In this era of digital and modern education, the existence of psychological stress on students cannot be denied. The surplus aggregation of the stress may lead to different problems like a decline in student grade (performance), an increase of violence in behavior, and even more extreme cases. The advent of Information Communication and Technology (ICT) and its tools opened the doors to innovations that facilitate interactions among things and humans. In this utilization, the paper proposes a novel, IoT-aware student-centric stress monitoring and real-time alert generating framework to predict student stress index in a particular context. In elaboration, we respectively used extended VGG16, Bidirectional Long Short Term Memory network (Bi -LSTM), and Multinomial Naïve Bayes techniques to generate the scores of emotions from student facial expressions, speech pitch, and content of student speech at the cloud layer. Specifically, the model aims to classify the stress events as normal or abnormal on basis of the overall emotion of the students' physiological data readings. The activation of the abnormal event in case of higher values for negative emotions like stress, fear, sadness, disgust, etc.; a stern alert is sent to the student, coordinators, and caretakers. This proposed framework will ultimately be a great tool that will support the education institutions, students, their parents, and guardians to get a real-time alert on students' overall emotions. The prior knowledge of stress accumulated on the mind of the student will help in overcoming major problems of student dropout, decrease student academic performance, and tackle the stress situation that may lead to the student attempting suicide.

With the advent of technology and digitization, the use of Information and Communication Technology (ICT) and its tools for the imperative dissemination of information to learners are gaining more ground. During the process of the conveyance of lectures, it is mostly observed that students (learners) are supposed to take notes (minutes) of the subject matter being delivered to them. The existence of different factors like disturbance (noise) from the environment, learner’s lack of interest, problems with the tutor’s voice, and pronunciation, or others, may hinder the practice of preparing (or taking) lecture notes effectively. To tackle such an issue, we propose an artificial intelligence-inspired multilanguage framework for the generation of the lecture script (of complete) and minutes (only important contents) of the lecture (or speech). We also aimed to perform a qualitative content-based analysis of the lecture’s content. Furthermore, we have validated the performance(accuracy) of the proposed framework with that of the manual note-taking method. The proposed framework outperforms its counterpart in terms of note-taking and performing the qualitative content-based analysis. In particular, this framework will assist the tutors in getting insights into their lecture delivery methods and materials. It will also help them improvise to a better approach in the future. The students will be benefited from the outcomes as they do not have to invest valuable time in note-taking/preparation.