Search Results (215)

Generative artificial intelligence (GAI) enables students to offload cognitive tasks to an external system, yet the consequences of such cognitive offloading for the development of critical thinking—a core dimension of human intelligence—remain underexplored. Drawing upon cognitive offloading theory and distributed cognition theory, this study investigates the heterogeneity of critical thinking outcomes among Chinese university students who use GAI, focusing on how different patterns of human–AI collaboration relate to cognitive autonomy relinquishment. A questionnaire survey was administered to 353 university students across multiple provinces in China. Cluster analysis and regression analysis were employed to identify distinct user profiles and to examine predictors of critical thinking gains and cognitive autonomy. Four distinct user profiles emerged, ranging from “simple Q&A users” (25.2%) to “critical co-thinkers” (15.6%). Learning motivation was the strongest predictor of both critical thinking gains (β = 0.42) and lower cognitive autonomy relinquishment (β = −0.35). Notably, offloading depth positively predicted cognitive autonomy relinquishment (β = 0.25), revealing a paradoxical pattern: sophisticated GAI use was associated with greater dependence. A “high depth–high dependence” subgroup (25.8%) was identified, disproportionately composed of female students and Information and Communication Technology (ICT) majors. The findings challenge the assumption that deeper GAI engagement automatically yields cognitive benefits. Because all constructs were measured through self-report, the findings are interpreted as reflecting students’ perceptions of their cognitive behaviors and abilities; the methodological implications of this design are discussed in detail. Educational interventions should prioritize metacognitive training over technical skill development to ensure that cognitive offloading enhances rather than undermines critical thinking. Full article

Visually impaired individuals face significant challenges in autonomous mobility and situational awareness. Most existing assistive technologies address isolated tasks, such as object recognition or text reading, while failing to capture broader environmental context. This work addresses this limitation by proposing a scene-sensitive, low-cost assistive system that delivers holistic situational information. We present Vocal-Eyes, an intelligent smart glasses platform that provides periodic audio descriptions of the surrounding environment. The system employs a cloud-based neural processing pipeline in which visual features are extracted using a Transformer-based architecture. Relational context is modeled through scene graph generation, and scene graphs are translated into natural language via a graph-to-text module. A lightweight hardware prototype captures visual data locally, while computationally intensive processing is offloaded to the cloud to reduce power consumption. The experimental results show that relational, scene-based narration produces more coherent and informative descriptions than object-centric approaches while maintaining acceptable periodic latency. Cost analysis further indicates that Vocal-Eyes is significantly more affordable than comparable commercial smart glasses solutions. These results demonstrate that Transformer-based scene understanding with cloud-assisted processing is an effective and practical approach for developing accessible, context-aware assistive technologies for visually impaired users. Full article

Generative Artificial Intelligence (AI), particularly large language models, has changed the conditions under which individuals judge their own cognitive performance. While AI-assisted tools can improve task outcomes, such improvements do not necessarily lead to more accurate self-evaluation. This article develops an integrative conceptual review of calibration and miscalibration in AI-assisted cognition. Drawing on research on metacognitive monitoring, self-regulated learning, judgment calibration, cognitive offloading, cognitive engagement, and trust in AI, the article identifies a central gap in the literature: the lack of an explanatory framework showing how AI-supported performance becomes a cue for users’ judgments of their own competence. To address this gap, the article proposes an eight-axis explanatory framework organized around the functional position of AI in the task, reflective support versus cognitive substitution, metacognitive engagement, effort redistribution, cognitive engagement, the distinction between assisted performance and actual learning, trust regulation and attribution of success, and self-evaluation accuracy. The framework is presented through qualitative relational expressions and a synthetic conceptual figure, not as an empirically estimated model. Its main contribution is to explain why AI may support calibration when it sustains reflection, verification, and learning, but may contribute to miscalibration when it promotes cognitive substitution, effort reduction, overreliance, or erroneous attribution of success. The article offers a conceptual basis for future empirical research on self-evaluation accuracy in human–AI interaction. Full article

Wireless sensor networks supported by mobile edge computing are increasingly required to process heterogeneous sensing data under stringent latency, reliability, and energy constraints. However, most existing task-offloading studies are still formulated for generic user equipment and primarily focus on uplink transmission, which is insufficient for practical sensing systems where sensor nodes continuously upload measurements while simultaneously receiving control commands, model updates, and feedback from the edge. To address this gap, this paper reformulates joint computation offloading and power control as a sensor-aware optimization problem in an edge-assisted wireless sensor network. We propose a three-layer architecture consisting of sensor nodes, access points with lightweight edge servers, and a cloud coordination layer. Each sensing task is characterized by data size, computation density, latency deadline, and sensing priority, while the optimization objective jointly minimizes long-term task delay, communication and computation energy, and packet-loss penalty under transmission power, edge resource, and residual-energy constraints. To solve the resulting mixed discrete–continuous problem, we develop a multi-agent reinforcement learning framework in which each sensor node acts as an autonomous agent and learns offloading and transmission policies with clipped proximal policy optimization, while the cloud layer performs coordinated edge-resource allocation through the alternating direction method of multipliers. In addition to delay and energy, network lifetime and sensing delivery performance are incorporated into the evaluation. Simulation results in a sensor-network monitoring scenario demonstrate that the proposed framework consistently reduces latency, lowers energy consumption, and prolongs network lifetime compared with representative baselines, highlighting its effectiveness and practical potential for intelligent sensing applications that require integrated sensing, communication, and edge computing. Full article

