The ETHICAL Protocol for Responsible Use of Generative AI for Research Purposes in Higher Education

Abstract

Generative AI's growing use in higher education research requires strong protocols for responsible use. This need arises from the potential for misuse and the current uncertainty around ethical concerns and intellectual property. The lack of clear rules about openness in AI use, along with the “black box” nature of many AI systems, raises worries about reproducibility and the possibility of biased or fake results. This paper focuses specifically on generative AI tools (e.g., LLMs like ChatGPT, research-specific platforms like Elicit/SciSpace). The paper presents the ETHICAL protocol (i.e., Establish your purpose, Thoroughly explore options, Harness the appropriate tool, Inspect and verify output, Cite and reference accurately, Acknowledge AI usage transparently, and Look over publisher's guidelines), a detailed guide designed to direct researchers in the ethical and responsible inclusion of generative AI in their work. The protocol was created through a multi-step process, including a scientometric review of current trends, a systematic review of researcher experiences, and a policy analysis of 74 documents from various stakeholders (authorities, universities, publishers, and publication manuals). This analysis shaped the creation of a seven-heading, nine-item checklist covering key aspects of responsible AI use, from setting clear research goals to checking outputs and openly acknowledging AI help. The ETHICAL protocol gives practical examples and detailed explanations for each item, highlighting the importance of AI literacy and careful choice of suitable tools. It also stresses the vital need for checking AI-generated content to lessen the risk of errors and made-up information (“hallucinations”). The resulting protocol offers a practical and easy-to-use guide for researchers, encouraging responsible AI practices and upholding academic integrity. The ETHICAL protocol offers a helpful tool for managing the complex area of AI in research, ultimately leading to more open, reliable, and ethically sound scholarly work. Its broad acceptance could greatly improve the responsible use of AI in higher education, building trust and furthering knowledge growth.