AIAVRT: 5.0 Transformation in Medical Education with Next Generation AI- 3D Animation and VR Integrated Computer Graphics Imagery

Head-mounted displays (HMDs) are commonplace in VR applications. A head-mounted display (HMD) is a display that supports immersive visualization of a simulated environment, while blocking out any external visual stimuli. The original HMDs could only display images of 800×600 pixels, and had a field of view (FOV) of 30°. In contrast, modern HMDs can display images in full high definition and have an FOV of up to 360°. Some wireless modern HMDs are embedded with positioning and detection systems for eye and head movements. Despite their importance in many VR environments, HMDs are not required for all medical applications. Many procedures, including laparoscopic and endoscopic surgeries, need a computer screen to display virtual images.

Network technology enables virtual classrooms, in which students and teachers interact via an online interface [6]. Considering the importance of career choice to a student, exceptional cultural practitioners adopted questionnaire surveys to identify critical impactors of learning performance. However, it is difficult to predict the course of work, given the complex goals and dreams of each student. Recent research has shown that student data based on behavioral features provide a good basis for workflow forecast. Hence, a revolutionary model was created and implemented to forecast student decisions, namely, ACCBOX (Approach Cluster Centers Based On XGBOOST). Experimental results show that the model outperforms all previous prediction algorithms.

In India, only 0.3% of the population advances from postgraduate to research level education. Focusing on student achievement through various reasoning, Walter et al. [7] divided students into low, middle, and high achievement groups, and combined support vector machine (SVM) with k-means clustering (KMC) to make the above architects accountable. The SVM was adopted for classification, and the KMC was used to assemble student data. The student performance was predicted on a range of tests, using data mining principles. The prediction program utilizes semantic rules and SVM concepts. The semantic limitations increase the quality of educational contents and facilitate the transmission of educational activity to all students. Their approach was found to benefit both low and high achievement students, and enhance their desire in continued learning. In this way, Walter enhanced the overall quality of learning by predicting their level of education and student assistance before they begin their studies.

Some scholars demonstrated the relationship between the level of primary education profile and the end level of education profile of Federal Polytechnic students. The O-level was defined as the first academic profile in the system of classification. The grade point average (GPA) was used to evaluate student achievement in courses (Grade R). Using data mining techniques, the authors intended to develop a new model for forecasting student performance in the future. The files were preprocessed to eliminate data redundancy. The idea of SVM was drawn to predict the success of students, and compared with the other ML ideas, such as line break, KNN, and decision tree. The SVM was proved to be more accurate than other ML concepts.

With the advancement of technology, virtual classes become much more accessible, particularly in social sciences [8]. It is increasingly important to predict the success of students in higher education institutions. This would help with the detection and improvement of student performance in various subjects. Many elements should be considered to enhance performance. The notion of clustering is often adopted to improve the quality of an enhancement learning program by separating students into groups. The Ada-boost genetically modified approach, often known as Ada-GA, were adopted by Militello et al. [8] to enhance the performance of dividers. This approach was utilized to assess the students’ risk level against a huge amount of collected data. The outcome provides teachers with a good reference for giving counselling to depressed students.

Liu [9] employed various tools and approaches in the virtual classroom, including video conferencing systems [9], and a multimedia environment that incorporates a whiteboard and text chat, among others. The students are accessible to many types of learning and teaching resources, including e-mail, YouTube, and wikis, while others make use of learning management systems (LMS). The market of virtual classroom grows, as new settings with more features begin to appear. In addition to sound and visual quality, the market focuses primarily on the overall implementation and usability of the environment. Interactive classrooms are originally utilized to benefit distance learners and mixed learning environments, as well as to supplement on-campus courses. In the virtual classroom, distant learners and instructors could interact actively to improve student engagement, inspire and encourage individuals, and assist students in developing a learning community while avoiding alienation. Group assignments in the virtual classroom enhance the students’ design skills, learning ability, motivation, and capacity, and help them understand more about themselves.

ML aims to investigate and invent algorithms that learn and anticipate the experimental outcomes. The University of Southern California has created an artificial gadget that includes avatar-based training modules, which could transfer military personnel to other locations for military training.

