Introduction: With the rapid advancement of technology, smart technologies have permeated various sectors, including palliative care in healthcare. Based on the available literature, similar studies have elucidated the application of individual and isolated smart technologies in palliative care, with their benefits, and limitations. This research, therefore, focuses on the conceptualisation of various smart technologies and makes holistic recommendations on the effective and efficient use of smart technologies in healthcare to enhance palliative care. Purpose: The purpose of this study is to systematically review the existing literature to conceptualise the role of various key smart technologies in palliative care, and their benefits and make recommendations on how smart technologies can enhance healthcare in palliative care. Methodology: The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines were adopted in this study. Using the keywords: ‘smart technologies’, ‘healthcare’ and ‘palliative care’ (using Booleans AND/OR in different ways, including their synonyms), an exhaustive search was done on four major databases: MEDLINE, PubMed, Scopus and CINAHL. Resultantly, 19 articles met the inclusion criteria and were analysed. Findings: The main results reveal that smart technologies enhance the quality of life and efficiency of palliative care, notably telemedicine, wearable devices, and mobile health apps. They reduce hospital visits and carer stress by improving patient-doctor communication, enabling remote monitoring, and forecasting health outcomes. However, there are challenges such as the digital divide, privacy issues, and the need for comprehensive integration and training into the current frameworks. Conclusion and implications: The study emphasises the significant impact of smart technologies in improving palliative care. It emphasises the importance of considering technological, ethical, and practical factors to ensure that these advancements truly enhance palliative care. This research has important implications for healthcare professionals and policymakers, indicating a need for a patient-centred palliative care model that integrates technology more effectively.

This research explains the perceptions of university students regarding the use of ChatGPT in their learning and research. The research is based on evidence provided in articles that were considered in a systematic literature review using the PRISMA model. The motivation for this study was informed by a realization that there are limited studies that provide a comprehensive understanding of students’ perceptions regarding the use of ChatGPT in learning and research. Many existing studies either focus on educators’ perceptions or isolated cases of empirical studies. It is therefore not clear what factors influence students’ adoption of ChatGPT. This gap was addressed by this study by conducting a systematic literature review to identify factors influencing the adoption of ChatGPT, the positive impact of ChatGPT on students’ education and the concerns raised by students regarding the integration of ChatGPT into academic practice. The research focus was on articles that discuss the students’ perceptions of ChatGPT, all of which were drawn from Google Scholar. In total 32 articles were considered for analysis for this study and in this paper, we present five constructs that influence the adoption of ChatGPT in academic activities. Even though the students’ perceptions of ChatGPT are widely varied, the findings of this research show that students concur that ChatGPT has a positive influence on their learning. The students further highlighted concerns relating to their use of ChatGPT in learning, which could be improved if the tool’s educational effectiveness is to be realized. The contribution of this study is three-fold. First, the findings of this research explain the perceptions of students about using ChatGPT in academic practice. Second, the results of this study inform the university management and policymakers about how to effectively integrate ChatGPT into the education system. Third, the study suggests, recommends, and guides educators who wish to incorporate this new, powerful tool (ChatGPT) into their teaching.

Artificial Intelligence (AI) is swiftly revolutionising higher education, presenting substantial prospects for innovation while concurrently evoking ethical and privacy concerns. These concerns include issues such as intrusive data collection, algorithmic bias, threats to student autonomy, and unequal access to AI-enhanced learning. Without clear guidelines or institutional safeguards, there is a risk that AI systems may reinforce existing social inequalities, compromise student privacy, and erode the human-centred nature of teaching and learning. This research presents an AI framework that is responsive and ethical within the area of higher education. The process involved sixteen in-depth interviews from university students, administrators, lecturers, and IT professionals belonging to three separate universities, with the Technology Organisation Environment model and the sociocultural learning theory being employed. Thematic analysis identified ten critical themes centred around benefits, challenges, applications, responsible use, privacy and data security, ethical considerations, institutional policies and frameworks, training, equity, and sustainable AI use. The results indicate that institutions should take a proactive stance in dealing with these issues and harnessing AI's full potential. The study thus advances the formulation of policies that provide for the equitable distribution of AI technologies, with the accompanying strong emphasis on ethical principles and data security. In preserving academic integrity and enhancing educational processes, this research stresses the need to create collaborative environments among the major stakeholders. Thus, higher education, taking into consideration AI implementations aligned with learner, educator, and administrator interests, thereby offers the promise of navigating the complex terrain of AI integration.

1. Personal information privacy is a growing concern in the digital era (Hallam & Zanella, 2017) and has significant implications for students at universities. To reduce the impact of privacy conce...

Parkinson's disease is a movement disorder within the nervous system that impacts millions of people across the world. The standard diagnostic methods usually miss early subtle signs of disease which has driven research into Machine Learning (ML) and Explainable Artificial Intelligence (XAI) to develop better predictive models. Following PRISMA guidelines we analyzed 13 studies found in IEEE Xplore, PubMed and ACM concerning different ML methodologies for Parkinson’s disease prediction. Deep learning models using vocal and motor data achieve good accuracy but require more clinical trust and transparency due to their opaque "black-box" nature. SHAP and LIME act as XAI solutions that address transparency issues in model predictions by delivering understandable information regarding model outputs to all users. Current solutions show progress. However, there are multiple complications, including limited and unbalanced datasets alongside accuracy-explainability trade-offs which underline the need for extensive datasets, multidisciplinary teamwork and practical validation.