Reframing tour guiding in the age of generative AI: a framework for self-guided tourism experiences

Abstract

This paper explores how generative AI (GAI) may complement, extend or selectively assume information-based functions traditionally associated with human tour guiding in self-guided tourism experiences (SGE). It presents a new framework for GAI-driven SGE, highlighting three central aspects: personalization, real-time support and contextual relevance. To map the relationship between GAI, on-site information-seeking behavior and SGE, we adapted a structured approach based on MacInnis' (2011) framework for explicating (descriptive) conceptual contributions. By utilizing GAI's features, the study shows how GAI may improve tourist independence and convenience. The paper also evaluates the limitations of GAI, particularly its difficulty in replicating the emotional connections, cultural understanding and narrative immersion that human guides provide. Through a comparison with traditional guided tours, the research discusses the consequences of adopting GAI for tourists, service providers and destination management organizations (DMOs). Ethical issues, including data privacy concerns and the potential for cultural inaccuracies, are also explored, along with proposed strategies for responsible implementation. This work lays the groundwork for future studies and real-world applications, offering insights into how GAI may make tourism more adaptable, inclusive and sustainable. While the paper focuses on how GAI may support or selectively assume specific information-based functions of guiding, we recognize that tour guiding is also a form of embodied, relational, and regulated labor that extends beyond the scope of this conceptual framework.