Large language models as travel co-planners : the GIFT model of youth–AI interaction

This study was reviewed and approved by the Corvinus University of Budapest Research Ethics Committee (Hungary). Approval ID: KRH/250/2025. Date of approval: 17 October 2025. Ethical approval was obtained retrospectively, after the data collection was completed. At the time of conducting the focus group interviews, all participants (and guardians, in the case of minors) had already provided written informed consent, in line with international ethical guidelines. Given that the research involved non-invasive, low-risk, anonymized group discussions without any sensitive topics or interventions, the retrospective approval process was acceptable under institutional policy. The ethics committee reviewed all relevant documentation (e.g., consent forms, anonymization procedures, recruitment protocols) before granting the official approval. The study was conducted in accordance with the university’s Rectoral Provision No. 2/2020 (V.26) and complied with internationally accepted research ethics standards.

Abstract

This study examines how members of Generation Z and Generation Alpha perceive and use LLMs in tourism planning. Drawing on six age-specific focus groups with 57 participants (26 Generation Alpha pupils aged 11–14 and 31 Generation Z BSc students aged 20–24) conducted in Budapest, Hungary, the study uses a qualitative, Grounded Theory–informed approach to explore prior use, anticipated practices, credibility assessments, and the meanings attached to LLMs. Participants in both cohorts were familiar with digital technologies and valued fast, personalized services, but their accounts differed in decision-making autonomy, social embeddedness, and evaluative practices. Generation Alpha participants more often positioned LLMs as an exploratory assistant within family-based decision structures, whereas Generation Z participants more frequently described comparing or verifying AI-generated outputs through other sources and social input. These patterns are better understood as different modes of credibility assessment and reliance than as simple differences in trust. The resulting GIFT (Generational Interpretations of Future-oriented Technologies) framework conceptualizes youth–AI interaction in tourism planning as contextdependent, with LLMs serving as cognitive support tools and potential co-creators. GIFT is presented as an exploratory framework grounded in this sample rather than as a universal theory. The study links human–AI interaction to the social contexts in which young people make travel decisions and offers implications for tourism professionals, educators, and AI developers.