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Machine vs. Mind: Assessing AI's Ability to Mimic Human Authorship in Fiction
Abstract
As the computational power increases and cloud computing becomes more accessible, computers are exploring the avenues that were earlier thought to be far-fetched. The question raised in 1950 by Alan Turing, 'Can machines think?', persists as a philosophical question. This study examines how well the Turing Test assesses artificial intelligence's capacity to produce literary works that resemble those of humans. In literary and computational linguistics, the topic of whether machine-generated narratives can be differentiated from human-authored works becomes more important as AI-generated content grows more complex. By contrasting a human-written short narrative with an AI-generated version of the same, this study investigates this subject by evaluating respondents' ability to distinguish between the two using qualitative literary criteria. The AI model was given instructions to read the original text and rewrite it with certain changes while preserving its emotional impact and depth. To assess both texts on several criteria, such as clarity, organisation, vocabulary, engagement, character development, and emotional impact, a standardised questionnaire was created. Respondents were also asked to rate the quality of the text using the above parameters, indicate whether each text was created by AI or by humans, and justify their classifications. People from a variety of backgrounds participated in this survey, guaranteeing a diverse sample of respondents for a more thorough analysis. By classifying respondents according to the language they were taught in elementary and secondary school, the questionnaire also took linguistic background into consideration. The study also examined whether gender affected how texts were seen and how accurately they were classified. This study emphasises how crucial it is to critically interact with AI-generated content in professional, artistic, and academic contexts. Understanding artificial intelligence's effects on authorship, authenticity, and literary value is still essential as it continues to influence the landscape of literary production and consumption. This study highlights the need for creating new frameworks for assessing and interpreting machine-generated narratives and lays the groundwork for future research into the nexus of AI and literature. As some of the old questions get answered, this research raises new questions related to the nature of the creation of art. Authors through this study arrive at the conclusion that maybe the Turing test will let us understand more about human beings than they do about computers.
PCF11 Sub-Theme: Gender, Technology and Innovation in Open Education
Paper ID: 8462
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Global
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Date
2025-09
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Commonwealth of Learning (COL)