Readers' Engagement With and Perception of AI-Generated Narratives in the Context of Digital Literature

With the growing capabilities of advanced language models, artificial intelligence (AI) is increasingly contributing to storytelling, raising questions about narrative coherence, emotional resonance, and the impact of authorship disclosure. The research explores how readers engage with and perceive AI-generated narratives in the evolving landscape of digital literature. It examines six key factors: Narrative Coherence (NC), Emotional Resonance (ER), Disclosure of AI Authorship (DAI), Reader Engagement (RE), Narrative Preference (NP), and Narrative Genre Preference (NGP) through a structured survey of 500 participants using a five-point Likert scale. The research has used the SPSS-29 and considers statistical methods like ANOVA, Chi-Square tests, and descriptive statistics. Hypothetical pathway analysis was employed to evaluate reader responses and identify significant patterns. Findings reveal that NC and ER positively influence RE, while DAI negatively affects NP. Among all hypotheses tested, the most significant was H5, demonstrating that reader engagement strongly enhances narrative preference (β = 0.47, p = 0.000). ANOVA results confirmed meaningful engagement differences across demographic groups (F = 4.73, p = 0.003), and Chi-Square tests indicated significant associations for all six variables, especially DAI (χ² = 15.87, p = 0.0001). Descriptive statistics highlighted high overall scores for NC and RE, and mixed responses for DAI. These results emphasize the importance of emotional and structural quality in AI storytelling and illustrates reader sensitivity to machine authorship. The research contributes to the understanding of AI's role in narrative creation and offers a data-driven framework for designing engaging, emotionally compelling, and contextually aware AI-generated literature.