Recreating Shakespeare: A Qualitative Inquiry into Authorship, Style and Simulation in the Age of Generative AI

The growing unification of generative artificial intelligence (AI) into creative practises has unresolved prolonged assumptions about individual artistry, authenticity and authorial identity. AI systems are capable of writing poetry, drama, fiction and other literary texts. The question is not about whether algorithmic machines can generate academic texts, but how they reproduce and mirror identities conventionally associated with human writers. This progress leads to a wider issue in which a well-known literary voice can be reconstructed from the patterns found in already-existing works computationally. Hence, the challenge is to figure out the relationship between style and authorship, originality and replicated text or literary recognition and individual artistic autonomy. With this upcoming backdrop, Shakespeare acts as a specifically targeted case through which we analyse the AI formation of literary identity. Shakespeare’s writing style, stylistic characteristics have become identifiable to an extent that they can be provoked as a model of machine-generated production. This research paper explores what happens to the idea of Shakespeare—as writer, stylistic features and cultural motifs when AI models are tasked with recreating him. Instead of questioning if AI can generate convincing pastiche, the paper asks a more significant question. It asks, “what kind of Shakespeare does AI actually produce and what does the formation unmask about the identity?” Relying on the poststructuralist theories of authorship (Barthes, 1967/1977; Foucault, 1969), computational creativity theory (Boden, 2004), and empirical research on stylometry and large language model (LLM) style recreation, this qualitative paper examines AI-driven Shakespeare-recreation projects — from LSTM and GPT-2 pastiche generators to modern LLM benchmarking studies. The investigation finds that AI does not recreate Shakespeare's authorial mindfulness. It reconstructs an average, decontextualized "Shakespeare-function". It is a surface level rhythm of diction, syntax, and meter taken from ancestral, sources, and biographical peculiarity. This conclusion has implications for literary pedagogy, digital humanities methodology, copyright law, and the ethics of algorithmic cultural memory