- Part I (conceptual entry): What Is AI Authorship? From Human Genius to Digital Persona
- Part II (how models write): How Large Language Models Write: AI Text Generation Explained
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Part III (role taxonomy): AI as Tool, Co-Author, or Creator? Three Models of AI Authorship
- Part IV (intent and mind): AI Authorship, Intent, and Consciousness: Do You Need a Mind to Be an Author?
- Part V (originality and plagiarism): Originality, Remix, and Plagiarism in AI-Generated Content
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Part VI (data and labor): Training Data, Invisible Labor, and Collective Memory in AI Writing
- Part VII (glitch aesthetics): Glitch Aesthetics: AI Hallucinations Between Error and Imagination
- Part VIII (search ecology): AI Content and SEO: How Automation Creates a Flood of Noise
- Part IX (identity layer): From Human Author to Digital Persona: Digital Identity in AI Authorship
- Part X (postsubjective meaning): Postsubjective AI Authorship: Can Meaning Exist Without a Self?
- Part XI (workflow design): Hybrid Authorship in Practice: Designing Human–AI Writing Workflows
- Part XII (credits and metadata): Attribution in the Age of AI: Credits, Metadata, and Structural Authorship
- Part XIII (reader psychology): How Readers Perceive AI-Written Texts: Trust, Bias, and the Uncanny Author
- Part XIV (case studies): Case Studies in AI Authorship: Art, Literature, Code, and Research
- Part XV (professional shift): The Future of Creative Professions in an AI-Authored World
- Part XVI (operational ethics): Guidelines for Using AI as an Author and Co-Creator