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Navigating ethical challenges in generative AI-enhanced research: The ETHICAL framework for responsible generative AI use

Eacersall, Douglas; Pretorius, Lynette; Smirnov, Ivan; Spray, Erika; Illingworth, Sam; Chugh, Ritesh; Strydom, Sonja; Stratton-Maher, Dianne; Simmons, Jonathan; Jennings, Isaac; Roux, Rian; Kamrowski, Ruth; Downie, Abigail; Ling Thong, Chee; Howell, Katharine A.

Authors

Douglas Eacersall

Lynette Pretorius

Ivan Smirnov

Erika Spray

Ritesh Chugh

Sonja Strydom

Dianne Stratton-Maher

Jonathan Simmons

Isaac Jennings

Rian Roux

Ruth Kamrowski

Abigail Downie

Chee Ling Thong

Katharine A. Howell



Abstract

The rapid adoption of generative artificial intelligence (GenAI) in research presents both opportunities and ethical challenges that should be carefully navigated. Although GenAI tools can enhance research efficiency by automating tasks such as literature reviews and data analysis, their use raises concerns about aspects including data accuracy, privacy, bias, and research integrity. This paper proposes the ETHICAL framework, which is a practical guide for responsible GenAI use in research. Employing a multi-stage single case study design, we examine multiple GenAI tools in real research contexts to develop the ETHICAL framework, which consists of seven key principles: Examine policies and guidelines, Think about social impacts, Harness understanding of the technology, Indicate use, Critically engage with outputs, Access secure versions, and Look at user agreements. Applying these principles will enable researchers to uphold research integrity while leveraging the benefits of GenAI. The framework addresses a critical gap between awareness of ethical issues and practical action steps, providing researchers with concrete guidance for ethical GenAI integration. This work has implications for research practice, institutional policy development, and the broader academic community as researchers adapt to an AI-enhanced research landscape. The ETHICAL framework can also serve as a foundation for developing AI literacy in academia and promoting responsible GenAI adoption in research settings.

Citation

Eacersall, D., Pretorius, L., Smirnov, I., Spray, E., Illingworth, S., Chugh, R., Strydom, S., Stratton-Maher, D., Simmons, J., Jennings, I., Roux, R., Kamrowski, R., Downie, A., Ling Thong, C., & Howell, K. A. (2025). Navigating ethical challenges in generative AI-enhanced research: The ETHICAL framework for responsible generative AI use. Journal of Applied Learning & Teaching, 8(2), https://doi.org/10.37074/jalt.2025.8.2.9

Journal Article Type Article
Acceptance Date Jul 22, 2025
Online Publication Date Jul 23, 2025
Publication Date 2025
Deposit Date Jul 23, 2025
Publicly Available Date Jul 24, 2025
Journal Journal of Applied Learning & Teaching
Print ISSN 2591-801X
Electronic ISSN 2591-801X
Publisher Journal of Applied Learning & Teaching
Peer Reviewed Peer Reviewed
Volume 8
Issue 2
DOI https://doi.org/10.37074/jalt.2025.8.2.9
Keywords Academic integrity, AI literacy, artificial intelligence, ethical AI use, generative artificial intelligence, research ethics
This output contributes to the following UN Sustainable Development Goals:

SDG 4 - Quality Education

Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all

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