Introducing AIRSim: An Innovative AI-Driven Feedback Generation Tool for Supporting Student Learning DOI
Anna Sung, Kelvin Leong

Опубликована: Май 30, 2024

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), generates hypothetical feedback data facilitate student learning. Through a series of 16 experiments, we evaluated AIRSim's capability simulating participant responses user-uploaded questionnaires. Our findings demonstrated notable degree diversity generated results, as indicated by Entropy Index, across various perspectives participant-question combinations. To best our knowledge, there exists lack relevant studies exploring this specific application context learning By introducing tool, educators can efficiently enhance students' analytical abilities responsiveness customer needs. practical contribution addresses pressing need for effective training methods hospitality sector while also capitalizing on transformative potential Generative technologies, such ChatGPT. Overall, study provides valuable insights into AI-driven identifies areas future research.

Язык: Английский

Large language models can help boost food production, but be mindful of their risks DOI Creative Commons
Djavan De Clercq,

Elias Nehring,

Harry Mayne

и другие.

Frontiers in Artificial Intelligence, Год журнала: 2024, Номер 7

Опубликована: Окт. 25, 2024

Coverage of ChatGPT-style large language models (LLMs) in the media has focused on their eye-catching achievements, including solving advanced mathematical problems and reaching expert proficiency medical examinations. But gradual adoption LLMs agriculture, an industry which touches every human life, received much less public scrutiny. In this short perspective, we examine risks opportunities related to more widespread food production systems. While can potentially enhance agricultural efficiency, drive innovation, inform better policies, challenges like misinformation, collection vast amounts farmer data, threats jobs are important concerns. The rapid evolution LLM landscape underscores need for policymakers think carefully about frameworks guidelines that ensure responsible use before these technologies become so ingrained policy intervention becomes challenging.

Язык: Английский

Процитировано

6
The Role of Generative Artificial Intelligence in Digital Agri-Food DOI Creative Commons
Sakib Shahriar, Maria G. Corradini, Shayan Sharif

и другие.

Journal of Agriculture and Food Research, Год журнала: 2025, Номер unknown, С. 101787 - 101787

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
Introducing AIRSim: An Innovative AI-Driven Feedback Generation Tool for Supporting Student Learning DOI Creative Commons
Kelvin Leong, Anna Sung

Technology Knowledge and Learning, Год журнала: 2025, Номер unknown

Опубликована: Март 10, 2025

Язык: Английский

Процитировано

0
Harnessing artificial intelligence for advancements in Rice / wheat functional food Research and Development DOI

Fangye Zeng,

Min Zhang, Chung Lim Law

и другие.

Food Research International, Год журнала: 2025, Номер unknown, С. 116306 - 116306

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
Generative AI for Smallholder Agricultural Advice in Sub-Saharan Africa DOI
Joyce Nakatumba‐Nabende, Ann Lisa Nabiryo, Peter Nabende

и другие.

Oxford University Press eBooks, Год журнала: 2025, Номер unknown

Опубликована: Апрель 22, 2025

Abstract Smallholder farmers are prone to food insecurity due the devastating effects of viral crop diseases, pest outbreaks, and lack timely, targeted advice. Leveraging Large Language Models (LLMs) in agriculture offers significant potential bridge information gaps that smallholder face. This study discusses development an expert-reviewed agricultural question-answer dataset. We analysed responses from LLMs experts on crop- animal-related questions using relevancy, coherence, fluency metrics. Our results show GPT-4 outperforms other across these LLM-powered systems can act as virtual extension agents, assisting decision-making overcoming farming challenges.

Язык: Английский

Процитировано

0
Introducing AIRSim: An Innovative AI-Driven Feedback Generation Tool for Supporting Student Learning DOI
Anna Sung, Kelvin Leong

Опубликована: Май 30, 2024

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), generates hypothetical feedback data facilitate student learning. Through a series of 16 experiments, we evaluated AIRSim's capability simulating participant responses user-uploaded questionnaires. Our findings demonstrated notable degree diversity generated results, as indicated by Entropy Index, across various perspectives participant-question combinations. To best our knowledge, there exists lack relevant studies exploring this specific application context learning By introducing tool, educators can efficiently enhance students' analytical abilities responsiveness customer needs. practical contribution addresses pressing need for effective training methods hospitality sector while also capitalizing on transformative potential Generative technologies, such ChatGPT. Overall, study provides valuable insights into AI-driven identifies areas future research.

Язык: Английский

Процитировано

0