Research Title: Workplace violence in Jordanian hospitals: sociodemographic and occupational characteristics influences, effect on quality of care and recommended solutions

Author: Audi Naji Khaled Al Smadi, Published Year: 2025

Journal of Health Organization and Management,

Faculty: Business

Abstract: Purpose – Workplace violence (WPV) negatively affects the well-being of healthcare workers (HCWs) and the healthcare system. This study explores how sociodemographic factors impact WPV prevalence, its effect on the quality of care, and possible solutions to decrease WPV in Jordanian hospitals. Design/methodology/approach – This study employs a retrospective cross-sectional design, using a webbased survey of 651 HCWs. The data were analyzed using descriptive statistics, logistic regression, an independent sample t-test, and path analysis. Findings – Around 651 HCWs participated in this study, and only 316 revealed they experienced WPV. Multivariate logistic regression analysis confirmed the significance of occupation, sector, and education level in influencing WPV. The result revealed a significant reduction in the quality of care of HCWs exposed to WPV. HCWs shared valuable recommendations to decrease WPV in their workplace. The recommendations were categorized under seven main areas: cultural issues, legal and legislative, administrative measures, internal organization arrangement, quality of health care services, physical working environment, and finally, training and awareness. Practical implications – Hospital management and policymakers should consider factors like local culture, legal and administrative measures, policies, healthcare quality, work environment, and community awareness when addressing WPV. Moreover, healthcare workers can offer valuable recommendations to reduce WPV prevalence and impact in healthcare settings. Originality/value – This is a unique study that explores the relationship between WPV and quality of care in Jordanian hospitals. It considers sociodemographic and occupational characteristics as contributing factors to WPV in hospitals and seeks realistic solutions from the victims’ perceptions.

Keywords: Workplace violence, Quality of care, Hospital, Jordan

Research Title: An overview of teaching AI literacy to university students

Author: Lamis Al-Qoran, Published Year: 2024

Faculty: Information Technology

Abstract: Although artificial intelligence (AI) is increasing in influence across different fields, the subject of AI literacy in higher education (HE) remains inadequately explored. Through a systematic review of 19 peer-reviewed journal articles, this chapter fills that gap. The findings of this study highlight the advancements in AI literacy education across different subject disciplines. It emphasizes the importance of critical thinking, ethical reasoning, and practical skills in managing the increasingly complicated world driven by AI.

Keywords: AI literacy education, Ethical AI, AI curriculum design, AI in higher education, Impact of AI literacy, AI and students with disabilities

Research Title: The Role of User Experience in Software Sustainability Designing for Longevity

Author: Lamis Al-Qoran, Published Year: 2025

2025 16th International Conference on Information and Communication Systems (ICICS), Jordan University of Science and Technology

Faculty: Information Technology

Abstract: User experience (UX) design plays a key role in ensuring the application's long-term sustainability by improving maintainability, usability and to adapt in continuously evolving digital environments. UX design has been shown to improve user engagement and satisfaction. It also reduces technical cost by reducing unconventional redesigns. Poor UX design can lead to abandoning the program as the user is directed towards easier alternatives, which increases the cost of maintenance. This paper investigates the role of UX design in the production of software systems that live over the long term and to explain its contribution to the focus on the user. This paper aims to examines the role of UX design in the long-term production of software systems and explain its contribution to user centeredness, iterative design processes, simple interfaces and customized interfaces that are supported by artificial intelligence in increasing software longevity and operating efficiency. Our research brings together current studies and best practices, reviews the comparison of successful user experience design with unsustainable software, and focuses on the benefits of investing in UX design. We concluded that UX practices significantly improve maintainability, retain users, and extend the life of the software. The study highlights the importance of incorporating sustainability into UX design, as this produces more scalable and future-proof software products. Index Terms—UX Design, Software Sustainability, Long-Term Viability, Technical Debt, Iterative Design, Minimalism, AI- Driven Personalization

Keywords: Costs , Production , Software systems , User experience , Maintenance , Iterative methods , Sustainable development , Usability , Artificial intelligence , Software development management

Research Title: Challenges and Benefits of Online Learning in Master's Programs: An Observational Qualitative Study

Author: Lamis Al-Qoran, Published Year: 2025

2025 16th International Conference on Information and Communication Systems (ICICS), Jordan University of Science and Technology

Faculty: Information Technology

Abstract: Online learning has become a popular learning model in the Jordanian higher education, especially during the COVID-19 pandemic. While numerous studies have investigated online learning effectiveness in various contexts, the unique challenges and benefits for master's students remain insufficiently studied. This study addresses this gap by exploring master's students' experiences with online learning, including emotional, technical, and management difficulties, as well as benefits such as flexibility, convenience, and skill development. We utilize a triangulated qualitative approach that combines observations, interviews, and a focus group approach. Thematic analysis was employed and it provided six themes, including fragmented learning, reliance on recorded lectures, work integrated learning, emotional isolation, time management, and transition anxiety. Our research provides recommendations to educators and educational institutions, such as having adaptive course structures, virtual peer communities to enhance the interaction between the students, and educators' training to address these challenges and optimize online learning for graduate students. These recommendations have the potential to help in improving the quality and effectiveness of online learning in master's programs. Therefore, our preliminary results pertain exclusively to the university and field of study, which restricts their relevance to other institutions or disciplines. Our future research will benefit from quantitative research to generalize our findings by expanding the sample size and including diverse set of learners.

