Exploring Generative AI's Influence on Tailored Learning

Abstract

Recent advancements in large language models, particularly in deep learning and natural language processing, have set new benchmarks for performance, enabling artificial intelligence to mimic human behavior and writing styles convincingly. Karpathy et al. (2016) reported generative models for analyzing vast amounts of information, with short-term applications and the long-term potential to grasp the inherent features of a dataset autonomously, be it categories, dimensions, or other aspects. These models, exemplified by ChatGPT (McKinsey, 2023), released by OpenAI on November 30, 2022, demonstrate enhanced accuracy and robustness, enabling applications in tasks such as text summarization, language translation, and content generation for open-ended conversations (Abdullah, 2022). Generative Adversarial Networks (GANs) emerge as a upcoming category of generative models, distinct from other techniques, as they excel in producing precise and vivid images while also capturing valuable information about the underlying textures through the learned codes (Karpathy et al., 2016).