In resource-constrained educational settings, particularly in developing nations like Ghana, the integration of artificial intelligence (AI) for personalized learning is hindered by unreliable internet connectivity. This paper introduces a novel framework that leverages edge computing to deliver adaptive learning systems offline, addressing the digital divide. The proposed approach combines Bayesian Knowledge Tracing (BKT) on low-cost Raspberry Pi nodes ($55 base units) with multilingual support for Twi, Ewe, and Ga via AfriBERTa language models, and competency-based digital portfolios for continuous assessment. Aligned with Ghana's 2026 National EdTech Strategy and inspired by implementations like Arizona State University's SolarSPELL, the framework operates in an offline-first mode, requiring no hyperscale cloud infrastructure. It structures reform around three pillars: resilient technological infrastructure, teacher-facilitated AI tutoring, and transformative assessment practices. Implementation guidelines, a cost analysis ($1.03 per student per year), and policy recommendations are provided, ensuring alignment with Sustainable Development Goal 4 (Quality Education). Preliminary field validation with Ghanaian educators shows 85% acceptance and projected learning gains of 0.45-0.55 standard deviations. This replicable model advances educational equity through contextually appropriate AI design, bridging policy aspirations with infrastructural realities.

Underwater optical imaging faces significant challenges due to wavelength-dependent light absorption, scattering, and color distortion, which degrade image quality and hinder marine exploration applications. Traditional enhancement methods often lack adaptability to diverse underwater conditions, while conventional deep learning approaches impose prohibitive computational demands for real-time deployment on resource-constrained platforms. This comprehensive review systematically examines the state-of-the-art in lightweight deep learning architectures specifically designed for real-time underwater image enhancement. We present a detailed taxonomy of efficiency-oriented designs including depth wise separable convolutions, attention mechanisms, neural architecture search, and model compression techniques. The paper critically analyzes implementation strategies, benchmark datasets, and evaluation metrics, both perceptual quality indicators and computational efficiency measures. Furthermore, we synthesize comparative performance analyses across multiple lightweight architectures and identify persistent challenges in domain generalization, temporal consistency, and hardware-software co-design. Emerging research directions including physics-informed networks, multimodal fusion, and ultra-low-power deployment paradigms are discussed. This review aims to consolidate current knowledge and guide future research toward robust, efficient vision systems for underwater autonomous platforms.

The field of Artificial Intelligence (AI) is at a critical inflection point, transitioning from narrow, task-specific models to Advanced Artificial Intelligence systems characterized by autonomy, agency, and complex goal-directed behavior. This comprehensive research paper provides an in-depth analysis of this paradigm shift, focusing on Agentic AI as the primary driver toward Artificial General Intelligence (AGI). We detail the foundational architectures, including the Transformer model and the critical role of Foundation Models (LLMs/VLMs), and explore the technical mechanisms of Agentic systems, such as the autonomous loop, advanced memory architectures like Retrieval-Augmented Generation (RAG) (Figure 4), and multi-agent collaboration (Figure 2). The paper further examines the transformative applications across enterprise, finance, and scientific discovery, and analyzes the profound challenges, including the hardware bottleneck (Section 6), the legal dilemma of accountability (Section 7), and the societal impact on the future of work (Section 8). By integrating five detailed diagrams and 37 scholarly references, this work provides a robust framework for understanding the current state, future trajectory, and responsible development of autonomous AI systems.