2.1 Impact of VR on classroom

2.1.1 Time efficiency

Traditional classrooms have a time limit. Sometimes, there is not enough time for the teacher to answer the questions raised by a student. In the virtual classroom, however, any student from anywhere can ask questions 24/7. There is no need to sit in the classroom and listen to lectures. The students only need to log in to the classroom server, pick the module, and choose subjects and teachers. Hence, every learner can better understand the topics with the aid of classroom resources [10].

2.1.2 Enhanced understanding and better learning ability

The virtual classroom provides many functions that develop student skills, making learning relatively easy. Students can access their classroom accounts, and learn from anywhere, anytime. The only requirements are classroom ID and Internet connection. Experienced teachers manage the class sessions in an online classroom, while the students communicate easily with the teachers. The teachers are offered innovative methods to connect with the students.

2.1.3 Accurate feedbacks and rating

At the end of the semester, the virtual classroom provides a feedback form to the students at school or colleges. Traditionally, the feedback process is a formality without much value. In the virtual classroom, however, hand-to-hand feedback is sent to students after each class, and a feedback report is issued on a daily basis [11]. The accurate feedback improves the skills and learning styles of both teachers and students. The virtual classroom provides a rating system. After each session, the teacher is rated by the AI technology. At the end of each month, the teacher with the highest rating will be observable openly.

2.1.4 ML and AI in virtual classroom

ML is a computer science based on AI. Most ML systems learn from their previous work, or gain knowledge of the prior work. As its name suggests, AI is both artificial and intelligent. Artificial means the replication is true to the original, and intelligent refers to the consistency between concepts and ideas, which facilitates user comprehensive. Instead of a machine, a tool, or a computing program, AI is applied in structures or devices. Using AI, a system can work insightfully to execute its tasks. AI can complete the jobs that can be done by humans effortlessly in the computer field [12].

On an intelligent learning platform, ML can easily handle scoring and grading tasks. Take the intellectual learning environment for example. Optical recognition, an AI technology, can be utilized in the grading system to complete grading with innovative assessments. The technology enables the teachers to optimize the efficiency of classroom teaching, and tailor the teaching to individual students.

Currently, ML and AI are being used to quickly review and rate written tasks, such as essays. Teachers can also store important college files and student scores in the cloud. All data must be preserved securely. Some cloud storages provide free reposition of up to 5 GB of data for new members [13].

In short, AI is any field in which the computer works intelligently. It derives from the human model. In virtual schools, the various cultural backgrounds and language abilities of students are fully considered. With the help of AI and natural language processing (NLP), the text files are interpreted manually, and subtitles of video files appear in the language that the user is familiar with.

Our experiments involve a total of 597 qualified students, who had shared their view on the use of VR in classroom teaching. The authors developed a VR teaching and learning platform, including MS Kinect. The training pitch simulation was created using MS HoloLens, HTC Vive Kit Pro, and Oculus Rift, Autodesk MAYA, and Unreal Game Engine. The data were collected using the VR simulator to identify user position, attention, and quick response, and to communicate the results to the VR devices wirelessly. Figure 2 illustrates the user activity and experiential learning in our model.

Table 1 summarizes the feedbacks of the students on different ML methods.

For the test group in the real operational environment [16], it is necessary to disassemble and reassemble the reducer assembly model with the aid of the data and tools, and to conduct a knowledge research and test based on the experimental results [17]. As shown in Table 2, the test group and the control group had not significant difference. The test devices and texts had an equivalent correlation with observed functions.

Virtual 2D texts and 3D videos exemplify how the learning materials of the AR education apps could be accessed from anywhere, and how the two types of data could be seen from any location.

