The rapid improvement of artificial intelligence (AI) in the educational domain has opened new possibilities for enhancing the learning experiences for students. This research discusses the critical need for personalized education in higher education by integrating deep learning (DL) techniques to create customized learning pathways for students. This research intends to bridge the gap between constant educational content and dynamic student needs. This research presents an AI-driven adaptive learning platform implemented across four different courses and 300 students at a university in Faisalabad-Pakistan. A controlled experiment compares student outcomes between those using the AI platform and those undergoing traditional instruction. Quantitative results demonstrate a 25 % improvement in grades, test scores, and engagement for the AI group, with a statistical significance of a p-value of 0.00045. Qualitative feedback highlights enhanced experiences attributed to personalized pathways. The DL analysis of student performance data highlights key parameters, including enhanced learning outcomes and engagement metrices over time. Surveys reveal increased satisfaction compared to one-size-fits-all content. Unlike prior AI research lacking rigorous validation, our methodology and significant results deliver a concrete framework for institutions to implement personalized, AI-driven education at scale. This data-driven approach builds on previous attempts by tying adaptations to actual student needs, yielding measurable improvements in key outcomes. Overall, this work empirically validates that AI platforms leveraging robust analytics to provide customized and adaptive learning can significantly enhance student academic performance, engagement, and satisfaction compared to traditional approaches. These findings have insightful consequences for the future of higher education. The research contributes to the growing demand for AI in education research and provides a practical framework for institutions seeking to implement more adaptive and student-centric teaching methodologies.

Urban Heat Islands (UHIs) and Urban Cool Islands (UCIs) can be measured by means of in situ measurements and interpolation methods, which often require densely distributed networks of sensors and can be time-consuming, expensive and in many cases infeasible. The use of satellite data to estimate Land Surface Temperature (LST) and spectral indices such as the Normalized Difference Vegetation Index (NDVI) has emerged in the last decade as a promising technique to map Surface Urban Heat Islands (SUHIs), primarily at large geographical scales. Furthermore, thermal comfort, the subjective perception and experience of humans of micro-climates, is also an important component of UHIs. It remains unanswered whether LST can be used to predict thermal comfort. The objective of this study is to evaluate the accuracy of remotely sensed data, including a derived LST, at a small geographical scale, in the case study of King Abdulaziz University (KAU) campus (Jeddah, Saudi Arabia) and four surrounding neighborhoods. We evaluate the potential use of LST estimates as proxy for air temperature (T_air) and thermal comfort. We estimate LST based on Landsat-8 measurements, T_air and other climatological parameters by means of in situ measurements and subjective thermal comfort by means of a Physiological Equivalent Temperature (PET) model. We find a significant correlation (r = 0.45, p < 0.001) between LST and mean T_air and the compatibility of LST and T_air as equivalent measures using Bland-Altman analysis. We evaluate several models with LST, NDVI, and Normalized Difference Built-up Index (NDBI) as data inputs to proxy T_air and find that they achieve error rates across metrics that are two orders of magnitude below that of a comparison with LST and T_air alone. We also find that, using only remotely sensed data, including LST, NDVI, and NDBI, random forest classifiers can detect sites with “very hot” classification of thermal comfort nearly as effectively as estimates using in situ data, with one such model attaining an F1 score of 0.65. This study demonstrates the potential use of remotely sensed measurements to infer the Physiological Equivalent Temperature (PET) and subjective thermal comfort at small geographical scales as well as the impacts of land cover and land use characteristics on UHI and UCI. Such insights are fundamental for sustainable urban planning and would contribute enormously to urban planning that considers people’s well-being and comfort.

Waste management poses a major challenge for cities worldwide, with significant environmental, economic, and social impacts. This paper proposes a novel waste management system leveraging recent advances in the Internet of Things (IoT), algorithms, and cloud analytics to enable more efficient, sustainable, and eco-friendly waste collection and processing in smart cities. An ultrasonic sensor prototype is tailored for reliable fill-level monitoring. A LoRaWAN and cellular network architecture provides city-wide connectivity. A cloud platform handles sensor data storage, processing, and analytics. Dynamic route optimization algorithms minimize time, distance, and fuel use based on real-time bin data. Extensive pilot studies in 10 different locations across Lahore, Pakistan, validated the system, processing over 200 million data points. The results showed a 32% improvement in route efficiency, a 29% decrease in fuel consumption and emissions, a 33% increase in waste processing throughput, and 18% vehicle maintenance savings versus conventional practices. This demonstrates quantifiable benefits across operational, economic, and sustainability dimensions. The proposed IoT-enabled waste management system represents a significant advancement towards sustainable and ecologically responsible waste practices in smart cities worldwide. This research provides a replicable model for holistic smart city solutions integrating sensing, algorithms, and analytics to transition civic operations towards data-driven, efficient paradigms. It represents a significant advancement in sustainable waste practices for smart cities worldwide. Further work could apply emerging technologies like automation and artificial intelligence to create waste management 3.0.

