This chapter focuses on the applications of robotics in the field of education. It discusses the scope for growth and improvement in the models of the robots currently in use. It deals with the current scenario regarding the implementation of a thought that can help in making the future of learning efficient, effective, and interesting. It also allows insight on the role of teachers in integrating this concept in the classrooms. It describes the future of merging AI in the regular classroom to make the process of teaching and learning easier and enjoyable.

Abstract:Computer dependent communication is based on a very important factor, which is Authentication. Human face recognition is an important branch of biometric verification and has a wide range of application. In this paper we describe a method for Automated Attendance System which is based on face recognition by Markov Random Field algorithm. The system describes a method for marking the student's attendance using proposed algorithm and also provides additional features like maintaining the student database and required information about them. There are many advantages of this system over traditional method for attendance. It eliminates the overhead of manually marking the attendance and reduces the time and efforts. Markov Random Field algorithm is Pose-Invariant that does not require manually selection of facial landmarks or head pose estimation. In order to improve the performance of our pose normalization method in face recognition, we also present an algorithm for classifying whether a given face image is at a frontal or non-frontal pose. Experimental results on different datasets are presented to demonstrate the effectiveness of the proposed approach

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