Keywords: Online Learning , graduate students , Master's students , higher education

Research Title: Neural Network-Based Topography Prediction for Dam-Break Events

Author: Lamis Al-Qoran, Published Year: 2025

2025 16th International Conference on Information and Communication Systems (ICICS), Jordan University of Science and Technology

Faculty: Information Technology

Abstract: This study applies the shallow water equations to simulate dam-break events and predict both bed topography and Manning's roughness coefficient. A well-balanced finite volume method is used to generate a dataset that captures the complex flow dynamics of dam failures. This dataset is then utilized to train a neural network capable of predicting terrain changes and roughness distribution after the event. The performance of the model is assessed using Root Mean Square Error (RMSE) to ensure accurate predictions. The findings demonstrate that integrating computational hydraulics with neural networks can enhance the prediction of post-event topography and hydraulic roughness. This approach provides valuable insights for flood modeling, hydraulic engineering, and infrastructure planning, contributing to more effective risk assessment and decision making in real-world scenarios.

Keywords: Computational modeling , Neural networks , Hydraulic systems , Surfaces , Predictive models , Mathematical models , Planning , Computational efficiency , Floods

Research Title: Heart Failure Early Prediction Using Machine And Deep Learning Algorithm

Author: Lamis Al-Qoran, Published Year: 2025

Fusion: Practice and Applications (FPA), 18(1)

Faculty: Information Technology

Abstract: In this article, we use machine learning approaches to give a thorough investigation into the prediction of cardiac illnesses and strokes. The Stroke Prediction Dataset and the Heart Failure Prediction Dataset are the two datasets that we use. Our objective is to maximize accuracy and minimize Mean Absolute Error (MAE) and Mean Squared Error (MSE) in order to enhance predictive performance. We use a variety of machine learning methods, such as Random Forests, Naive Bayes, Decision Trees, and k-Nearest Neighbors (KNN). We also use Artificial Neural Networks (ANN) and Multi-Layer Perceptrons (MLP) as deep learning models. We use oversampling approaches to rectify the imbalance in classes. For hyperparameter tweaking, we also use Grid Search and k-Fold Cross Validation. Our goal is to deliver valuable insights into early detection and preventive measures through comprehensive testing and assessment for prevention of strokes and heart diseases.

Keywords: Heart Disease; Machine learning; Deep learning; Multi layer perceptron; Model evaluation

Research Title: Educators’ Perceptions on Artificial Intelligence in Higher Education: Insights from the Jordanian Higher Education

Author: Lamis Al-Qoran, Published Year: 2025

,

Faculty: Information Technology

Abstract: This paper investigates educators’ perceptions of the application of Artificial Intelligence in Higher Education (AIHEd) and its benefits and concerns within the Jordanian higher education. Like in other contexts, the adoption of Artificial Intelligence (AI) in the Jordanian higher education brought many benefits and a variety of concerns. Due to the lack of regulations and clear policies to cope with such new technologies, the increasing prevalence of these concerns has a negative impact on academic integrity. We used a sequential exploratory mixed approach to accomplish our study, which is guided by the Technology Acceptance Model (TAM), which helps in analysing the adoption of AI in higher education. Our approach involves conducting interviews with university educators from three different Jordanian universities. Interviews were done to identify educators’ thoughts regarding the responsibility of universities to adopt new AI technologies, what motivates them to use AI tools and services in their daily work, whether using AI in higher education institutions is legitimate, and the concerns associated with implementing such technologies into practice. Thus, the paper tries to portray the acceptable benefits and concerns of using AI in Jordanian higher education institutions. After conducting a thematic analysis on 18 interviews with educators, we identified 10 corresponding benefit themes and 8 corresponding concern themes that resulted from the coding and theme-building process. The average rate of educators’ responses to the themes of benefits and concerns is then determined by distributing a questionnaire to 145 higher education educators to generalise the results. Although our findings offer valuable insights, further investigation in wider contexts may be necessary to ensure the representativeness and generalisability of the findings. Through the themes that the study outlined, we concluded that although AI can transform the way students learn and educators work, there are still several issues that need to be resolved by researchers and teachers who work with associated application systems. Such issues require greater emphasis on appropriately and logically handling related ethical dilemmas. These concerns also highlight the importance of developing the necessary strategies and skills for responsible AIHEd. Using a mixed approach helped us to develop a strong understanding of the current state of AIHEd in the Jordanian context.

