Exploring the role of large language model in collaborative travel planning task

Abstract

Introduction. This study explores how generative AI, specifically ChatGPT, influences collaborative travel planning. Understanding its effect on tasks like trip planning reveals insights into human-AI collaboration, particularly how AI tools support decision-making and streamline information gathering.

Method. Twenty participants (10 pairs) planned a 1-night, 2-day trip under two conditions: (1) using only Google, and (2) using Google with ChatGPT. This within-subject study measured completion time, satisfaction, and plan quality via questionnaires and observation to capture task performance, user behaviour, and collaboration dynamics.

Analysis. Data was analysed using t-tests and Wilcoxon signed-rank tests to compare completion times, satisfaction, and plan quality. Analyses of conversation volume and ChatGPT logs provided insights into AI-assisted collaboration dynamics and interaction patterns.

Results. No significant difference in task completion time was found. However, plans made with ChatGPT were more complete and aligned with requirements. Participants found information more easily with ChatGPT, but satisfaction levels remained similar, suggesting that easier information access did not translate to higher satisfaction.

Conclusions. Generative AI improves collaborative search task quality but does not enhance efficiency or satisfaction. AI tools like ChatGPT are effective for providing structured information and are best used as complementary resources alongside traditional search engines in planning tasks.