This paper investigates an RIS-assisted mobile edge computing (MEC) system without reliable direct links between users and base stations (BSs). Users offload tasks to BSs through reconfigurable intelligent surface (RIS)-reflected links, where offloading decisions, service prices, and RIS-assisted transmission quality are tightly coupled. We formulate a joint design problem that considers task latency, transmission energy consumption, service pricing, BS computing constraints, and RIS phase-shift constraints. The RIS phase shifts are first optimized to improve the effective cascaded channel gain. Then, a distributed price-negotiation-based offloading mechanism is developed to coordinate user association and service pricing under channel-dependent utilities. Analysis and simulations show that the proposed algorithm converges within a finite number of iterations and achieves a balanced tradeoff between user utility and BS revenue. Full article

This paper addresses the problem of throughput utility maximization in a non-orthogonal multiple access (NOMA)-enabled wireless power transfer mobile edge computing (WPT-MEC) network with dynamic task arrivals and user cooperation. To promote fairness and effectively handle random task arrivals and time-varying channels, we model the system utility as a nonlinear function of time-averaged throughput. We then formulate a stochastic optimization problem aimed at maximizing utility while strictly maintaining sensor queue stability. By leveraging the Lyapunov optimization framework, the long-term network-wide utility maximization is decomposed into efficient, slot-wise convex subproblems that operate online without requiring prior knowledge of future task arrivals or channel states. We develop a Dynamic User Cooperation Throughput Maximization (DUCTM) algorithm that enables adaptive resource allocation and cooperative computation offloading in an online manner. Theoretical analysis establishes a provable $\left[O\right(1/V),O(V\left)\right]$ trade-off between utility optimality and queue backlog. Extensive simulations demonstrate that our approach consistently outperforms baseline methods, providing robust and stable performance even under bursty traffic and highly dynamic environmental conditions. Full article

Complex knowledge work depends on individual output and on public exchanges that document problems, evaluate contributions, route expertise, and preserve reusable knowledge. Software work makes this infrastructure unusually visible through GitHub issues, reviews, comments, mentions, and cross-project ties. As generative AI coding tools become private, on-demand sources of task support, it is unclear whether productive output remains tightly coupled with participation in this GitHub-visible public knowledge infrastructure. This study examines that question using a balanced panel of approximately 38,000 freelance developers on GitHub observed quarterly from 2019 to 2025 (approximately 1,080,000 person-quarter observations), estimating within-person changes in the association between a Productivity Index and a Social Connectivity Index. Two-way fixed effects models estimate a substantively large weakening after mid-2022 (−0.138 SD, about 44 percent of the pre-AI slope), and the pattern remains stable across alternative operationalizations, model specifications, and sample definitions. A survey-linked subsample (n = 237) provides individual-level triangulation: the weakening aligns with developers’ self-reported AI adoption dates, and heavier AI users exhibit larger decoupling. Decomposition by exchange function is selective: public exchanges with more direct private AI support pathways (information seeking, troubleshooting, preliminary evaluation) weaken more than exchanges anchored in contextual judgment and new-tie formation. This study documents a large-scale behavioral decoupling between productive output and visible GitHub-based public knowledge-infrastructure participation in a real-world problem-solving setting. The pattern is consistent with cognitive offloading as one micro-level pathway, while direct process evidence is left to future work. Full article

