Artificial intelligence perspective on tourism education

Abstract

This study is designed with an analytical approach that compares and analyzes the views of artificial intelligence algorithms on tourism education. This study, which includes data collection, data analysis, and conclusion-drawing processes, aims to understand, evaluate, and improve the problems related to tourism education from the perspective of artificial intelligence. The questions used in the data collection phase were inspired by the 2023 theme of the 23rd National Tourism Congress, "Tourism Education." The answers obtained through four basic questions directed to ChatGBT 3.5, Jenni, Bearly, and Google Bard artificial intelligence algorithms were collected in August 2023. The average time to answer each question was between 5-20 seconds. The questions were posed in Turkish for the ChatGBT 3.5, Google Bard, and Bearly algorithms, while they were translated into English for Jenni. In the data analysis phase, the long answer texts obtained from the artificial intelligence algorithms were analyzed using the hierarchical code sub-code model of the MAXQDA24 qualitative data analysis program. The similarities and differences between the findings were interpreted. As a result of the examinations conducted, it has been observed that the most comprehensive and up-to-date data were provided by Bard and Bearly. The information provided by the ChatGBT 3.5 algorithm, being based on data up to September 2021, and Jenni's limited features being freely accessible, have been restrictive in terms of the obtained responses. When the research findings are evaluated overall, it is observed that the language used is fluent, a general-to-specific approach is adopted, and there is no significant inconsistency among the provided information.