In the era of modernization, urban green spaces (UGSs) are attracting increasing attention in smart city (SC) development. There is currently a paucity of UGSs, and their importance has become crucial in enhancing life expectancy and health. To meet people’s demands, scientists are busy designing smart cities under flagship programs for urban transformation. UGSs are relevant in the analysis and investigation of improved lifestyles. The scarcity of UGSs can have many social and physical impacts. The presence of UGSs in smart cities is proposed to improve residents’ lifestyles. Semi-structured interviews were conducted with residents, officials and government experts to inform the development of UGSs further. The result of the surveys analyzed using the MAXQDA software, which presents the importance of SC and the residents’ satisfaction. The results show an urgent requirement for larger UGSs to enhance the security and economic opportunities within urban environments. The deployment of USGs is particularly relevant to fully enhancing residents’ lifestyles and health. The present research aligned with the World Health Organization (WHO) standards to identify the scope of UGS initiatives in different parts of the city of Graz, Austria, and recommendations for improving the quality of future UGS planning is provided. The proposed research results conclude that UGS is a valid alternative to enhance air quality, with measurable and substantial air pollution changes even in space-constrained sites.

The outbreak of the COVID-19 pandemic emerged as a global public health threat. In this crisis period, urban parks provided multiple ecosystem services and direct/indirect benefits to mental and physical health. However, the use patterns, attitudes, and perceptions of urban park visitors remained unexplored in Saudi cities. This study aimed to find out the use patterns, attitudes, and perceptions of people regarding urban parks during the COVID-19 pandemic, and the findings were compared with non-pandemic periods. Two urban parks (Al Masarah Garden and Al Jamaa Garden) in Jeddah megacity (Saudi Arabia) were assessed, and 215 respondents were surveyed to fulfil the objectives of the study. The study applied a questionnaire survey and field observations to understand the impact of the COVID-19 pandemic on the use patterns, attitudes, and perceptions of urban park visitors. Non-parametric tests (Kruskal–Wallis and Mann–Whitney tests) were performed to find out the impact of sociodemographic factors (such as age, gender, and education). From the results, it was found that the COVID-19 pandemic had a substantial impact on the use, attitudes, and perceptions of urban parks in comparison with non-pandemic periods. The number of park visitors decreased during the COVID-19 pandemic, but urban parks were crucial to people’s mental and physical well-being. The findings of this study may help to understand the importance of urban parks as green spaces and to implement measures to enhance the quality and quantity of urban green spaces in Saudi cities.

The phenomenal growth of digital learning platforms has brought new learner engagement and retention challenges to higher education. This study proposes a framework that integrates game mechanics—leveling systems, badges, and timely feedback—with artificial intelligence (AI)-driven personalization to meet the challenges of enhanced adaptability, motivation, and learning outcomes in online environments. Key design elements were identified through literature reviews and consultations with instructional design experts, leading to the development an adaptive learning platform prototype. The prototype underwent an eight-week pilot study with 250 Prince Sattam Bin Abdulaziz University (PSAU) students randomly assigned to a control group (non-adaptive system) or an experimental group (adaptive system). Data sources included pre- and post-tests, platform engagement analytics, and learner perception surveys. The results showed that the adaptive group outperformed the control group in the post-test scores (M = 85.2, SD = 6.4 vs. M = 78.5, SD = 7.2) and motivation levels (M = 4.2, SD = 0.7 vs. M = 3.6, SD = 0.8). Additionally, 82% of the adaptive group achieved mastery-level performance compared to 64% in the control group. These findings demonstrate the potential of integrating game mechanics and AI-driven personalization to transform digital learning, offering a roadmap for scalable, data-driven adaptive platforms. Future research will address long-term retention and diverse subject applications.