Assessment of a student means collecting, analyzing information, interpreting, and acting on that information about his/her performance with respect to learning goals ^[1][14]. There are many types of assessments. However, the choice of assessment depends upon the purpose and choice of the person making the assessment. For example, educational institutes mainly use standard-based assessments ^[2][15] which is beneficial for grading, etc. Another type of assessment is learner-centered measurement models ^[3][16] which are most formative and is beneficial for the guidance of instruction and for the supporting learning of students. It may or may not always be valid or useful. Unlike the traditional or old methods of assessments, currently, computer-based applications are also in use for the purpose of assessments ^[4][17]. Those AI applications not only give a rapid assessment of large numbers of students but also same the same standards without any biases, etc., and all students get the grading without the fear of bias from any likes or dislikes. Additionally, it also assists the teachers, minimizes their workload, and provides time for other tasks.

On one hand, the assessment of a large number of students is not an easy task, on the other hand, it is one of the primary tasks of teachers ^[5][18]. After the attack of COVID-19, educational institutes have shifted their operation to online learning systems and systems like Learning Management Systems, MOOC, MOODLES, etc., and it is very difficult for teachers to handle everything online, especially the assessment of assignments, quizzes, and answer papers ^[6][19]. Automatic assessment or grading systems are one of the answers to address the issue. Questions of various types like short answer questions, multiple-choice questions, etc., can be assessed through automatic assessment systems ^[7][20]. Many researchers have worked for the development of impartial and effective grading or assessment systems using different types of computer technology ^[8][9][21,22]. AI applications through machine learning methods and unsupervised clustering algorithms can work effectively and can address the challenge ^[10][23]. The scope of this study is limited to the managerially highlight the use of AI applications in the assessment of student performance, the technicalities of which are described by ^[11][12][13][24,25,26], etc. It can be concluded that AI applications can assist a lot in the student performance assessment and grading as well as minimize the tasks of a teacher. This will also increase impartiality and effectiveness in assessment as compared to the traditional type of assessment.

As AI has its helping characteristics in many walks of life especially in education, its application starts from the admission process. Many education institutes advertise admissions on their websites and the students expect satisfactory services like consultation and information related to admission ^[14][27]. Nowadays, many universities, etc., are providing the necessary services to be given to the candidates or their parents, through web-based service systems. Therefore, this requires it to be developed as user-friendly as possible, which is the main factor innovators consider during the technology acceptance model ^[15][16][17][28,29,30]. Although websites provide a lot of help in the admission process, it may lead to a pool of questionnaires and lengthier waiting times which minimizes visitor satisfaction ^[18][31]. Like humans, AI can also answer questions and provide information. One of the means which is very famous is Chatbot, where computer-based information technology system interacts with humans ^[19][32]. It is defined as a “software program that simulates a conversation with human users, using text, voice, or images or a combination of spoken and visual heuristics” ^[20][33]. It is used in many commercial and education universities’ websites to answer visitors like a human would ^[21][34].

Chatbots are referred as technology-fueled virtual assistants and it stems from established/written scripts or AI. It works 24/7 by providing the necessary knowledge and answers related to admission. It not only helps the visitors or information seekers around the clock to obtain what they need but also reduces the burden on the admission staff, etc. ^[22][35]. This study is not looking into the technical structure or algorithm of Chatbot, it only focuses on its usage in the admission process and how it reduces the workload on the admission department. However, it is important to be mentioned here in layman language that it uses a keyword or string similarity algorithm to search through a script or data set and finds a suitable answer. In addition, it also provides relevant links to the user if he/she is not satisfied and a web interface for both the user and the administrator.

It can be summarized from the literature that AI applications in the form of Chatbot, etc., are helping the education institutions in the admission process to a greater extent. It not only assists in the admission process but also decreases the question answering burden on admission staff and department. It provides services round the clock without any assistance from humans. Figure 1 shows the detailed structure of Chatbot and how it works ^[23][36].