Keywords: Artificial Intelligence (AI), higher education, educators, perceptions, developing countries, Artificial Intelligence in Higher Education (AIHEd)

Research Title: Designing Inclusive and Adaptive Content in Moodle: A Framework and a Case Study from Jordanian Higher Education

Author: Lamis Al-Qoran, Published Year: 2025

Multimodal Technologies and Interaction, 9(6)

Faculty: Information Technology

Abstract: Blended learning has introduced a more accessible and flexible teaching environment in higher education. However, ensuring that content is inclusive, particularly for students with learning difficulties, remains a challenge. This paper explores how Moodle, a widely adopted learning management system (LMS), can support inclusive and adaptive learning based on Universal Design for Learning (UDL) principles. A 16-week descriptive exploratory study was conducted with 70 undergraduate students during a software engineering fundamentals course at Philadelphia University in Jordan. The research combined weekly iterative focus groups, teaching reflections, and interviews with 16 educators to identify and address inclusion barriers. The findings highlight that the students responded positively to features such as conditional activities, flexible quizzes, and multimodal content. A UDL-based framework was developed to guide the design of inclusive Moodle content, and it was validated by experienced educators. To our knowledge, this is the first UDL-based framework designed for Moodle in Middle Eastern computing and engineering education. The findings indicate that Moodle features, such as conditional activities and flexible deadlines, can facilitate inclusive practices, but adoption remains hindered by institutional and workload constraints. This study contributes a replicable design model for inclusive blended learning and emphasizes the need for structured training, intentional course planning, and technological support for implementing inclusivity in blended learning environments. Moreover, this study provides a novel weekly iterative focus group methodology, which enables continuous course refinement based on evolving students’ feedback. Future work will look into generalizing the research findings and transferring the findings to other contexts. It will also explore AI-driven adaptive learning pathways within LMS platforms. This is an empirical study grounded in weekly student focus groups, educator interviews, and reflective teaching practice, offering evidence-based insights on the application of UDL in a real-world higher education setting.

Keywords: inclusive blended learning; Moodle; learning management system; universal design for learning; educational technology; students with disabilities; higher education; flipped classroom model

Research Title: Insights into in silico analysis to explore the multitarget antidepressant role of Camellia sinensis

Author: Balakumar Chandrasekarn, Published Year: 2025

Journal of Biomolecular Structure and Dynamics, 43

Faculty: Pharmacy

Abstract: Depression is the fourth leading cause of death due to suicides every year according to WHO. Various adverse effects are associated with many of the available antidepressants due to the irreversible nature of these drugs. So, it is worthwhile to explore the natural phytoconstituents as an alternative therapy for the treatment of depression-dependent symptoms. Computational chemistry provides a cost-effective method to explore or develop new therapies for various diseases through in silico studies. In this study, multitargeting antidepressant potential of Camellia sinensis is explored via docking and binding interaction studies with monoamine oxidase-A enzyme, serotonin, and dopamine receptors involved in depression as targets. All the selected phytoconstituents were evaluated for drug-likeliness properties using Swiss ADME. Among all the selected phytoconstituents, Theasinensin, and Theaflavin-3-gallate were found to have best affinities with all the selected targets under investigation and can be considered as promising lead molecules for the development of novel antidepressants. Molecular dynamics simulations assessed the binding affinity of four compounds to Human Monoamine Oxidase A. All compounds showed potential, with Theaflavin-3-gallate and Theasinesin displaying the strongest binding. This suggests their potential for modulating enzyme activity and potential relevance in depression treatment.

Keywords: Depression; tea; MAO-A; multitargeting; plants; suicide; molecular docking; molecular dynamics; mental illness; health; thea sinensin

Research Title: Microwave-assisted Green Synthesis: An Approach for the Development of Anti-tubercular Agents

Author: Balakumar Chandrasekarn, Published Year: 2025

Current Drug Targets, 27

Faculty: Pharmacy

Abstract: Tuberculosis (TB) is a serious infectious disease that primarily affects the lungs but can also spread to the brain and spine. The highly pathogenic bacteria that causes TB is called Mycobacterium tuberculosis (Mtb). Usually, when an infected person coughs, sneezes, or speaks, the disease spreads through the air. TB is treatable with antibiotics, but it requires a long course of treatment, usually 6 to 9 months to eliminate the bacteria and prevent drug resistance. Thus, developing novel anti-tubercular therapeutics with various structural classes is necessary to solve the problems brought on by strains that are resistant to several currently available therapies. Resistance to widely used anti-tubercular drugs is increasing daily. As a result, continuing medication therapy is necessary to stop more microbial infections. However, it leads to treatment resistance, which increases the likelihood that the disease may resurface in immune-compromised patients. Several anti-tubercular medications with various molecular structures show appropriate anti-tubercular action against Mycobacterium TB strains that are drug-sensitive and drug-resistant. Compared to conventional synthetic methods, synthetic reactions can be carried out more effectively and selectively under simple reaction conditions by employing microwave radiation. Microwave-assisted organic synthesis (MAOS) is a useful method for increasing product yield and selectivity while accelerating the reaction rate for different types of organic synthesis. Several lead compounds with anti-tubercular properties that were synthesized using the microwave irradiation (MWI) approach are discussed in the current work.

Keywords: tuberculosis ; Microwave ; green chemistry ; infection ; drug ; heterocycles ; synthesis