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Incorporating games into learning a foreign language is an effective strategy that can lower students’ stress and improve their learning. Thus, this study tried to examine the impacts of Kahoot as a game-based learning tool on Iranian EFL learners’ vocabulary recall and retention. To this end, 50 Iranian intermediate learners were chosen for the participants of the study. After that, the participants were randomly assigned to two equal groups, one experimental and one control. Then, the participants’ English vocabulary knowledge was measured by administering a vocabulary pretest. After pretesting process, the participants in both groups received the treatment in two different ways. In the EG, the new vocabulary items were taught using the Kahoot game, while in the CG, the new ones were taught using traditional instruction. After finishing the treatment course, which lasted ten sessions, the two groups took the immediate vocabulary posttest. After three weeks, a delayed vocabulary posttest was administered to both groups to determine the effects of Kahoot on their vocabulary retention. The results of paired samples and independent samples t-tests depicted that there were significant differences between the immediate and delayed posttests of the EG and the CG in favor of the EG. The implications of this study can tell the teachers that teaching through game-based learning tools may produce better results than teaching through traditional methods. Also, the implications of this study can encourage students to practice and learn English even outside of the class context.

Expert system (ES) is a branch of artificial intelligence (AI) that is used to manage different problems by making use of interactive computer-based decision-making process. It uses both factual information and heuristics to resolve the complicated decision-making issues in a specific domain. The architecture of the expert system was analyzed and found that it includes several parts such as user interface, knowledge base, working memory, inference engine, explanation system, system engineer, and knowledge engineer, user, and expert system shell in which each part of the architecture of an expert system is based on different functionary that helps it to make an adequate decision by analyzing complex situations. The research aims to analyze the application of expert systems or decision-making systems in the field of education and found that it is used for different purposes such as assessing teacher performance, providing guidance to the students regarding their career, and providing quality learning to students with disabilities. It is also used to help the students to make rightful career decisions and become efficient professionals after completing their studies.

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The usage of Artificial intelligence in medical arena has proved to be a game changer in the detection and diagnosis of several medical conditions. In the current digital era, children with stressful medical issues are suffering from Deep Obsessive-Compulsive Disorder (DOCD). This kind of mental stress occurs in children because of the continuous usage of gadgets such as mobile phone, playing games using play stations, watching videos on tablets, etc. In most of the possibilities, single children are the ones affected with several obsessions such as stubborn activities, fighting for selfish priorities and so on. In medical terms, these kinds of complex behavioral changes are identified as DOCD. Genetic behaviors sometimes in a few group of children are also noticed as a modality difference. As symptoms are psychiatric impairment, such a child remains isolated, abnormal silence, being obsessive and repeating irrelevant words, high stress or anxiety. All medical challenges could be treated as healthcare research metrics and the gradual increase in DOCD disorder among children of this generation can be considered too. Early detection of DOCD is essential as it can help in early diagnosis but techniques to do so is unavailable currently. Deep learning-an artificial intelligence method can be utilized to detect DOCD, diagnose and treat it and bring about a positive character in children. Behavior changes in children can be classified and detected using transfer learning algorithms. In COVID-19 pandemic situation, 3% of DOCD has increased to 10-15% as a disorder. This information is retrieved from children by monitoring negative activities, unusual behavior such as nail biting, removing spectacles and placing them in the wrong place, watching tablets, mobile phones and television for more hours. Using Convolutional Neural Networks (CNN), input such as MRI (Magnetic resonance Imaging) is used for experimenting the variations in behavior with the high dimension that are analyzed from the image dataset. Using Transfer Learning with Inception V3-, CNN generalization of misophonia level can be statistically analyzed to avoid overfitting problems. By employing AI techniques, the aggression level can be predicted using data augmentation method with better accuracy and a low error rate than the existing systems. In the research it is observed that using the model employing Inception-V3 transfer learning CNN a better prediction of aggression levels can be achieved in comparison to the existing CNN model used.