Virtual reality technology has appealed to many sectors, especially education by providing and adding new opportunities and benefits to the education process ^[24][37]. It is a stimulating experience similar to the real world or as desired by the programmer. Its applications extend to education, business, and entertainment ^[25][38]. It is also defined as “the computer-supported setting that enhances the real-world experience through the provision of multi-aesthetic stimuli (e.g., visual, audio, motion)” ^[26][39]. Examples of VR settings used in educational institutions are room-scale VR such as CAVE, standalone-VR such as Oculus Rift, HTC Vive, and mobile-VR such as Samsung Gear VR, Google Cardboard, etc. With the expansion of education around the globe, it is necessary to provide well-designed instructional contexts to learners or students, and the name that comes to mind for the purpose is virtual reality. Through it, science, engineering, technology, mathematics, physics, etc. education can be delivered in an outstanding manner ^[27][40].

Virtual reality can address the issues that teachers and students face in traditional instructional methods like delivering lectures in classrooms and conducting experiments physically, where acquiring in-depth knowledge and understanding are very important ^[28][41]. VR is contributing to the learning process in a potential manner due to its value addition to interactivity, information intensity, and involvement ^[29][42]. Some experiments are very risky and there are a lot of environmental and safety concerns, some activities like field-based experiments and the use of expensive equipment are very costly; for both cases, VR is the best option. Another case of VR usage and its importance is where when the students or learners have no experience of using something or of working in an environment and safety and security risks are there ^[30][43]. Even if a teacher is present in such critical situations, it is not possible for him/her to provide enough attention and time to each student, which further creates frustration, negative emotions, dissatisfaction, etc., among the students. With such feelings, students can neither learn the theoretical knowledge nor obtain experiential advancement ^[31][44].

However, in learning through VR, the learners are free from any interruptions and can get a high-level involvement in the environment. The environment provides a sense of realism to the learner which leads to the sense of presence and finally to learning outcomes in a positive manner ^[32][33][45,46].

In short, VR is used to provide education with the help of a virtual environment where learners or students develop and enhance their understanding, skills, and experience without facing the fear of failure, danger, or any other negative consequences. It is in used in school education ^[34][47], military training ^[35][48], medical education ^[36][49], astronaut training ^[37][50], miner training ^[38][51], driver training ^[39][52], civil engineering ^[40][53], etc.

Learning Analytics (LA) is defined as “the measurement, collection, analysis, and reporting of data about learners and their contexts, for the purposes of understanding and optimizing learning and the environments in which it occurs” ^[41][54]. It has attracted the eyes of many areas like academics, research, etc. In education, especially, this is due to the need of getting a better understanding of teaching, personalization, adaptation, and intelligent content. After the emergence of the big data concept, analytics has the capabilities to increase the productivity of an organization ^[42][55] and enhance competition ^[43][56]. It is a fact that the education sector has not used the data for improvement but the development of AI and its application in education has motivated the educators to collect and analyze the data and put solutions to many issues and challenges. Nowadays data from education institutes are used for making various types of analyses and decisions ^[44][57]. The data is collected and recorded when the students use social media, LMS, MOOC, etc. Their clicks on various buttons, navigation, the time they spent on a task, everything can be tracked which then is used by the analysts to evaluate the teaching environment and enhance it ^[45][58]. This is evident from another definition which is “Analytics is the process of developing actionable insights through problem definition and the application of statistical models and analysis against existing and/or simulated future data” ^[46][59]. It is concerned with making sense of data, action, and data mining in education institutes.

Learning Analytics is seen differently by different researchers. Perhaps the reason behind this is its applications to different areas and at different levels. It is viewed as a prediction model due to its use of intelligent data, the data produced by the learner, and the analytical models to predict student learning ^[47][60]. It is also considered a generic design framework ^[48][61], data-driven decision-making ^[49][62], an application of analytics ^[50][63], and the application of data science ^[51][64].

In the education and learning industry, analytics is important and necessary at different levels like classrooms, departments, universities, and the regional, national, and international levels. At each level, it gives different outputs for the betterment of education. For example, in classrooms, it gives information about the student’s interest, social networks, intelligence level, grades, and many more. At the department level, it gives the statistics about the department like risk, intervention, support services, and guides what to do and what not. At a national and international level, it gives direction for educational policy, budget, etc. It is termed as micro, meso, and macro-analytics level ^[52][65]. For each level, there is a need for different types of data sets, depending upon the objectives and contexts of the analytics. LA is explained in Figure 2 as proposed by Khalil and Ebner ^[53][66].