The purpose of this study was to assess the impact of Artificial Intelligence (AI) on education. Premised on a narrative and framework for assessing AI identified from a preliminary analysis, the scope of the study was limited to the application and effects of AI in administration, instruction, and learning. A qualitative research approach, leveraging the use of literature review as a research design and approach was used and effectively facilitated the realization of the study purpose. Artificial intelligence is a field of study and the resulting innovations and developments that have culminated in computers, machines, and other artifacts having human-like intelligence characterized by cognitive abilities, learning, adaptability, and decision-making capabilities. The study ascertained that AI has extensively been adopted and used in education, particularly by education institutions, in different forms. AI initially took the form of computer and computer related technologies, transitioning to web-based and online intelligent education systems, and ultimately with the use of embedded computer systems, together with other technologies, the use of humanoid robots and web-based chatbots to perform instructors’ duties and functions independently or with instructors. Using these platforms, instructors have been able to perform different administrative functions, such as reviewing and grading students’ assignments more effectively and efficiently, and achieve higher quality in their teaching activities. On the other hand, because the systems leverage machine learning and adaptability, curriculum and content has been customized and personalized in line with students’ needs, which has fostered uptake and retention, thereby improving learners experience and overall quality of learning.

Vehicular fog computing (VFC) that supports inter-vehicular task offloading emerges as a promising complement to handle the explosive growth of computation-intensive tasks in Intelligent Transportation Systems (ITS). Nonetheless, as the fog access points (F-APs) in crowed areas are often overloaded, the conventional single F-AP VFC may become incompetent and energy-inefficient. To tackle the issue, a novel scheme of non-orthogonal multiple access (NOMA)-enabled multi-F-AP VFC with partial offloading is proposed in this work. However, the corresponding energy minimization turns out to be a highly non-trivial non-linear mixed-integer programming problem. To this end, the optimal power allocation is derived by exploiting monotonicity while good task splitting ratio and user association are found through successive convex approximation (SCA)-based interior-point method and game theoretic approach, respectively. Extensive simulations based on MATLAB show that, in the considered scenarios, the proposed scheme can fulfill a more balanced offloading and better exploit the available computing resources, thereby leading to an approximately 30% energy consumption reduction compared to the baselines.

The growth of maritime activities has boosted the demand for efficient marine communications and computation offloading. Mobile edge computing (MEC)-powered space-air-ground-sea integrated network (SAGSIN) is a promising solution to satisfy these demands. To the best of our knowledge, the existing research on high altitude platforms (HAPs) in SAGSIN remains open. To exploit the HAPs’ advantages of facilitating communication at shorter distances and more stable computing services than terrestrial base stations, this work considers multi-HAP-assisted computation offloading in SAGSIN and formulates an optimization problem, which turns out to be a mixed integer nonlinear programming (MINLP) due to the joint optimization among task-HAP association, computational resource allocation of HAPs and task-satellite association. To this end, the problem is decoupled into three single-variable subproblems by relaxing the delay constraint, and the subproblems are solved by graph theory, the Lagrange multiplier method with Karush-Kuhn-Tucker (KKT) constraints, and the alternating optimization, respectively. Simulation results demonstrate the efficiency of the proposed scheme with superior performances compared to benchmark schemes.

The rapid growth of intelligent connected vehicles fosters the development of Internet-of-Vehicle (IoV) applications. Due to the complex traffic environments, integrated sensing, communication and computation (ISCC) technology would be seen as an efficient paradigm to support a plethora of IoV applications, which causes such a surge of computation-intensive tasks that the task computation with a single server node cannot keep energy-efficient under the strict delay constraints. Inspired by the idea of distributed computing, a scheme of distributed edge computing-based task offloading for ISCC is designed and various assisted edge nodes (AENs) are introduced in a stochastic geometry approach to alleviate the dilemma of energy consumption. After analyzing the proposed joint optimization problem for energy minimization, a double-iteration joint optimization algorithm (DIJOA) is developed to derive the solution. The results of the performance evaluation not only verify the plausibility of the proposed model, but also indicate that the introduction of AENs can significantly reduce system energy consumption and the proposed algorithm outperforms other schemes by over 2.5% in energy consumption, which corroborates the superiority of the proposed scheme through numerical simulations.

The sharp development of maritime activities fosters maritime Internet-of-Things (M-IoT) applications. However, the computation resources of vessels are limited, which cannot adequately satisfy high-quality services in M-IoT. With this consideration, space-air-ground-sea integrated networks (SAGSINs) are introduced in this work. Besides, a maritime distributed computation offloading framework is designed. Furthermore, an energy consumption minimization problem is formulated to jointly optimize the offloading strategy and the bandwidth allocation. For solution, a double-iteration penalty-limited simulated annealing-based particle swarm optimization algorithm is proposed. Simulations are conducted to validate our analysis and corroborate the effectiveness of the proposed scheme.

As one of the most promising solutions, edge caching is emerging to reduce the content retrieval latency and relieve the huge burden on the backhaul links in the 6th generation (6G) millimeter wave (mmWave) intelligent vehicular networks. In this paper, we first formulate the optimization problem in terms of request hit probability and investigate two crucial factors, which impact the effectiveness of cache placement in device-to-device (D2D) enabled mmWave vehicular networks. Then, the general mathematical expressions for optimization of the request hit probability with the constraints of caching capacity are derived. Moreover, a group caching scheme (GCS) is proposed to obtain the sub-optimal results by utilizing relaxation method and Karush-Kuhn-Tucker (KKT) conditions. Inspired from the Matern hard-core processes and by exploiting the spatially correlated characteristics of users distribution, hard-cored based caching algorithm (HCCA) is proposed to avoid the simultaneous caching for a particular file. In addition, a joint caching and scheduling strategy is investigated which maximizes the number of concurrent transmissions by D2D enabled vehicle pairs matching and antennas adjustment. Comprehensive simulations validate our theoretical analysis and demonstrate that the proposed scheme can achieve higher performance in terms of request hit probability and the number of concurrent transmissions compared to the existing methods. Consequently, the proposed caching scheme has great potential in future intelligent vehicular applications.