Generative Artificial Intelligence (GenAI) represents a transformative frontier in contemporary AI research. It is characterized by its ability to autonomously generate content with human-like creativity and complexity across various modalities, including text, images, audio, code, and scientific hypothesis generation. This survey systematically reviews the extensive evolution, cutting-edge technological innovations, and sophisticated theoretical frameworks that underpin GenAI. Utilizing a rigorous systematic literature review methodology, the paper critically analyzes peer-reviewed sources from key scholarly databases, synthesizing advancements in transformer-based architectures, quantum-enhanced generative models, and meta-learning frameworks. It also addresses critical interdisciplinary issues, particularly the ethical, societal, and philosophical implications related to generative systems, such as authorship ambiguity, algorithmic bias, misinformation propagation, and accountability deficits. By meticulously identifying existing research gaps and outlining future investigative pathways, this survey significantly enhances scholarly understanding and contributes to shaping responsible research agendas, policy formulations, and technological governance in the rapidly evolving field of generative AI.
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Generative AI: A survey of historical development, emerging trends, and future outlook
Semantic Scholar · 2025
Abstract
Generative Artificial Intelligence (GenAI) represents a transformative frontier in contemporary AI research. It is characterized by its ability to autonomously generate content with human-like creativity and complexity across various modalities, including text, images, audio, code, and scientific hypothesis generation. This survey systematically reviews the extensive evolution, cutting-edge technological innovations, and sophisticated theoretical frameworks that underpin GenAI. Utilizing a rigorous systematic literature review methodology, the paper critically analyzes peer-reviewed sources from key scholarly databases, synthesizing advancements in transformer-based architectures, quantum-enhanced generative models, and meta-learning frameworks. It also addresses critical interdisciplinary issues, particularly the ethical, societal, and philosophical implications related to generative systems, such as authorship ambiguity, algorithmic bias, misinformation propagation, and accountability deficits. By meticulously identifying existing research gaps and outlining future investigative pathways, this survey significantly enhances scholarly understanding and contributes to shaping responsible research agendas, policy formulations, and technological governance in the rapidly evolving field of generative AI.