Table 1. Feedbacks on ML methods

Table 2. Statical analysis on VR effect on online education

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Figure 2. User activity and experiential learning in our model

Independent sample t-test was adopted to analyse all data completed in SPSS software. Suppose the P-value of significance is greater than 0.05 in Levene's test for equality of variances. This means VR boosts student happiness, because of its high devotion. Student satisfaction with their education experience is directly correlated to the qualities of their teachers. Thus, instructor quality stands as an extremely important factor to consider [18]. VR is crucial to the smoothness of course teaching, and enables students to do better academically. In the presence of VR, students become happier, and the learning process is improved. Students are more likely to be satisfied, if the instructor takes the time to get to know them and their needs, with the aid of VR. Thus, it was concluded that the quality of the teacher has a major impact on student satisfaction (Table 3).

During the analysis of test data, three conditions were considered as errors: a single error, a single record, and several mistakes [19]. The failure of depositing parts in the designated tray is noted as a task error.

Table 3. Results of independent sample t-test

In this study, six variables (categories) were identified through the exploratory investigation [20]. For the first category, the following statements were explored: the teacher communicated well; the instruction is passionate about VR application in online education; the instructor is concerned about student learning.

For the second category, the following statements were explored: the course is organized using VR; the course design allows assignments to be completed in various learning contexts; the teacher guided the course well.

For the third category, the following statements were explored: The teacher answered swiftly to my inquiries concerning the usage of Webinar; the instructor reacted promptly to my queries about the general requirements of the course.

In the category of student expectations, the following statements were explored: The teacher gave models that clearly express expectations for weekly group tasks; the instructor uses good examples to illustrate statistical concepts…

Table 4. T-test results analysis

Table 5. Model summary and parameter estimates

When it comes to the category of student happiness, the following comments were noticed: The online classes were beneficial; Overall, I am delighted with the quality of this course… [21].

For the last category, the following statements were explored: The online classes have polished my analytic abilities; online classes genuinely attempt to get the best out of all students…

69.8% of the total variation was explained by these six variables [22].

4.1 One sample test

VR is frequently adopted to enhance the classroom experience in the traditional education system. To distinguish novel items in VR is known as novelty recognition [23]. Object identification is a common task among animals. If a mouse is placed in two comparable environments for 5min, one of the environments would be changed to include a new or unique item. It is expected that the mouse would spend more time examining the unknown object. Since mice are inherently interested, the percentage of time spent examining the novel object compared to a familiar object can be calculated by:

Time Spent for identifying the Familiar Object using Virtual

Reality $=\frac{\text { Time }=\text { Spent Investigating }}{\text { Time } \text { Spent Investigating+Novel Object }} \times 100$          (6)

If a student spends the same amount of time exploring each object [24], the outcome would be 50% using the same formula. Thus, the null hypothesis for this test was compared to the standard of 50%. A score below 50% suggests that the students recognized the novel object, but preferred the familiar one to the novel one. This might be associated with the human inclination for familiarity. Our goal is to judge whether the mean sample score varies substantially from the predicted value of 90%, using the four-step hypothesis test, which is discussed in the preceding section. That is, the goal is to determine whether the population mean varies from that of the sample mean:

H0: The mean percentage of time spent examining the novel item is 90%, as predicted by chance, when students are given the option to explore a novel and familiar object in the virtual classroom [25].

H1: If a student is given the option to explore a novel and a well-known object, the novel object often receives a smaller share of his/her attention.

Table 4 demonstrates the correlations between teacher quality, feedback timeliness, course design, student expectation, student happiness, and student performance. The first four factors had a positive correlation between them. The student happiness in online classes is positively correlated with the quality of the teacher (SE = 0.706, t-value = 24.196; p 0.05) [26], which validates H0. The course design exhibited a positive correlation with student satisfaction (SE = 0.064, t-value = 2.395; the correlation is statistically significant), which backs up H1 [27]. In addition, feedback timeliness promotes student happiness in online courses (SE = 0.067, t-value = 2.520; p=0.05), which also supports H1. Finally, student expectation has a positive correlated with student happiness (SE = 0.149, t-value = 5.127; p = 0.05) [28]. Thus, H1 was found to be correct.

The semi-supervised training approach suggested in the article was contrasted with the supervised training, with different features and feature combinations [29, 30]. As shown in Table 5, the proposed approach met all three criteria given here.