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Introduction: Coronavirus disease 2019 (COVID-19) has had a profound impact globally, causing the death of millions of people and deeply affecting socio-psychological, human health, and economic systems, with some nations bearing a disproportionate burden. Despite obesity having been established as one of the major risk factors of COVID-19 severity and other degenerative diseases, the effects that dietary pattern intake plays in COVID-19 outcomes remain poorly understood. The goal of this study is to look into the connection between eating habits, the number of non-obese and obese people, and COVID-19 outcomes in countries with populations exhibiting normal Body Mass Index (BMI), which is an indicator of obesity. Methods: The analysis includes data from 170 countries. From the 170 countries, we focused on 53 nations where the average, BMI falls within the normal range (18.5 to 24.9). A subset of 20 nations was selected for a more detailed examination, comprising 10 nations with the lowest BMI values within the normal range (18.5-19.8) and 10 nations with the highest BMI values within the normal range (23.5-24.9). We used Artificial Intelligence (AI) and Machine Learning (ML) applications to evaluate key metrics, including dietary patterns (sugar and vegetable intake), obesity prevalence, incidence rate, mortality rate, and Case Fatality Rate (CFR). Results: The results demonstrate a significant correlation between higher obesity prevalence and increased COVID-19 severity, evidenced by elevated incidence, mortality, and CFRs in countries like North Macedonia and Italy. In contrast, nations such as Iceland and New Zealand with well-established healthcare systems revealed low mortality rate and case fatality rate despite variations in dietary habits. The study also revealed that vegetable consumption appears to provide a slight to significant protective effects, suggesting that dietary patters alone do not consistently predict COVID-19 Outcomes. Conclusion: Data generated from this study showed the crucial role of healthcare infrastructure along with the testing capacity and data reporting in influencing the success of pandemic responses. It also highlights the need of integrating public health strategies, which focus on obesity management and improvement of healthcare preparedness. In addition, AI-driven predictive modeling offers valuable insights that may guide pandemic response efforts in the future, thereby enhancing global health crisis management and mitigating the impact of future health emergencies. Keywords: COVID-19; Dietary patterns; Obesity; Artificial intelligence; Machine learning; Public health; Health care systems 

This paper presents an innovative courseware project based on the Adaptive Distributed Learning (ADL) Initiative’s Total Learning Architecture (TLA [1]), which encompasses a technical framework for education and training based on a data strategy built around open standards to support interoperability across diverse organizations and products ([2]). This framework includes definitions of a set of policies, specifications, and standards that enable a future learning ecosystem to facilitate lifelong learning principles promoting personalized and flexible learning environments that include both formal and informal activities [3]. In Fall 2023, a TLA- inspired course framework was implemented in a data visualization course for senior undergraduates and graduate students, using Moodle and a Learning Record Store (LRS) to track over 200,000 learning records. This system allowed instructors to visually monitor online learning activities for the whole class as well as selected individual learners. As future work, the course will expand to 10 STEM courses across 11 universities in the next three years as part of an existing NSF commitment. 

Artificial intelligence (AI) leverages mathematical algorithms to emulate human cognitive abilities, leading to a transformative impact on the education sector. Educators are at the front lines of implementing AI in the classroom. Recent scientific studies demonstrate the capacity of AI, particularly generative models like ChatGPT, to reshape various aspects of education. In a recent study, we showcased that the integration of both artificial intelligence, specifically ChatGPT, and interactive learning activities significantly enhances the engagement levels of STEM students enrolled in a General Biology course. Furthermore, this combined approach not only boosts student engagement but also demonstrates an improvement in their overall performance within the course. Building on preliminary studies, the objective of this review article is to delineate the diverse applications of generative AI in education. To achieve this objective, we conducted a thorough search across scientific databases, including Google Scholar, Science Direct, government websites, and other resources, to collect relevant papers. Our findings underscore the contributions of generative AI, exemplified by ChatGPT, in enabling students to generate innovative text for written assignments, providing personalized feedback, facilitating adaptive learning, enhancing accessibility to education by eliminating barriers for individuals with disabilities, and supporting research endeavors. 

Diabetes mellitus (DM) is a serious chronic metabolic disease that is associated with hyperglycemia and several complications including cardiovascular disease and chronic kidney disease. DM is caused by high levels of blood sugar in the body associated with the disruption of insulin metabolism and homeostasis. Over time, DM can induce life-threatening health problems such as blindness, heart disease, kidney damage, and stroke. Although the cure of DM has improved over the past decades, its morbidity and mortality rates remain high. Hence, new therapeutic strategies are needed to overcome the burden of this disease. One such prevention and treatment strategy that is easily accessible to diabetic patients at low cost is the use of medicinal plants, vitamins, and essential elements. The research objective of this review article is to study DM and explore its treatment modalities based on medicinal plants and vitamins. To achieve our objective, we searched scientific databases of ongoing trials in PubMed Central, Medline databases, and Google Scholar websites. We also searched databases on World Health Organization International Clinical Trials Registry Platform to collect relevant papers. Results of numerous scientific investigations revealed that phytochemicals present in medicinal plants (Allium sativum, Momordica charantia, Hibiscus sabdariffa L., and Zingiber officinale) possess anti-hypoglycemic activities and show promise for the prevention and/or control of DM. Results also revealed that intake of vitamins C, D, E, or their combination improves the health of diabetes patients by reducing blood glucose, inflammation, lipid peroxidation, and blood pressure levels. However, very limited studies have addressed the health benefits of medicinal plants and vitamins as chemo-therapeutic/preventive agents for the management of DM. This review paper aims at addressing this knowledge gap by studying DM and highlighting the biomedical significance of the most potent medicinal plants and vitamins with hypoglycemic properties that show a great potential to prevent and/or treat DM.