As the number of unmanned aerial vehicles (UAVs) and the volume of computational tasks increase in UAV ad hoc networks (UAVANET), the solution space for task allocation strategies grows exponentially. In practical emergency scenarios with concurrent multi-user access, multi-UAV systems equipped with mobile edge computing (MEC) devices face challenges such as limited computing resources and imbalanced task distribution during task offloading. To address these challenges, this paper proposes an adaptive task allocation algorithm named AUSTA-DQHO (Adaptive UAV Swarm Task Allocation using Deep Q-networks and Genetic Algorithms Hybrid Optimization), which combines Deep Q-Network (DQN) with Genetic Algorithm (GA), aiming to optimize computational task scheduling and minimize both the total task delay and the variance in task delays. First, we introduce a multi-UAV-assisted MEC application framework. In this framework, UAVs equipped with high-performance computing modules are deployed as airborne servers in the target area, providing data offloading and task computation support for IoT devices. Next, to tackle the optimization problem, we replace the random action selection process in DQN with a hybrid strategy that incorporates heuristic methods—specifically, GA and greedy algorithms—to perform global search and make more effective decisions for optimal task allocation for each offloading request. Furthermore, to accelerate the convergence of the AUSTA-DQHO policy while ensuring global optimality, we introduce a pre-clustering mechanism and a dynamic weighting factor for randomly generated task offloading requests in the target area. These mechanisms effectively reduce the solution space and ensure that optimal actions are learned at different stages of the training process. Experimental results demonstrate that the proposed algorithm achieves a task latency reduction of 18.72% and a load balancing improvement of 98.72%, surpassing the performance of the other algorithms. Additionally, we explore the optimal number of UAVs under given environmental conditions to minimize the waste of computing resources. Full article

Mobile edge computing (MEC) enables resource-constrained mobile devices to execute delay-sensitive and compute-intensive applications by offloading tasks to nearby edge servers. However, task orchestration in MEC is challenged by the highly dynamic system conditions, unreliable networks, and distributed edge environments. Moreover, as the number of mobile users, tasks, and distributed computing resources (edge/cloud servers) increases, the task orchestration process becomes more complex due to the expanded decision space and the need to efficiently allocate heterogeneous resources under latency and capacity constraints. As the decision space grows, exhaustive-search-based orchestration becomes computationally infeasible. Clustering approaches often rely on proximity-only grouping, while learning-based solutions require extensive training and parameter tuning. To address these challenges, this paper proposes a Multi-Criteria Hierarchical Clustering-based Task Orchestrator (MCHC-TO), a novel framework that integrates multi-criteria decision making with divisive hierarchical clustering for preference-aware and adaptive workload orchestration. Edge servers are first evaluated using multiple decision criteria, and the resulting preference rankings are exploited to form hierarchical preference-based clusters. Incoming tasks are then assigned to the most suitable cluster based on task requirements, enabling efficient resource utilization and dynamic decision-making. Extensive simulations conducted using an edge computing simulator demonstrate that the proposed MCHC-TO framework consistently outperforms benchmark approaches, achieving reductions in average service delay and task failure rate of up to 48% and 92%, respectively. These results highlight the effectiveness of combining multi-criteria evaluation with hierarchical clustering for robust and dynamic task orchestration in MEC environments. Full article

Widespread acceptance of collaborative robots in human-involved scenarios requires accessible and intuitive interfaces for lay workers and non-expert users. Existing interfaces often rely on users to plan and issue low-level commands, necessitating extensive knowledge of robot control. This study proposes a multimodal agentic AI framework integrating natural user interfaces (NUIs) to foster effortless human-like partnerships in human–robot collaboration (HRC), which enhance intuitiveness and operational efficiency. First, it allows users to instruct robots using plain language verbally, coupled with gaze, revealing objects precisely. Second, it offloads users’ workload for robot motion planning by understanding context and reasoning task decomposition. Third, coordinating with AI agents built on large language models (LLMs), the system interprets users’ requests effectively and provides feedback to establish transparent communication. This proof-of-concept study included experiments to demonstrate a practical implementation of the agentic AI framework on a mobile manipulation robot in the collaborative task of human–robot wood assembly. Seven participants were recruited to interact with this AI-integrated agentic robotic system. Task performance and user experience metrics were measured in terms of completion time, intervention rate, NASA TLX survey for workload, and valuable insights of practical applications were summarized through a qualitative analysis. This study highlights the potential of NUIs and agentic AI-embodied robots to overcome existing HRC barriers and contributes to improving HRC intuitiveness and efficiency. Full article

Vehicular Edge Computing (VEC) can effectively guarantee the service experience of user vehicles, but resource-limited Roadside Units (RSUs) may face insufficient computing capacity during task peak periods. Utilizing Assisting Vehicles (AVs) with idle resources to share computing power can alleviate the pressure on RSUs. However, existing studies often fail to adequately incentivize selfish assisting vehicles to contribute resources and frequently lack a global optimization perspective from the overall system welfare. To address these challenges, this paper proposes an incentive-driven utility-balanced task offloading framework that aims to maximize social welfare while jointly optimizing resource allocation and profit pricing. Specifically, we first formulate the resource allocation as a Mixed-Integer Nonlinear Programming (MINLP) problem. To solve this problem, we introduce hybrid action representation learning to VEC for the first time and propose the HyAR-PPO algorithm to jointly optimize discrete offloading decisions and continuous resource allocation. This algorithm maps heterogeneous hybrid actions to a unified latent representation space through a Variational Autoencoder for the solution. Subsequently, equilibrium prices among user vehicles, Computation Service Providers (CSPs), and assisting vehicles are determined through Nash bargaining games, satisfying individual rationality constraints and achieving Pareto-optimal fair profit distribution. Experimental results demonstrate that the proposed framework can effectively coordinate multi-party interests. Compared with mainstream methods, the approach based on hybrid action representation learning achieves a significant improvement in social welfare, with its advantages being more pronounced in medium-to-large-scale scenarios. Full article

