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Paper Type

ERF

Abstract

In the battle of Large Language Models (LLMs), OpenAI's GPT-4, Google's Gemini, and Anthropic’s Claude are considered heavyweights. The release of LLMs has elicited both excitement and anxiety from all levels of academia, higher education notwithstanding. Educators fall on both ends of spectrum. Proponents tout the revolutionary nature of LLMS while opponents express concern over the potential increase in cheating, plagiarism, and unethical use. One such area that has drawn the interest of educators is the creation of educational content. In particular, this study examines the use of LLMs to generate information systems (IS) teaching cases. As such, this study aims to (1) determine the ability of Copilot, Gemini, and Claude to generate reasonable and educationally relevant teaching cases, (2) compare the quality and educational suitability of teaching cases generated by these LLMs, and (3) understand the limitations and strengths of each LLM in generating teaching cases.

Paper Number

1610

Author Connect URL

https://authorconnect.aisnet.org/conferences/AMCIS2024/papers/1610

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Aug 16th, 12:00 AM

Battle of the Large Language Models: Crowning the Winner for Generating Information Systems Teaching Cases

In the battle of Large Language Models (LLMs), OpenAI's GPT-4, Google's Gemini, and Anthropic’s Claude are considered heavyweights. The release of LLMs has elicited both excitement and anxiety from all levels of academia, higher education notwithstanding. Educators fall on both ends of spectrum. Proponents tout the revolutionary nature of LLMS while opponents express concern over the potential increase in cheating, plagiarism, and unethical use. One such area that has drawn the interest of educators is the creation of educational content. In particular, this study examines the use of LLMs to generate information systems (IS) teaching cases. As such, this study aims to (1) determine the ability of Copilot, Gemini, and Claude to generate reasonable and educationally relevant teaching cases, (2) compare the quality and educational suitability of teaching cases generated by these LLMs, and (3) understand the limitations and strengths of each LLM in generating teaching cases.

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