Today, Artificial Intelligence (AI) is rapidly transforming digitalstorytelling through advances in text generation, multimodalsynthesis, and interactive narrative systems. Large LanguageModels (LLMs), vision-language models, and generative mediamodels make it possible for the creators to design adaptivemultimedia content stories, images, and auditory environmentsthat can be created with less manual work. This paper suggests aconceptual framework for understanding practicing AI-enabledstorytelling as human-AI collaborative production. Instead ofdiscussing an actual implemented model, the paper synthesizesexisting research in AI, narrative theory, and digital mediato introduce the AI-Assisted Storytelling Model (AASM) asan analytical and organizational framework. The paper talksabout narrative aspects, multimodal alignment, Interactivity,applications, and ethical issues to be supported/reviewed futureempirical and creative research.

Large Language Models (LLMs) have rapidly become a central focus in both research and practical applications, owing to their remarkable ability to understand and generate text with a level of fluency comparable to human communication. Recently, these models have evolved into multimodal large language models (MM-LLMs), extending their capabilities beyond text to include images, audio, and video. This advancement has enabled a wide array of applications, including text-to-video synthesis, image captioning, and text-to-speech systems. MM-LLMs are developed either by augmenting existing LLMs with multi-modal functionality or by designing multi-modal architectures from the ground up. This paper presents a comprehensive review of the current landscape of LLMs with multi-modal capabilities, highlighting both foundational and cutting-edge MM-LLMs. It traces the historical development of LLMs, emphasizing the transformative impact of transformer-based architectures such as OpenAI's GPT series and Google's BERT, as well as the role of attention mechanisms in improving model performance. The review also examines key strategies for adapting pre-trained models to specific tasks, including fine-tuning and prompt engineering. Ethical challenges, including data bias and the potential for misuse, are discussed to stress the importance of responsible AI deployment. Finally, we explore the implications of open-source versus proprietary models for advancing research in this field. By synthesizing these insights, this paper underscores the significant potential of MM-LLMs to reshape diverse applications across multiple domains.

Artificial Intelligence (AI) has evolved remarkably over the past seven decades, transforming from simple rule-based systems into complex multimodal and generative frameworks capable of reasoning, creativity, and perception. This review traces the chronological development of AI tools, highlighting key milestones that shaped the field—from the early symbolic programs like Logic Theorist and ELIZA to the emergence of modern large-scale models such as GPT-4, Gemini, and Claude. The study explores the progression across distinct eras: the foundational period of symbolic reasoning (1940s–1970s), the rise of machine learning and statistical modeling (1980s–2000s), the deep learning revolution (2010s), and the recent explosion of generative and multimodal systems (2020–2025). Each phase reflects a major shift in how intelligence is defined, represented, and implemented—from handcrafted logic to data-driven learning and now to context-aware multimodal understanding. By reviewing over fifty significant AI tools and frameworks, this paper provides a comprehensive overview of how incremental innovations in computation, data availability, and model architecture have collectively enabled the current state of AI. The work concludes with insights on how this evolution paves the way for the next generation of agentic and real-time AI systems capable of seamless interaction across text, image, audio, and video modalities.

Artificial Intelligence (AI) has evolved remarkably over the past seven decades, transforming from simple rule-based systems into complex multimodal and generative frameworks capable of reasoning, creativity, and perception. This review traces the chronological development of AI tools, highlighting key milestones that shaped the field-from the early symbolic programs like Logic Theorist and ELIZA to the emergence of modern large-scale models such as GPT-4, Gemini, and Claude. The study explores the progression across distinct eras: the foundational period of symbolic reasoning (1940s-1970s), the rise of machine learning and statistical modeling (1980s-2000s), the deep learning revolution (2010s), and the recent explosion of generative and multimodal systems (2020-2025). Each phase reflects a major shift in how intelligence is defined, represented, and implemented-from handcrafted logic to data-driven learning and now to context-aware multimodal understanding. By reviewing over fifty significant AI tools and frameworks, this paper provides a comprehensive overview of how incremental innovations in computation, data availability, and model architecture have collectively enabled the current state of AI. The work concludes with insights on how this evolution paves the way for the next generation of agentic and real-time AI systems capable of seamless interaction across text, image, audio, and video modalities.

Potential enabling artificial intelligence (AI) to fundamentally transform both businesses along with civilizations has made AI a revolutionary force. AI has many beneficial applications, but there are also many moral dilemmas that come with its development. With the increasing integration of AI systems into every aspect of lives, there is an increased possibility of unforeseen effects and ethical conflicts.The lack of information’s gathering, sharing, as well as readability; the inability to demonstrate the internal decision-making process; and the disregard for ethical considerations in the creation of AI frameworks are some possible impediments to the ordinary application of AI. In artificial intelligence, “byte-sized morality” corresponds to the moral precepts and considerations that are ingrained in AI systems including are frequently constrained by information in digital format, algorithms, and analytical procedures. In this paper aims to contribute to ongoing ethical AI discussion, By examining these advancing understanding of legal obligations that accompany the advancement developing artificial intelligence in the technologically advanced society.

Large Language Models (LLMs) have rapidly become a central focus in both research and practical applications, owing to their remarkable ability to understand and generate text with a level of fluency comparable to human communication. Recently, these models have evolved into multimodal large language models (MM-LLMs), extending their capabilities beyond text to include images, audio, and video. This advancement has enabled a wide array of applications, including text-to-video synthesis, image captioning, and text-to-speech systems. MM-LLMs are developed either by augmenting existing LLMs with multi-modal functionality or by designing multi-modal architectures from the ground up. This paper presents a comprehensive review of the current landscape of LLMs with multi-modal capabilities, highlighting both foundational and cutting-edge MM-LLMs. It traces the historical development of LLMs, emphasizing the transformative impact of transformer-based architectures such as OpenAI's GPT series and Google's BERT, as well as the role of attention mechanisms in improving model performance. The review also examines key strategies for adapting pre-trained models to specific tasks, including fine-tuning and prompt engineering. Ethical challenges, including data bias and the potential for misuse, are discussed to stress the importance of responsible AI deployment. Finally, we explore the implications of open-source versus proprietary models for advancing research in this field. By synthesizing these insights, this paper underscores the significant potential of MM-LLMs to reshape diverse applications across multiple domains.