or university [27] by considering each student's academic background and interests, as well as the requirements of their selected university. The framework is designed to analyze various data inputs, including subject grades and academic setbacks. It works principally in two distinct scenarios; the first step involves predicting a student's chances of gaining admission into their preferred institution [30] from the available universities on the platform. In the second step, the framework utilizes a recommendation engine to suggest alternative universities [31] based on a description matching the career paths and opportunities those universities offer [32]. The recommendation option goal is to empower students by providing them with insights into similar academic opportunities at other universities, especially if their chances of admission are low at their first-choice institution. To generate the prediction scenario, 13 machine learning algorithms were investigated, including Decision Tree, Linear Regression, Random Forest, K-Neighbours, Support Vector Machine (SVM), AdaBoost Regressor, Gradient Boosting Regressor, XGBoost, CatBoost, Lasso, Ridge, Bayesian Ridge, and Huber Regressor. These algorithms were employed to identify the most accurate model for predictions suiting the collected educational data [33].