The dataset analyzed, visualized, and fed to different machine learning algorithms such as logistic Regression (LR), linear discriminant analysis (LDA), K-nearest neighbors (KNN), classification and regression trees (CART), naive Bayes (NB), support vector machines (SVM), and finally random forest (RF) algorithm and achieved a maximum accuracy of 58%. In this concern, we have built a use case model by using a student assessment data of our students and then built a synthesized using generative adversarial network (GAN). Various arguments and challenges on the implementation of artificial intelligence are prevailing in the educational sector. This research article throws light on the current education system the problems faced by the subject faculties, students, changing government rules, and regulations in the educational sector. Through this paper, the authors try to predict the future of higher education with the help of artificial intelligence. In this current scenario, there is a huge need to arise, implement information bridge technology, and enhance communication in the classroom. Despite the fact that this technology is used in higher education, many professors are unaware of it. A total of 22,554 articles were retrieved and after applying inclusion, exclusion, and quality criteria, an overall number of 99 articles were selected, which are the basis for this systematic review.Īrtificial intelligence is an emerging technology that revolutionizes human lives. Well-known scientific databases were searched for articles on IAs published from January 2015 to June 2021 using 172 search strings. A set of research questions was formulated to guide this systematic review and address the proposed objectives. As a result of this systematic review, it is also proposed a taxonomy of IAs and a set of potential future research directions to further improve IAs. This paper presents a systematic review aiming to (i) describe, classify, and organize recent advances in IAs (ii) characterize IAs’ objectives, application domains, and workings (iii) analyze how IAs have been evaluated and (iv) identify what artificial intelligence and machine learning techniques are used to enable the intelligence of IAs. In particular, these efforts have led to an increasingly extensive and complex state of the art of IAs, making evident the need to carry out a review in order to identify and catalogue the advances in the construction of IAs as well as to detect potential areas of further research. IAs have gained in popularity over recent years due to their usefulness, significant commercial developments, and a myriad of scientific and technological advances resulting from research efforts by the computer science community. RQ1.Īn intelligent assistant (IA) is a computer system endowed with artificial intelligence and/or machine learning techniques capable of intelligently assisting people.
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