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Introducing AIRSim: An innovative AI-driven feedback generation tool for supporting student learning

Leong, Kelvin
; 
Sung, Anna
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Affiliation
University of Chester
EPub Date
Publication Date
2025-03-10
Submitted Date
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Chester Business School
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Abstract
This paper introduces AIRSim (AI Responses Simulator), an innovative AI tool designed to support students in practicing their questionnaire analysis skills within the café and restaurant discipline. Utilizing artificial intelligence (AI), AIRSim generates hypothetical feedback data to facilitate student learning. Through a series of 16 experiments, we evaluated AIRSim’s 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 café and restaurant discipline. By introducing the AIRSim tool, educators can efficiently enhance their students’ 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.
Citation
Leong, K., & Sung, A. (2025). Introducing AIRSim: An innovative AI-driven feedback generation tool for supporting student learning. Technology, Knowledge and Learning, vol(issue), pages. https://doi.org/10.1007/s10758-025-09835-9
Publisher
Springer
Journal
Technology, Knowledge and Learning
Research Unit
URI
http://hdl.handle.net/10034/629291
DOI
10.1007/s10758-025-09835-9
PubMed ID
PubMed Central ID
Type
Article
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Description
The version of record of this article, first published in [Technology, Knowledge and Learning], is available online at Publisher’s website: http://dx.doi.org/10.1007/s10758-025-09835-9
Series/Report no.
ISSN
2211-1662
EISSN
2211-1670
ISBN
ISMN
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Sponsors
Unfunded
Additional Links
https://link.springer.com/article/10.1007/s10758-025-09835-9