Personal Libraries and AI-Assisted Reading: Opportunities, Risks, and Research Directions

Abstract

The article investigates how artificial intelligence (AI) can be integrated into the study, organization, and reinterpretation of personal book collections. The aim is to explore how AI transforms personal libraries from static archives into dynamic, self-reflexive systems capable of generating meaning, predicting reading needs, and personalizing intellectual experiences. The research uses a qualitative case study based on the author’s own library, focusing on a corpus of over thirty titles related to Homer. Through algorithmic analysis and structuring, the initial accumulation of books was transformed into a coherent reading itinerary, guided by thematic, chronological, and interpretative connections. The methodology combines bibliographic mapping, AI-assisted data structuring, and hermeneutic interpretation. Findings reveal that AI tools can optimize reading time, enhance comparative comprehension, and support cognitive retention, turning the library into an intellectual laboratory. However, the study also identifies risks: algorithmic uniformity, the loss of material engagement with the book, and reduced reader autonomy. The article proposes hybrid models where AI complements rather than replaces human discernment, envisioning reading as a co-creative process between human and machine. Thus, personal libraries become predictive and affective archives that reflect both cultural memory and cognitive identity. JEL: D83, L86, O33, Z11, Z18