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Background Integrating Artificial Intelligence (AI) in educational applications is becoming increasingly prevalent, bringing opportunities and challenges to the learning environment. While AI applications have the potential to enhance structured learning, they may also significantly impact students’ creativity and academic emotions. Objectives This study aims to explore the effects of AI-integrated educational applications on college students’ creativity and academic emotions from the perspectives of both students and teachers. It also assessed undergraduate students’ and faculty’s attitudes to AI-integrated applications. Methodology A mixed-method research design was used. In the first phase, a qualitative research approach was employed, utilizing theoretical sampling to select informants. Data were collected through in-depth interviews with undergraduate students and university lecturers to gain comprehensive insights into their experiences and perceptions. A scale was developed, validated, and administered to 120 students and faculty in the quantitative phase. Descriptive statistics was used to analyze the data. Findings The study revealed that AI applications often impose rigid frameworks that constrain creative thinking and innovation, leading to emotional disengagement due to AI interactions’ repetitive and impersonal nature. Additionally, constant AI assessments heightened performance anxiety, and technical frustrations disrupted the learning process. Conversely, AI applications stimulated creativity by introducing new ideas and problem-solving techniques, enhanced engagement through interactive elements, provided personalized feedback, and supported emotional well-being with gamified elements and constant availability. Quantitative data also verified that teachers and students have positive attitudes toward the benefits and challenges of these applications. Conclusions AI integration in educational applications has a dual-edged impact on college students’ creativity and academic emotions. While there are notable benefits in stimulating creativity and enhancing engagement, significant challenges such as creativity constraints, emotional disengagement, and performance anxiety must be addressed. Balancing these factors requires thoughtful implementation and continuous evaluation to optimize the role of AI in education.

Background In today’s rapidly evolving educational landscape, AI-powered applications like ChatGPT, POE, and Duolingo are revolutionizing language education, offering personalized learning experiences in EFL. However, challenges such as student engagement and academic procrastination persist. This study delves into how these AI tools impact EFL learners’ engagement and procrastination tendencies, aiming to inform effective technology integration in language instruction. Objectives The primary goals of this research are to assess the influence of AI-empowered applications on affective, cognitive, and behavioural engagement and academic procrastination among EFL learners. By considering both the affective and cognitive aspects of engagement, the study aims to provide insights into optimizing EFL instruction through AI-driven tools while addressing academic procrastination challenges. Methodology A quasi-experimental research method was employed, involving ten intact classes comprising 350 students divided into two groups. Engagement and procrastination scales were administered before and after the treatment. T-tests were utilized to analyze the data, comparing pre- and post-treatment scores between the groups. Findings The results indicate that the experimental group, exposed to AI-empowered applications, demonstrated significantly higher levels of affective, cognitive, and behavioural engagement than the control group. Keywords: AI-empowered applications, Academic procrastination, engagement, Chinese EFL learners’ engagement. Moreover, a substantial reduction in academic procrastination was observed among students exposed to AI-empowered applications. Conclusions The study underscores the potential of AI-empowered applications to enhance learner engagement and mitigate academic procrastination. The findings contribute to the pedagogical discourse surrounding technology integration and advocate adopting learner-centred approaches. The incorporation of AI-empowered applications in diverse educational settings is recommended.

Current deep learning technologies used a large number of parameters to achieve a high accuracy rate, and the number of parameters is commonly more than a hundred million for image-related tasks. To improve both training speed and accuracy in multi-clouds, distributed deep learning is also widely applied. Therefore, reducing the network scale or improving the training speed has become an urgent problem to be solved in multi-clouds. Concerning this issue, we proposed a game architecture in multi-clouds, which can be supported by resource provision and service schedule. Furthermore, we trained a deep learning network, which can ensure high accuracy while reducing the number of network parameters. An adapted game, called flappy bird, is used as an experimental environment to test our neural network. Experimental results showed that the decision logic of the flappy bird, including flight planning, avoidance, and sacrifice, is accurate. In addition, we published the parameters of the neural network, so other scholars can reuse our neural network parameters for further research.

Artificial Intelligence (AI), as an innovation in technology, has greatly affected human life. AI applications such as ChatGPT have been used in different fields, particularly education. However, the use of AI applications to enhance undergraduate students’ academic emotions and test anxiety has not been appropriately investigated. This study addresses the effects of undergraduate students’ test anxiety and academic emotions. A total of 160 undergraduate students majoring in different fields of study were selected through convenience sampling and divided into control and experimental groups. Both groups received test anxiety and academic emotions scales at the onset of the treatment. The students assigned to the experimental group were trained to use ChatGPT and monitored for learning and doing their assignments outside the classroom during the semester. The two groups received the scales at the end of the semester, which lasted 16 weeks. Independent samples t-tests were used for analyzing the data. Results revealed that using AI-empowered applications significantly reduced the students’ test anxiety and negative academic emotions but enhanced their positive academic emotions. Students can use ChatGPT as an auxiliary instrument to overcome their negative emotions and enhance their educational attainment. Findings affect teachers, educational technologists, educational psychologists, and students.

