Date of Graduation

5-2026

Document Type

Thesis

Degree Name

Master of Arts in Journalism (MA)

Degree Level

Graduate

Department

Journalism

Advisor/Mentor

Hu, Sisi

Committee Member

Chung, Jee Young

Second Committee Member

Reed, Joel

Third Committee Member

Johnston, Nick

Keywords

Artificial Intelligence; Gatekeeping; Tourism; Travel Planning

Abstract

As artificial intelligence chatbots become a primary tool for travel discovery, questions arise about whose destinations get recommended and how. This study examines how ChatGPT (OpenAI), Claude (Anthropic), and Google Gemini function as algorithmic gatekeepers in travel recommendations, drawing on gatekeeping theory, framing theory, and growing scholarship on AI bias in tourism. Using a content analysis of 135 AI-generated responses to 15 standardized travel prompts (45 per platform, each run three times) this study coded destination selection patterns, dominant narrative frames, and informational omissions across platforms. Results show that 88.9% of recommended destinations were classified as high-income nations, with Japan, New Zealand, Italy, Portugal, and Iceland accounting for a disproportionate share of all mentions. Cultural/Historical (95.6%), Adventure (89.6%), and Relaxation/Escape (84.4&) frames dominated across all three platforms, while Family-Friendly (3.7%) and Solo Travel (5.9%) frames were nearly absent. Critical practical information was broadly omitted: visa requirements appeared in only 0.7% of responses and budget estimates in just 7.4%, while activity recommendations appeared in 95.6%. Meaningful platform differences were found in Budget/Affordable framing, with Claude applying it in 75.6% of responses compared to 33.3% for ChatGPT and 35.6% for Gemini. These findings suggest that AI chatbots operate less as balanced travel advisors and more as promotional gatekeepers that amplify existing tourism hierarchies, narrow destination representation, and omit the practical information travelers need for informed decision-making.

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