Understanding AI involves unpacking its layers-from foundational concepts such as artificial intelligence (AI) and machine learning (ML) to more complex structures such as neural networks (NNs), deep learning (DL), and large language models (LLMs) (Figure ) . Machine learning (ML) is a subset of AI where algorithms learn from data, allowing systems to improve over time without explicit programming. Within ML, neural networks (NNs) play a crucial role by mimicking the structure of the human brain to process and interpret complex data. Deep learning (DL), a more advanced form of neural networks, involves multiple layers that enable systems to handle vast amounts of data, making it essential for tasks such as image and speech recognition Large Language Models (LLMs), a specialized form of DL, have gained significant attention for their ability to process and generate human language with remarkable accuracy. These models, built on transformer architectures, are capable of understanding context and generating coherent text, making them indispensable in applications ranging from chatbots to automated content creation. Recent advancements in LLMs have led to their widespread adoption across several industries . Figure shows the various techniques used within AI. Supervised learning trains from labeled data for tasks such as spam detection and image classification, and unsupervised learning identifies hidden patterns in unlabeled data. Reinforcement learning is shown to interact with the environment to optimize actions, exemplified by autonomous robotics and game playing. Generative adversarial networks (GANs) represent another transformative AI technology. GANs consist of two neural networks-the generator and the discriminator-that work together to produce highly realistic data, such as images, videos, and even audio. GANs have been particularly influential in creative industries, enabling applications such as AI-generated art, deepfakes, and advanced image editing . Additionally, deep learning (DL) extends neural networks with multiple layers for complex pattern recognition. Finally, neural networks (NNs) are described as mimicking the brain structure, enabling tasks such as handwriting recognition and speech processing. Figure presents a comprehensive overview of various AI applications. NLP uses AI techniques such as machine learning (ML), DL, regression, and k-means for interpreting and generating text, with applications such as Otter, Google BERT, and OpenAI GPT. Speech recognition, which is applied to spoken words in personal assistants such as Alibaba Cloud and Google Assistant, uses deep learning techniques, specifically recurrent neural networks (LSTMs). On the other hand, Autonomous Agents, such as Woebot and Boston Dynamics' Spot, are driven by AI techniques, including GOFAI, DL, and reinforcement learning, which function as virtual companions, smart robots, and drones. Affect detection employs Bayesian networks and DL to analyze sentiment in text and facial expressions, with examples such as Affectiva and the Microsoft Azure Emotion API. Image recognition and processing, which are used in tasks such as facial recognition and autonomous vehicles with tools such as Google Lens and Tesla Autopilot, rely on deep learning and convolutional neural networks (CNNs). Data mining for prediction, which is essential for predicting outcomes in healthcare, weather forecasting, and fraud detection, uses supervised learning, DL, and support vector machines, as demonstrated by IBM Watson Health and Google DeepMind's AlphaFold. Finally, artificial creativity involves AI systems that generate new content, such as images and stories, using generative adversarial networks (GANs) and autoregressive models, with notable examples being OpenAI's DALL-E and GPT-4o, Claude 3. This critical review aims to explore the current state of AI in education and analyze the global guidelines provided by UNESCO between 2022 and 2024, such as UNESCO's recommendation for AI policy development, highlighting the emerging trends and ethical considerations that must be addressed to ensure that AI contributes to a more equitable and sustainable future . This review will also examine the intersection of AI with sustainable development goals, particularly SDG 4, and identify future research directions and policy implications necessary for aligning AI education with global ethical standards.