This study investigates the adaptability of Artificial Intelligence (AI) agents in the Metaverse, focusing on their ability to enhance responsiveness, decision-making, and engagement through the proposed Adaptive Learning Model for AI Agents (ALMAA) framework. The research does not introduce new interventions to existing platforms like Epic Games or AltspaceVR but instead analyzes how their operations align with adaptive learning principles. By examining these platforms, the study demonstrates the alignment between real-world practices and theoretical constructs, offering insights into how adaptive AI systems operate in dynamic virtual environments. Case observations highlight key metrics such as user interaction efficiency, contextual decision accuracy, and predictive engagement strategies. The data, derived from detailed user interaction logs and feedback reports, underscore the practical application of adaptive learning in optimizing user satisfaction and system performance. Statistical analyses reveal notable gains in response speed, predictive precision, and user engagement, validating the theoretical framework’s relevance. This paper positions the ALMAA framework as a critical lens for understanding and analyzing adaptive AI in virtual settings. It emphasizes theoretical exploration rather than experimental application, providing a foundation for future research into scalable, user-centered AI systems tailored for the Metaverse’s evolving demands.

Accurate brain tumor segmentation in multimodal magnetic resonance imaging (MRI) scans is essential for quantitative neuro-oncology analysis, treatment planning, and longitudinal disease monitoring. However, automatic delineation remains challenging because glioma subregions exhibit heterogeneous appearance across T1-weighted (T1), contrast-enhanced T1-weighted (T1ce), T2-weighted (T2), and fluid-attenuated inversion recovery (FLAIR) sequences; weak and irregular lesion boundaries; and severe scale variation between edema, tumor core, and enhancing tumor regions. This paper presents NeuroSPFNet, a vision–language semantic prior-guided frequency-enhanced segmentation framework for tumor subregion delineation in multimodal MRI volumes. NeuroSPFNet integrates four complementary components: a Clinical Vision–Language Prior Component (CVLPC) for category-level semantic guidance; a Frequency-Enhanced Boundary Module (FEBM) for boundary-sensitive representation learning; a Multi-Scale Lesion-Dominated Fusion Module (MLDFM) for hierarchical lesion aggregation; and a Lesion-Semantic Boundary Consistency Loss (LSBCL) for jointly constraining region overlap, boundary quality, and semantic alignment. The task-specific contribution lies in the closed coupling of shared BraTS-region semantic prototypes, gated frequency responses, lesion-weighted multiscale features, and semantic-boundary supervision within one volumetric segmentation pipeline. On the Brain Tumor Segmentation (BraTS) 2021 benchmark, NeuroSPFNet achieved Dice scores of 91.94%, 88.84%, and 85.38% for whole tumor, tumor core, and enhancing tumor, respectively, with a mean Dice of 88.72% and a mean 95th-percentile Hausdorff distance (HD95) of 3.58 mm. Ablation studies show distinct effects across subregions and metrics: semantic priors primarily strengthen tumor-core and enhancing-tumor discrimination, whereas frequency and boundary constraints primarily reduce contour error. NeuroSPFNet also attains competitive performance under the current setting, with moderate computational overhead of 42.58 million parameters, 78.60 billion floating-point operations (GFLOPs), and 265 ms per volume.

Vision-based human motion assessment requires a model to capture both temporal execution patterns and context-dependent quality criteria. This problem is particularly challenging in multi-style dance, where similar kinematic patterns may receive different quality evaluations under different stylistic conventions. We propose C-TransDance, a style-conditioned gated CNN–Transformer for real-time multi-style dance quality assessment using pose-based visual sensing. The system extracts 33 body landmarks from a single front-facing camera with MediaPipe BlazePose GHUM and represents each motion sequence using normalized joint coordinates, confidence, velocity, and acceleration. A convolutional branch for local spatio-temporal execution patterns and a Transformer branch for long-range temporal dependencies are combined through learnable gated fusion and modulated by a learned dance style embedding with an explicit feature–style interaction. Multi-task heads jointly predict continuous quality scores, quality classes, and structured feedback categories, and temporal attention and joint-level sensitivity are coupled with constrained feedback templates to expose inspectable movement evidence. The framework was evaluated on 1152 clips from 96 participants across four dance styles, annotated independently by three qualified instructors, using participant-independent splits and five random seeds. On the held-out test set (173 clips, 15 unseen participants), C-TransDance achieved an MAE of 4.86 ± 0.16, an RMSE of 6.38 ± 0.22, an accuracy of 82.1% ± 1.1%, and a macro-F1 of 0.822 ± 0.011 compared with an MAE of 5.63 for the strongest style-agnostic pose baseline. Within this backbone and dataset, removing style conditioning increased MAE by 15.6%, compared with 12.8% after removing the convolutional branch and 9.3% after removing the Transformer branch. Because the baselines do not receive the style label and style is partly confounded with routine in the dataset, these results are interpreted as controlled evidence that style information is a major factor, not as a general architectural ranking. The complete system operated at 28.8 FPS with 34.7 ms end-to-end latency, supporting the technical feasibility of style-aware pose sensing for instructor-supervised feedback.

Human pose estimation (HPE) has become a core technology for human-centered visual understanding and is widely used in sports analysis, rehabilitation assessment, human–computer interaction, autonomous driving, and industrial safety monitoring. Although benchmark-driven research has substantially improved pose estimation accuracy, strong performance on standard datasets does not necessarily ensure reliable deployment in real-world environments. Practical HPE systems are commonly deployed in camera-based, mobile, wearable, and edge-computing environments. In these settings, sensor quality, device placement, frame rate, illumination conditions, hardware limitations, temporal instability, and application-specific reliability requirements jointly influence system performance. Using a structured narrative synthesis of representative literature, this review examines HPE from a deployment-oriented perspective instead of treating pose estimation solely as a model-level task. The review first summarizes the major benchmark-to-deployment gaps, including data, system, task, and temporal gaps; it then analyzes the end-to-end HPE pipeline, including image or video acquisition, human detection, region localization, pose inference, tracking, temporal smoothing, coordinate decoding, and task-level decision-making. Representative CNN-based, Transformer-based, temporal modeling, and lightweight optimization methods are reviewed in terms of deployment cost, optimization potential, and application suitability. This review also discusses practical application requirements and future research directions related to deployment-aware evaluation, system-level optimization, efficient temporal modeling, and application-level reliability. By linking benchmark performance with practical sensing and system constraints, this review provides a structured reference for the selection, evaluation, and deployment of HPE methods in real-world environments.

This study proposes AIEAM, an AI-Enhanced Adaptive Learning Framework designed to support personalized education in higher education. The framework integrates cognitive, metacognitive, and ethical components into a modular architecture comprising five core modules: learner profiling, adaptive path generation, real-time feedback and assessment, resource recommendation, and an ethical and trust layer. Grounded in established learning theories, AIEAM addresses the limitations of existing systems that often lack pedagogical transparency, multidimensional adaptivity, and ethical oversight. To validate the framework, two real-world systems—Squirrel AI and Carnegie Learning (MATHia)—are analyzed against AIEAM’s design principles. The analysis reveals strong alignment in functional components while highlighting gaps in metacognitive support and ethical integration. AIEAM contributes both a conceptual model and practical roadmap for developing adaptive learning environments that are scalable, interpretable, and pedagogically sound. The framework provides a foundation for future research and implementation strategies in AI-supported higher education learning systems.