Nowadays, the emergence of Foundation Models as a Service enables mobile users to access powerful capabilities such as inference and fine-tuning on demand and without incurring local computational overhead. This paper introduces a software-aware offloading framework for FMaaS that allows edge nodes to forecast software aging and prevent service degradation. Each node employs a lightweight Echo State Network to predict its software age, with tasks dynamically assigned based on communication cost, inference delay, and forecast reliability. Simulation results including ablation studies confirm the effectiveness of software age forecasting in reducing task failures and improving session continuity. Full article

In disaster scenarios where communication infrastructure is damaged, Unmanned Aerial Vehicle (UAV)-assisted wireless networks can provide temporary connectivity and hence the indispensable mobile edge computing functionality. However, limited resources on UAVs require prioritization of critical data in such scenarios. This research addresses reliable transmission and task offloading by modeling user tasks as layered compositions, where the base layer is essential and enhancement layers are optional. TDMA-based prioritization is employed to ensure reliable decoding of high-priority layers of the computational tasks (i.e., intra-user priority) along with inter-user priority needed for urgent users like rescue teams. Under these reliability constraints, this work formulates a joint communication–computation optimization problem to allocate transmission power and UAV CPU cycles efficiently in order to minimize total weighted offloading latency. The original problem is non-convex; thus, we leverage epigraph and perspective functions to recast the problem into a convex one. We also derive analytically, using the KKT conditions, the optimal water-filling-like solutions for the reformulated problem. The numerical results show that, at a signal-to-noise ratio of 5 dB, the proposed algorithm achieves relative latency reductions vs. the baseline algorithms (39.99% reduction vs. Equal Allocation, 49.99% reduction vs. Enhancement First, and 69.99% reduction vs. No Priority), which reflect considerable latency reduction with priority-aware offloading. Full article

Multi-access edge computing (MEC) has been widely recognized as a promising solution for alleviating the computational burden on edge devices, particularly in supporting fast and real-time processing of resource-intensive applications. In this paper, we propose a decentralized offloading decision strategy based on multi-agent deep reinforcement learning (MADRL), aiming to minimize the overall task completion latency experienced by edge devices. Our proposed scheme adopts a grant-free access mechanism during the initialization of offloading in a fully decentralized manner, which serves as the key feature of our strategy. As a result, determining the optimal offloading factor becomes significantly more challenging due to the simultaneous access attempts from multiple edge devices. To resolve this problem, we consider a discrete action space-based deep reinforcement learning (DRL) approach, termed deep Q network (DQN), to enable each edge device to learn a decentralized computation offloading policy based solely on its local observation without requiring global network information. In our design, each edge device dynamically adjusts its offloading factor according to its observed channel state and the number of active users, thereby balancing local and remote computation loads adaptively. Furthermore, the proposed MADRL-based framework jointly accounts for user association and offloading decision optimization to mitigate access collisions and computation bottlenecks in a multi-user environment. We perform extensive computer simulations using MATLAB R2023b to evaluate the performance of the proposed strategy, focusing on the task completion latency under various system configurations. The numerical results demonstrate that our proposed strategy effectively reduces the overall task completion latency and achieves faster convergence of learning performance compared with conventional schemes, confirming the efficiency and scalability of the proposed decentralized approach. Full article

Semantic Communication (SC), driven by a deep learning (DL)-based “understand-before-transmit” paradigm, transmits lightweight semantic information (SI) instead of raw data. This approach significantly reduces data volume and communication overhead while maintaining performance, making it particularly suitable for UAV communications where the platform is constrained by size, weight, and power (SWAP) limitations. To alleviate the computational burden of semantic extraction (SE) on the UAV, this paper introduces federated learning (FL) as a distributed training framework. By establishing a collaborative architecture with edge users, computationally intensive tasks are offloaded to the edge devices, while the UAV serves as a central coordinator. We first demonstrate the feasibility of integrating FL into SC systems and then propose a novel solution based on Proximal Policy Optimization (PPO) to address the critical challenge of ensuring service fairness in UAV-assisted semantic communications. Specifically, we formulate a joint optimization problem that simultaneously designs the UAV’s flight trajectory and bandwidth allocation strategy. Experimental results validate that our FL-based training framework significantly reduces computational resource consumption, while the PPO-based algorithm approach effectively minimizes both energy consumption and task completion time while ensuring equitable quality-of-service (QoS) across all edge users. Full article