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Ethical implications of ChatGPT and other large language models in academia

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Mixed-methods analysis reveals concerns about plagiarism and bias in LLM adoption in academia.

Substantial adoption of large language models raises concerns about academic integrity and plagiarism.
Key findings show a significant prevalence of ethical concerns including bias and authenticity in LLM use.
The mixed-methods approach included surveys and focus groups with students, faculty, and AI developers.
Guidelines for responsible integration of LLMs emphasize transparency and ongoing stakeholder dialogue.

Almufarreh et al. (2025) studied this question.

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View Full Paper

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Authors

Mixed-methods analysis reveals concerns about plagiarism and bias in LLM adoption in academia.

Substantial adoption of large language models raises concerns about academic integrity and plagiarism.
Key findings show a significant prevalence of ethical concerns including bias and authenticity in LLM use.
The mixed-methods approach included surveys and focus groups with students, faculty, and AI developers.
Guidelines for responsible integration of LLMs emphasize transparency and ongoing stakeholder dialogue.

Almufarreh et al. (2025) studied this question.

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