ETHICAL USE OF LARGE LEARNING MODELS IN ACADEMIA

Raymond MAIORESCU, Augustin SEMENESCU

Abstract


This article explores the ethical integration of large language models (LLMs) in academia, focusing on authorship integrity, bias in AI outputs, and the risks of plagiarism and data fabrication. It advocates for guidelines to distinguish between human and AI contributions and highlights the need for accountability and transparency to maintain scholarly integrity. This study examines the propensity for bias in LLM-generated content and explores statistical methodologies to discern AI-generated material in educational contexts. Additionally, the paper analyzes the manifestation of bias in LLM outputs, underscoring the need for detection and correction mechanisms.


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References


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