# Towards Contactless Learning Activities during Pandemics Using Autonomous Service Robots
## 1 Introduction
As [8] explains, to recommend series and movies to a user, Netflix utilizes a complex recommender system (consisting of a variety of algorithms) that uses information such as what and how each member watches, un-played recommendations in a session, and where a video was found in Netflix itself. A recommender system could also be used in tag-based content discovery. Ref. [9] details how the similarity of users, measured from the tags they used, could be used with the relationships between the tags to recommend content for a user based on the semantic similarity of the tags of that content to that in the user's history. Ref. [10] shows that a recommender system can be used to recommend categories of news articles in real time based on the freshness of content using support vector machines (SVM), which outperforms the use of collaborative filtering. Ref. [11] introduces newer approaches to tag-based recommendations where the recommendation process incorporates tags, particularly higher-order singular value decomposition (HOSVD). Several assistant robots have been proposed to aid in educational activities. Ref. [12] presents IDML tools that can provide teachers with a humanoid robot to assist them in engaging in classroom teaching activities. The results show that robots can enhance children's learning interests and have the potential to help elementary education. The authors of [13] developed a robot as a teaching assistant to offer teachers with the support they needed, and they used the robot teaching assistant to help teachers explain and illustrate two icebreaker games in particular. The findings show that education aided by a robot teaching assistant is more effective in enhancing student knowledge and assisting students in developing more favorable attitudes toward classroom learning activities. Robots can potentially be employed to proctor exams in the educational field. Ref. [14] describes a low-cost, self-contained desktop robot for proctoring exams in online/distance learning courses. The robot is connected to the student's computer through a USB port and uses a webcam, as well as an array of sound sensors, to monitor the examination environment. The exam can be monitored in real time through the Internet by a live proctor, or the data can be recorded for later review. However, one of the drawbacks of such systems is that they are only applicable to online exams and cannot be utilized in paper exams or face-to-face classrooms. To address the needs and limitations of other systems, we propose an autonomous AI and computer vision-based teaching assistant for face-to-face proctoring, contactless paper scanning in examinations, and voice-oriented course material provision to students. The robot initially maps a classroom to navigate via waypoints while avoiding obstacles. Using four cameras that are mounted and set up on the robot at right angles to each other, video footage of the exam session is recorded. The footage is fed into a trained neural network responsible for detecting suspicious activities during the recorded session. Our robot is also capable of interacting with students during the exam. The student can ask a question, and a response is returned that best fits the student depending on the learning style preference stored in their profile and the allowed answers during the exam. Answers to posed questions can be displayed as text or video on a tablet running any web browser. Once an exam session is complete, our robot uses the four cameras to scan four students' multi-page submissions simultaneously and rapidly. Students are asked to quickly flip their submission pages on camera, safely dispose of the paper, and leave the exam room. The main contribution of our work is an end-to-end viable solution to contactless class activities during pandemics.