Artificial Intelligence Chatbots: A Survey of Classical versus Deep Machine Learning Techniques

Artificial Intelligence (AI) enables machines to be intelligent, most importantly using Machine Learning (ML) in which machines are trained to be able to make better decisions and predictions. In particular, ML-based chatbot systems have been developed to simulate chats with people using Natural Language Processing (NLP) techniques. The adoption of chatbots has increased rapidly in many sectors, including, Education, Health Care, Cultural Heritage, Supporting Systems and Marketing, and Entertainment. Chatbots have the potential to improve human interaction with machines, and NLP helps them understand human language more clearly and thus create proper and intelligent responses. In addition to classical ML techniques, Deep Learning (DL) has attracted many researchers to develop chatbots using more sophisticated and accurate techniques. However, research has paid chatbots have widely been developed for English, there is relatively less research on Arabic, which is mainly due to its complexity and lack of proper corpora compared to English. Though there have been several survey studies that reviewed the state-of-the-art of chatbot systems, these studies (a) did not give a comprehensive overview of how different the techniques used for Arabic chatbots in comparison with English chatbots; and (b) paid little attention to the application of ANN for developing chatbots. Therefore, in this paper, we conduct a literature survey of chatbot studies to highlight differences between (1) classical and deep ML techniques for chatbots; and (2) techniques employed for Arabic chatbots versus those for other languages. To this end, we propose various comparison criteria of the techniques, extract data from collected studies accordingly, and provide insights on the progress of chatbot development for Arabic and what still needs to be done in the future

Commentary: Using Meta-synthesis of Qualitative Research Studies as Evidence in Practice and Policy

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Arabic Educational Neural Network Chatbot

Chatbots (machine-based conversational systems) have grown in popularity in recent years. Chatbots powered by artificial intelligence (AI) are sophisticated technologies that replicate human communication in a range of natural languages. A chatbot’s primary purpose is to interpret user inquiries and give relevant, contextual responses. Chatbot success has been extensively reported in a number of widely spoken languages; nonetheless, chatbots have not yet reached the predicted degree of success in Arabic. In recent years, several academics have worked to solve the challenges of creating Arabic chatbots. Furthermore, the development of Arabic chatbots is critical to our attempts to increase the use of the language in academic contexts. Our objective is to install and create an Arabic chatbot that will help the Arabic language in the area of education. To begin implementing the chabot, we collected datasets from Arabic educational websites and had to prepare these data using the NLP methods. We then used this data to train the system using a neural network model to create an Arabic neural network chabot. Furthermore, we found relevant research, conducted earlier investigations, and compared their findings by searching Google scholar and looking through the linked references. Data was gathered and saved in a json file. Finally, we programmed the chabot and the models in Python. As a consequence, an Arabic chatbot answers all questions about educational regulations in the United Arab Emirates

Evaluation of Intervention Programs for Children with Autism

The present study reviewed the literature about intervention programs for Autism Spectrum Disorder (ASD) among children. ASD is a long-term neurodevelopment disorder that is identified as impairment in the context of social communication and interaction, and the predominance of restricted and repetitive patterns of behavior, interests or activities. Intervention programs have objectives such as to help individuals with ASD, particularly at the early developmental stage. Another objective is to involve the parents of the children with ASD to improve the effectiveness of implementing these interventions. Reviewing the literature showed that the involvement of parents reduced their stress level

Sleep, Dietary Habits, Smoking Status and Physical Activity among Jordanian Nurses

Background: Healthy lifestyle is important in promoting health and reducing risk of chronic diseases. Nurses’ lifestyle could be affected negatively by working night shifts or always rotating shifts, long working hours, and high exposure to work-related stress.  Objectives: This study aims to assess nurses’ lifestyle and factors associated with it.  Methods: A cross-sectional design with an online survey was used in this study. The sample included 203 Jordanian nurses from four hospitals. Sleep quality was assessed using The Pittsburgh Sleep Quality Index, while dietary habits was assessed using the Rapid Eating Assessment for Participants-Shortened Version. Physical activity was assessed using The International Physical Activity Questionnaires.  Results: Nurses’ mean age was 32.7± 21.78 years and on average they have 8.27±5.63 years of experience. Approximately, 25% of nurses were tobacco smokers. The majority of nurses reported poor sleep quality (n = 174, 85.5%). Approximately, 58% of nurses were overweight or obese and 41.9% of nurses had poor dietary habits. Only 39.5% of nurses reported moderate or high levels of activity.  Conclusion: Jordanian nurses’ lifestyle showed poor quality in most aspects.  Implications to nursing: Nurses should be aware of the importance of adopting a healthier lifestyle to prevent possible complications. Nurse leaders should consider the health status of nurses and prevent illnesses by encouraging a healthier lifestyle of nurses

Commentary: Ethical consideration in conducting clinical research among patients with mental health problems in Arab countries

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Enhancing Empathy in Nursing Education: A Feasibility and Usability Study of Virtual Reality-Based Training for Dementia Care

Abstract Introduction: Virtual Reality (VR) is recognized as a versatile training tool across various domains, including healthcare. In Jordan, dementia is a significant public health concern, accounting for 5.17% of all recorded deaths. Given the complex nature of dementia care, preparing nursing students is crucial. This paper explores the feasibility and effectiveness of  VR-based empathy training for nursing students, with a growing global interest in VR training applications, despite the need for further study of VR acceptability in the Jordanian context. Methods: This mixed-methods study included 71 nursing students in a psychiatric course at a university in Jordan. Feasibility was assessed through recruitment, retention, adherence, data completion, and implementation fidelity. Pre- and post-intervention assessments measured empathy levels. Thematic analysis of participant feedback provided insights into usability. Results: Recruitment and retention rates were excellent, with a 100% participation rate and 100% retention. The intervention demonstrated a statistically significant enhancement in empathy scores following the program (M_pre = 50.44, SD_pre = 9.24; M_post = 65.17, SD_post = 8.53), t(71) = -23.89, p < 0.001. The thematic analysis highlighted the importance of a user-friendly interface, a supportive learning environment, and engaging content. Conclusion: This study highlights the potential of VR-based training to enhance empathy among nursing students in the context of dementia care. The robust feasibility outcomes and positive user experiences suggest that VR technology can be a valuable addition to nursing curricula, ultimately benefiting patient care and the nursing workforce. Implications for Nursing:  Integrating VR into nursing education presents the potential for elevating empathy in nursing students, notably within dementia care. This innovative approach equips prospective healthcare providers with vital skills for delivering more compassionate patient care

Social discrimination perception of health-care workers and ordinary people toward individuals with COVID-19

The purpose of this study is to explore perception of social discrimination among ordinary people and health-care workers toward individuals with COVID-19 in Jordan. A cross-sectional descriptive-comparative design was used to collect data from a convenience sample of 272 ordinary people and 109 HCWs utilizing an online survey format. HCWs reported low to medium social discrimination (SDS) level, while ordinary people reported a higher level with statistical difference (t = 8.64, p <.001). SDS had positive and significant correlation with years of experience, specialty of nursing, education and area of working among HCWs. The study signifies the social discrimination associated with COVID-19 among ordinary people and healthcare workers. Implications to health practices and public policies discussed