Utilizing Open Access Publications and Educational Content in LLM-Based Research: Global Trends and Ethical–Policy Considerations
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Large language models (LLMs) have become increasingly ingrained in academic researchand education. As a result, leveraging open-access publications and publicly availableeducational materials through LLMs is emerging as a promising strategy to broadenknowledge discovery and enhance teaching and learning. This review surveys global anddomestic trends in the application of LLMs within science and education, highlightingnotable implementations including Meta’s Galactica, KISTI’s KONI model, and the opensourceCleverBee system. These examples showcase the substantial potential of generativeAI when applied to openly available knowledge resources, while also revealing importantlimitations. In this context, we identify six key ethical challenges associated with LLMuse—covering issues of copyright and licensing, personal data privacy, hallucinated content,algorithmic bias, plagiarism, and accountability—and examine evolving legal and regulatoryresponses in the United States, European Union, and South Korea. Additionally, we proposeseven practical recommendations for researchers (such as checking data licenses, removingpersonal identifiers, verifying model outputs, and transparently reporting AI assistance) tofoster responsible use of LLMs. Drawing on up-to-date guidelines from organizations like theCommittee on Publication Ethics (COPE) and the International Committee of Medical JournalEditors (ICMJE) as well as recent national policy directives, this review offers comprehensiveguidance for scholars across disciplines including neuroscience, linguistics, learning sciences,and artificial intelligence. Our aim is to support the responsible and effective integration ofLLM technologies into academic practice—advancing innovation in research and educationwhile maintaining high ethical and legal standards.
Abstract
Large language models (LLMs) have become increasingly ingrained in academic researchand education. As a result, leveraging open-access publications and publicly availableeducational materials through LLMs is emerging as a promising strategy to broadenknowledge discovery and enhance teaching and learning. This review surveys global anddomestic trends in the application of LLMs within science and education, highlightingnotable implementations including Meta’s Galactica, KISTI’s KONI model, and the opensourceCleverBee system. These examples showcase the substantial potential of generativeAI when applied to openly available knowledge resources, while also revealing importantlimitations. In this context, we identify six key ethical challenges associated with LLMuse—covering issues of copyright and licensing, personal data privacy, hallucinated content,algorithmic bias, plagiarism, and accountability—and examine evolving legal and regulatoryresponses in the United States, European Union, and South Korea. Additionally, we proposeseven practical recommendations for researchers (such as checking data licenses, removingpersonal identifiers, verifying model outputs, and transparently reporting AI assistance) tofoster responsible use of LLMs. Drawing on up-to-date guidelines from organizations like theCommittee on Publication Ethics (COPE) and the International Committee of Medical JournalEditors (ICMJE) as well as recent national policy directives, this review offers comprehensiveguidance for scholars across disciplines including neuroscience, linguistics, learning sciences,and artificial intelligence. Our aim is to support the responsible and effective integration ofLLM technologies into academic practice—advancing innovation in research and educationwhile maintaining high ethical and legal standards.