# Towards Contactless Learning Activities during Pandemics Using Autonomous Service Robots
## 1 Introduction  
With the recent advancements in computer vision and artificial intelligence, service robots are becoming increasingly involved in our daily lives. In the educational field, both students and instructors may benefit from the employment of robots. A robot may help students by acting as a tutor, and it can also help instructors by working as a teaching assistant [1]. One characteristic that makes a service robot promising as a teaching assistant is its ability to move and perform repetitive physical activities [2]. The primary duties of a teaching assistant include exam proctoring, communicating with the students, and delivering tutorials. However, with the widespread COVID-19 pandemic nowadays, people are more concerned about protecting their families, making them less willing to interact with others to avoid virus transmission. With academic institutes adapting to the rules and regulations of social distancing due to the ongoing pandemic, online exams and their proctoring tools have been prevalent. These AI-proctored tests are a combination of AI and human proctors. A video of the student taking the exam is recorded using a webcam, and the AI flags any suspicious behaviors that the test-taker carries out. A human proctor then reviews the recording to verify whether the student was cheating during those flagged periods. Cheating events mainly include the student's face missing from the frame, multiple faces detected, andan internet interruption. One of the limitations of such systems is that they only use the candidate's face to trigger flags. Some academic institutions have reverted to using eye-tracking equipment to combat cheating in exams further. The Eye Tribe is a device that can track eye movements and identify times at which the candidate's face was looking outside of the screen [3]. However, this solution is considered costly and difficult to implement on a large scale since all students will be required to have the tracking device set up in their testing area. Some advancements are also being made by using pose estimation techniques to detect cheating while taking online tests [4]. This allows for the detection of the head and the tilting of the head and the limbs of the test-taker. The different variations of the head poses can also indicate whether cheating has occurred or not. Once an exam is over, the motor would typically be required to collect the papers from the students to be handed over to the instructor for grading purposes. However, since papers are physical mediums, the coronavirus can be transmitted by simply touching or handling them. Therefore, scanning exam papers could be one of the solutions to avoid the exchange of papers. Several approaches in the literature propose scanning approaches. Scanning of documents using vision alone without dedicated hardware presents a few challenges to be solved. One of them is correctly detecting the borders or the corners of documents in the images, as described in [5]. Whether it is to fix the skew or properly extract the document page from an image, detecting the edges of a document is essential. Another problem is that when an image of a document is taken in an uncontrolled environment, it is inevitable that the paper will be skewed, which cannot be fixed with a simple crop. Ref. [6] describes a solution to this problem using Progressive Probabilistic Hough Transform. Ref. [7] outlines a \"click-less\" system where a video of the document to be scanned is recorded, and pages are then extracted and scanned without the need for any input from the user, saving time and effort. A service robot can be used to display content based on the student's need to support students. Many companies use recommendation engines for many different types of content recommendations. For instance, they are used in the real world by services such as MovieLens for movie recommendations and social media sites such as Pinterest to recommend posts.