Ethical Challenges of Large Language Models in Academic Writing

Introduction

The rapid advancement of artificial intelligence and the growing use of large language models (LLMs), such as ChatGPT, in academic writing have made questions of ethics, authenticity, and integrity increasingly important. Although LLMs are valued for their efficiency and potential benefits, their integration into academic settings also raises substantial ethical concerns.

This review draws on recent research to outline key ethical issues associated with LLM use in academic writing and to identify possible mitigation strategies.

Ethical Concerns

Authenticity and credibility are central concerns. Dergaa et al. (2023) note that LLMs can generate large quantities of information at unprecedented speed, but excessive reliance on them may marginalize the human intelligence and critical thinking that are essential to academic research.

Ownership and research integrity are also at issue. Khlaif et al. (2023) emphasize possible shortcomings in transparency and accountability when AI-generated text is used, as well as questions about ownership. Unintended plagiarism and the misrepresentation of information are particular concerns.

The increasing integration of LLMs may also contribute to plagiarism and inadequate attribution. Hosseini et al. (2022) discuss the growing average number of authors per publication (INAP), observing that an increase in INAP can amplify existing ethical issues related to authorship. These issues may particularly disadvantage junior researchers and those who perform routine tasks.

Bias and lack of transparency in the generation process can further compromise academic work. Fatani (2023) identifies the possibility of bias in LLM-generated content and concerns about how such content is produced. Biased outputs may distort academic conclusions, mislead readers, and undermine trust in research.

In health professions education (HPE), Ellaway and Tolsgaard (2023) highlight the risk that LLM use could diminish the role and expertise of human authors. Without appropriate management, LLMs may produce content that lacks the context, accuracy, and ethical alignment necessary for educating healthcare professionals.

Mitigation Strategies

Human intelligence and critical thinking should remain central to the research process. Dergaa et al. (2023) argue that this is necessary to preserve the authenticity and credibility of academic work.

Khlaif et al. (2023) suggest emphasizing research methodology, including detailed research design, tool development, and in-depth data analysis. Attention to these elements can support strong theoretical and practical implications while maintaining the depth and integrity of academic work in the AI era.

Authorship guidelines may need refinement. Hosseini et al. (2022) recommend incorporating clearer guidance into existing attribution systems, including the CRediT taxonomy and ICMJE guidelines. Changes to academic recognition and institutional promotion systems may also help ensure ethical attribution.

Academic institutions can establish clear policies governing the use of AI tools. According to Fatani (2023), such policies should emphasize originality and appropriate citation practices, alongside regular monitoring and auditing of AI-generated content.

For HPE specifically, Ellaway and Tolsgaard (2023) propose clear disclosure of LLM involvement, human control over the final product, rigorous validation and peer-review procedures, and ongoing monitoring of LLM performance.

Conclusion

LLMs offer potentially transformative possibilities for academic writing, but they also introduce ethical concerns that require careful attention. Comprehensive mitigation strategies can help the academic community use the advantages of LLMs while preserving academic integrity, authenticity, and ethical standards.

References

Dergaa I et al. From human writing to artificial intelligence generated text: examining the prospects and potential threats of ChatGPT in academic writing. Biol Sport. 2023;40(2):615–622. DOI: 10.5114/biolsport.2023.125623.

Khlaif ZN et al. The Potential and Concerns of Using AI in Scientific Research: ChatGPT Performance Evaluation. JMIR Med Educ. 2023;9:e47049. DOI: 10.2196/47049.

Hosseini M et al. An Ethical Exploration of Increased Average Number of Authors Per Publication. Sci Eng Ethics. 2022;28(3):25. DOI: 10.1007/s11948-021-00352-3.

Fatani B. ChatGPT for Future Medical and Dental Research. Cureus. 2023;15(4):e37285. DOI: 10.7759/cureus.37285.

Ellaway RH et al. Artificial scholarship: LLMs in health professions education research. Adv Health Sci Educ Theory Pract. 2023;28(3):659–664. DOI: 10.1007/s10459-023-10257-4.