Smart home technology implementation remains an essential aspect of Internet of Things (IoT). It provides needed living support and convenience for elderly people in society. Despite the remarkable achievements in smart home monitoring technology studies, a systematic literature review (SLR) on smart home technology implementation is lacking. There is a limited number of SLR studies on smart home monitoring technology. Therefore, the current study assesses the literature to collect the evidence regarding studies on smart home monitoring technology implantation. An SLR method involving a manual search was applied to review the articles published from January 2010 to December 2019. To carefully classify these articles accordingly, we applied certain quality assessment criteria. Out of 73 relevant primary studies, only 3% were identified to have applied comprehensive SLR guidelines. Seven percent of the relevant studies were identified to have applied the SLR guidelines in a strongly moderately acceptable manner, but they were not completely comprehensive, whereas 8% of the primary studies applied the SLR guideline in a fairly acceptable manner. This manuscript therefore encourages researchers working on SLR studies on smart home technology to apply comprehensive SLR guidelines that takes into account the quality standard.

In recent years, smart home technologies have offered opportunities for elderly people to manage their daily health-related activities. Despite the advancement in smart home technology (SHT), the level of end-user acceptance among elderly people is still low. This study proposes an SHT framework by examining the determinants of elderly behavioural intention (BI) to use smart home technologies by extending the Unified Theory of Acceptance and Use of Technology (UTAUT) model. This study uses the quantitative approach to survey about 486 elderly people in Saudi Arabia, and it applies the Partial Least Square Structural Equation Model (PLS-SEM) technique to perform the data analysis. Findings reveal that culture influence and technology awareness are significant factors in determining the BI to use SHT among elderly people. The study also finds that attitude mediates the relationships between performance expectancy, effort expectancy, and behavioural intention. We find that region and education moderate the relationships between culture influence, technology awareness, and behavioural intention. This study theoretically extends the UTAUT theory by including external constructs: culture influence, technology awareness, attitude, education, and region.

The incorporation of Artificial Intelligence (AI) into medical services in Saudi Arabia offers a substantial opportunity. Despite the increasing integration of AI techniques such as machine learning, natural language processing, and predictive analytics, there persists an issue in the thorough comprehension of their applications, advantages, and issues within the Saudi healthcare framework. This study aims to perform a thorough systematic literature review (SLR) to assess the current status of AI in Saudi healthcare, determine its alignment with Vision 2030, and suggest practical recommendations for future research and policy. In accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, 699 studies were initially obtained from electronic databases, with 24 studies selected after the application of established inclusion and exclusion criteria. The results indicated that AI has been effectively utilised in disease prediction, diagnosis, therapy optimisation, patient monitoring, and resource allocation, resulting in notable advancements in diagnostic accuracy, operational efficiency, and patient outcomes. Nonetheless, limitations to adoption, such as ethical issues, legislative complexities, data protection issues, and shortages in worker skills, were also recognised. This review emphasises the necessity for strong ethical frameworks, regulatory control, and capacity-building efforts to guarantee the responsible and fair implementation of AI in healthcare. Recommendations encompass the creation of national AI ethics and governance frameworks, investment in AI education and training initiatives, and the formulation of modular AI solutions to guarantee scalability and cost-effectiveness. This breakthrough enables Saudi Arabia to realise its Vision 2030 objectives, establishing the Kingdom as a global leader in AI-driven healthcare innovation.

The swift advancement of renewable energy technology has highlighted the need for effective photovoltaic (PV) solar energy tracking systems. Deep learning (DL) has surfaced as a promising method to improve the precision and efficacy of photovoltaic (PV) solar tracking by utilising complicated patterns in meteorological and PV system data. This systematic literature review (SLR) seeks to offer a thorough examination of the progress in deep learning architectures for photovoltaic solar energy tracking over the last decade (2016–2025). The review was structured around four research questions (RQs) aimed at identifying prevalent deep learning architectures, datasets, performance metrics, and issues within the context of deep learning-based PV solar tracking systems. The present research utilised SLR methodology to analyse 64 high-quality publications from reputed academic databases like IEEE Xplore, Science Direct, Springer, and MDPI. The results indicated that deep learning architectures, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Transformer-based models, are extensively employed to improve the accuracy and efficiency of photovoltaic solar tracking systems. Widely utilised datasets comprised meteorological data, photovoltaic system data, time series data, temperature data, and image data. Performance metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE), were employed to assess model efficacy. Identified significant challenges encompass inadequate data quality, restricted availability, high computing complexity, and issues in model generalisation. Future research should concentrate on enhancing data quality and accessibility, creating generalised models, minimising computational complexity, and integrating deep learning with real-time photovoltaic systems. Resolving these challenges would facilitate advancements in efficient, reliable, and sustainable photovoltaic solar tracking systems, hence promoting the wider adoption of renewable energy technology. This review emphasises the capability of deep learning to transform photovoltaic solar tracking and stresses the necessity for interdisciplinary collaboration to address current limitations.

The rapid adoption of immersive and adaptive digital technologies is redefining sustainability education, but the mechanisms by which these technologies support perceived sustainability outcomes remain unclear. This paper models the Digital Experiential Learning Ecosystem (DELE), including simulation, AR/VR, gamification, AI personalization, and collaborative digital platforms, as a higher-order construct. It discusses its role in Perceived Sustainability Outcomes through learning engagement. Basing the study on the Stimulus-Organism-Response framework, the study hypothesizes that the digital ecosystem design can be viewed as an environmental stimulus, engagement as the organismic processing state, and Perceived Sustainability Outcomes as the developmental response. The results, obtained using Partial Least Squares Structural Equation Modeling (PLS-SEM), indicate that DELE is positively associated with learning engagement and Perceived Sustainability Outcomes. Learning engagement is found to be the leading mechanism through which digital experiential environments are converted into perceived sustainability outcomes, but a smaller yet significant direct structural relationship also remains. These findings indicate that digital transformation within the education sector creates sustainable value not only through technological sophistication but also through carefully planned engagement-based learning environments that support systems thinking, applied problem-solving, and adaptive readiness to work in multifaceted environments. The research also advances the body of research on sustainability education by developing a model of digital learning as an integrated ecosystem and by explaining the psychological and structural processes of perceived sustainability outcomes.