The rapid convergence of computational creativity research and large scale generative language models has redefined the production, dissemination, and consumption of narratives across digital ecosystems. This study develops a comprehensive theoretical and empirical examination of narrative intelligence in the era of generative artificial intelligence by integrating foundational research in computational storytelling, contemporary transformer based architectures, ethical analyses of machine narratives, and emergent insights into consumer behavior and digital governance. Drawing upon narrative planning theory, mixed initiative authoring systems, encoder decoder architectures for causal reasoning in stories, prompt based learning frameworks, and large scale pre trained transformer models, the research constructs an interdisciplinary framework that links narrative generation capabilities with socio technical implications in education, marketing, and consumer protection.

The study adopts a conceptual synthesis methodology anchored in qualitative meta analysis of selected scholarly works. It reconstructs the evolution of computational storytelling from early plan based systems that balanced plot and character intentionality to transformer driven architectures such as GPT and T5 that enable few shot and prompt conditioned narrative production. Particular attention is given to the ethical challenges posed by machine generated narratives, including authorship ambiguity, bias propagation, manipulation risks, and the transformation of consumer trust. The integration of consumer behavior scholarship reveals how generative AI mediated personalization modifies trust formation, purchase behavior, and marketing ethics.

Findings indicate that transformer architectures dramatically expand narrative coherence, contextual retention, and stylistic versatility, yet remain structurally dependent on probabilistic pattern recognition rather than explicit intentional models. While narrative planning systems emphasize goal driven plot causality and character agency, large scale generative models operate through statistical abstraction and emergent structure. The study proposes a hybrid narrative intelligence model that synthesizes symbolic planning with neural generation to preserve causal integrity and ethical transparency. Furthermore, the research demonstrates that generative narratives influence consumer cognition by amplifying personalization, emotional resonance, and perceived authenticity, thereby reshaping trust dynamics and regulatory concerns.

The discussion highlights tensions between creative augmentation and autonomy displacement, explores educational transformations enabled by AI assisted storytelling, and outlines governance strategies for ethical alignment. Limitations relate to the theoretical orientation and the absence of quantitative experimentation. Future research directions include empirical validation of hybrid narrative architectures and interdisciplinary evaluation of long term socio economic impacts.

This research contributes a unified conceptual model linking computational creativity, machine learning, ethics, and consumer studies, offering a holistic perspective on narrative intelligence as both a technological and societal phenomenon.