In recent years, with the rapid growth of edge data, the novel cloud-edge collaborative architecture has been proposed to compensate for the lack of data processing power of traditional cloud computing. On the other hand, on account of the increasing demand of the public for data privacy, federated learning has been proposed to compensate for the lack of security of traditional centralized machine learning. Deploying federated learning in cloud-edge collaborative architecture is widely considered to be a promising cyber infrastructure in the future. Although each cloud-edge collaboration and federated learning is hot research topic respectively at present, the discussion of deploying federated learning in cloud-edge collaborative architecture is still in its infancy and little research has been conducted. This article aims to fill the gap by providing a detailed description of the critical technologies, challenges, and applications of deploying federated learning in cloud-edge collaborative architecture, and providing guidance on future research directions.

Besides many sectors, artificial intelligence (AI) will drive energy sector transformation, offering new approaches to optimize energy systems’ operation and reliability, ensuring techno-economic advantages. However, integrating AI into the energy sector is associated with unforeseen obstacles that might change optimistic approaches to dealing with AI integration. From a multidimensional perspective, these challenges are identified, categorized based on common dependency attributes, and finally, evaluated to align with the viable recommendations. A multidisciplinary approach is employed through the exhaustive literature to assess the main challenges facing the integration of AI into the energy sector. This study also provides insights and recommendations on overcoming these obstacles and highlights the potential benefits of successful integration. The findings suggest the need for a coordinated approach to overcome unforeseen obstacles and can serve as a valuable resource for policymakers, energy practitioners, and researchers looking to unlock the potential of AI in the energy sector.

Cloud-native database systems have started to gain broad support and popularity due to more and more applications and systems moving to the cloud. Various cloud-native databases have been emerging in recent years, but their developments are still in the primary stage. At this stage, database developers are generally confused about improving the performance of the database by applying AI technologies. The maturity model can help database developers formulate the measures and clarify the improvement path during development. However, the current maturity models are unsuitable for cloud-native databases since their architecture and resource management differ from traditional databases. Hence, we propose a maturity model for AI-empowered cloud-native databases from the perspective of resource management. We employ a systematic literature review and expert interviews to conduct the maturity model. Also, we develop an assessment tool based on the maturity model to help developers assess cloud-native databases. And we provide an assessment case to prove our maturity model. The assessment case results show that the database’s development direction conforms to the maturity model. It proves the effectiveness of the maturity model.

In the era of information explosion, the energy consumption of cloud data centers is significant. It’s critical to reduce the energy consumption of large-scale data centers while guaranteeing quality of service (QoS), especially the energy consumption of video cloud computing platforms. The application of virtual machine (VM) consolidation has been regarded as a promising approach to improve resource utilization and save energy of the data centers. In this paper, an energy efficient and QoS-aware VM consolidation method is proposed to address the issues. A combined prediction model based on grey model and ARIMA is applied to host status detection, and we provide a new scheme that VM placement policy based on resource utilization and varying energy consumption to search most suitable host and VM selection policy called AUMT selecting VM with low average CPU utilization and migration time. Extensive experimental results based on the cloudsim simulator demonstrate that proposed approach enables to achieve the objectives reducing energy consumption, number of migrations, SLAV and ESV by an average of 56.07%, 79.21%, 91.01% and 84.34% compared with the benchmark methods and the AUMT can reduce energy consumption, the number of migrations and ESV by an average of 15.46%, 28.11% and 3.96% compared with the state-of-the-art method.

In vehicular edge computing, the low-delay services are invoked by the vehicles from the edge clouds while the vehicles moving on the roads. Because of the insufficiency of computing capacity and storage resource for edge clouds, a single edge cloud cannot handle all the services, and thus the efficient service deployment strategy in multi edge clouds should be designed according to the service demands. Noticed that the service demands are dynamic in temporal, and the inter-relationship between services is a non-negligible factor for service deployment. In order to address the new challenges produced by these factors, a collaborative service on-demand dynamic deployment approach with deep reinforcement learning is proposed, which is named CODD-DQN. In our approach, the number of service request of each edge clouds are forecasted by a time-aware service demands prediction algorithm, and then the interacting services are discovered through the analysis of service invoking logs. On this basis, the service response time models are constructed to formulated the problem, aiming to minimize service response time with data transmission delay between services. Furthermore, a collaborative service dynamic deployment algorithm with DQN model is proposed to deploy the interacting services. Finally, the real-world dataset based experiments are conducted. The results show our approach can achieve lowest service response time than other algorithms for service deployment.

Multi-cloud computing is becoming a promising paradigm to provide abundant computation resources for Internet-of-Things (IoT) devices. For a multi-device multi-cloud network, the real-time computing requirements, frequently varied wireless channel gains and changeable network scale, make the system more dynamic. It is critical to satisfy the dynamic nature of network with different constraints of IoT devices in multi-cloud environment. In this paper, we establish a continuous-discrete hybrid decision offloading model, each device should learn to make coordinated actions, including cloud server selection, offloading ratio and local computation capacity. Therefore, both continuous-discrete hybrid decision and coordination among IoT devices are challenging. To this end, we first develop a probabilistic method to relax the discrete action (e.g. cloud server selection) to a continuous set. Then, by leveraging a centralized training and distributed execution strategy, we design a cooperative multi-agent deep reinforcement learning (CMADRL) based framework to minimize the total system cost in terms of the energy consumption of IoT device and the renting charge of cloud servers. Each IoT device acts as an agent, which not only learns efficient decentralized policies, but also relieves IoT devices’ computing pressure. Experimental results demonstrate that the proposed CMADRL could efficiently learn dynamic offloading polices at each IoT device, and significantly outperform the four state-of-the-art DRL based agents and two heuristic algorithms with lower system cost.