The advancement in the field of artificial intelligence (AI) has revolutionized every field of life, including the learning of second languages. These intelligent devices are capable of effectively and efficiently utilizing the time and energy of both learners and teachers. Students can learn at their own pace and at their own skill level. They can learn and practice in an interactive and fruitful environment thanks to intelligent chatbots and voice assistants. Students no longer require humanized teachers as a result of the use of these new methodologies; instead, they can learn more effectively by interacting with computer‐assisted systems. With the integration of information and communication technology (ICT) and AI, new technologies like computer‐assisted language learning (CALL) and mobile‐assisted language learning (MALL) are playing a very crucial role in the learning of the English language. Due to the various AI‐based applications and technologies available, learners are unable to use the most valuable and effective ones. This paper focuses on the role of AI in the learning of the English language. The study will help learners in the selection of efficient and effective AI‐powered paradigms for the teaching and learning process of the English language. Various features have been selected from the identified ones, and then, on the basis of these features, different AI‐grounded paradigms for English learning are ranked using analytical hierarchy process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The alternative with the highest performance value is ranked at the top of all available alternatives, while the one with the lowest performance score is placed at the last.

The main symptoms of COVID-19 are high temperature, throat infection, and irregular heartbeat. An integrated wearable device has been presented in this paper for the measurement of temperature and heartbeat in real time using different sensors and NodeMCU ESP8266. For temperature, the DHT11 sensor is used and, for heartbeat, the pulse sensor is used. After reading the data from the sensors processed by NodeMCU ESP8266, it is sent to the firebase database using wireless connection (Wi-Fi module). From the database, the data are displayed in an android application. On the basis of certain conditions of the data, the user as well as the administrator is notified regarding the user’s current health. For the social distancing, an ultrasonic sensor is used. The sensor will warn the user, if he/she is in close contact with someone within a specified distance. The user’s current location is also tracked using the location services of android. A module named COVID-meter, based on the disease.sh-Open Disease Data API, was also included in the research for reading of real-time data of different countries related to COVID-19 like total cases, total deaths, total recovered patients, and so on. The proposed device can be used in both populated and rural areas, but in rural areas it will be much more important because people are unable to reach a doctor on time; thus, they can check their health conditions remotely using the proposed device.