Travel Literature as Hypertext: Definition and Case Study of AI-enabled Interactive Explorations

Abstract

Travel literature is a unique form of hypertext that extends beyond its medium into real-world physical exploration. While conventional computational methods can easily extract surface-level ontological entities (e.g., locations, dates), the deeper epistemic and judgmental subtext that guides the discovery and evaluation of places has traditionally required close human reading. To address this gap, this paper explores the capacity of Large Language Models (LLMs) to complement classical computing by performing interpretive analysis of deeper textual characteristics. We present a proof-of-concept case study using an LLM and Retrieval-Augmented Generation (RAG) on a corpus of over 600 digitised travel literature. By employing LLMs as hypertext engines to structure and apply analytical frameworks in collaboration with scholars, we demonstrate the feasibility of using generative AI for the interpretative study of unstructured corpora. This contribution provides actionable insights into system architecture, AI roles, and interaction design, advancing the broader integration of LLM technologies within the hypertext paradigm and Digital Humanities research infrastructures.