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Utilizing artificial intelligence (AI), AIRSim generates hypothetical feedback data to facilitate student learning. Through a series of 16 experiments, we evaluated AIRSim\u2019s capability in simulating participant responses to user-uploaded questionnaires. Our findings demonstrated a notable degree of diversity in the generated results, as indicated by the Entropy Index, across various perspectives and participant-question combinations. To the best of our knowledge, there exists a lack of relevant studies exploring this specific application of AI in the context of student learning within the caf\u00e9 and restaurant discipline. By introducing the AIRSim tool, educators can efficiently enhance their students\u2019 analytical abilities and responsiveness to customer needs. This practical contribution addresses the pressing need for effective training methods in the hospitality sector while also capitalizing on the transformative potential of Generative AI technologies, such as ChatGPT. Overall, this study provides valuable insights into AI-driven student learning and identifies areas for future research.\n                  <\/jats:p>","DOI":"10.1007\/s10758-025-09835-9","type":"journal-article","created":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T01:16:05Z","timestamp":1741569365000},"page":"2393-2415","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Introducing AIRSim: An Innovative AI-Driven Feedback Generation Tool for Supporting Student Learning"],"prefix":"10.1007","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5896-0181","authenticated-orcid":false,"given":"Kelvin","family":"Leong","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0801-3119","authenticated-orcid":false,"given":"Anna","family":"Sung","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,10]]},"reference":[{"key":"9835_CR1","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.ijhm.2017.07.001","volume":"66","author":"JA Alhelalat","year":"2017","unstructured":"Alhelalat, J. 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