Considerations for the Responsible and Ethical Use of AI
Drafted: Nov. 25, 2025 | Last Revised: May 19, 2026
Virginia Tech’s Considerations for Researchers on the Use of AI translates the university’s Responsible and Ethical AI Framework (2025) and the Generative AI Delegation Taxonomy (GAIDeT) (Suchikova et al., 2025) into practical steps for researchers. It provides stage-specific considerations for using generative AI responsibly across the stages of the research lifecycle as identified by the GAIDeT. These seven stages include conceptualization, literature review, methodology, software development, data management, writing, and ethical oversight. Each of the seven stages is discussed in terms of the seven principles in Virginia Tech's Responsible and Ethical AI Framework.
Responsible Use of AI Across the Research Lifecycle
Select the stage that best matches where you are in the research process. Each stage links to considerations for responsible and ethical AI use.
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Conceptualization
Ethical Use of AI in the Early Stages of a Research Project
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Literature Review
Ethical Use of Generative AI in Literature Review Process
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Methods
Ethical Use of AI, Including Generative AI, in Research Design and Analysis Planning
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Software Development & Automation
Ethical Use of AI for Algorithm Design, Code Generation, and Optimization
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Data Management & Analysis
Ethical Use of AI in Data Collection, Processing, and Visualization
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Writing & Editing
Ethical Use of AI in Scholarly Communication and Dissemination
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Ethics Review
Responsible Use of AI in Research Oversight and Ethical Risk Assessment
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Publication Support
Responsible Use of AI in Research Dissemination and Communication
In addition to the seven GAIDeT stages, we acknowledge that researchers engage in peer and proposal review as part of their academic responsibilities. Many journals and sponsors prohibit the use of AI tools in these contexts, and researchers should therefore exercise heightened caution and follow all applicable confidentiality and integrity requirements.
In these considerations we construe Artificial Intelligence (AI) broadly and note that this term, coined in 1955, includes a vast array of methods and technologies. AI, in its simplest definition, refers to any technology or machine that can perform complex tasks typically associated with human intelligence. These considerations focus on the responsible use of AI tools in research, not on the development, training, or fine-tuning of AI or machine-learning models.
The resource helps faculty, students, and research staff apply AI transparently and in compliance with university and sponsor requirements and recommendations. Reflection prompts and real-world tips support ethical decision-making, documentation, and data protection. Together, these practices uphold Virginia Tech’s commitment to Ut Prosim, the Principles of Community, and leadership in responsible innovation.
In addition to these considerations, researchers are encouraged to review AI-related position statements, ethical codes, or policy guidance issued by their professional or disciplinary societies. Many organizations, such as the Human Factors and Ergonomics Society (HFES), the Institute of Electrical and Electronics Engineers (IEEE), the American Psychological Association (APA), the American Medical Association (AMA), and the Association for Computing Machinery (ACM), have published discipline-specific standards for responsible AI design, testing, and use. These professional resources complement Virginia Tech’s institutional framework by clarifying expectations and ethical norms within specific research domains. Researchers are also encouraged to pursue training in ethical AI use and data protection as appropriate for their discipline and research activities.
These considerations are not intended to serve as a legal advice or as an exhaustive set of best practices. It should be viewed as a living document. It is not a comprehensive manual on how to conduct research using AI, nor does it provide technical instruction on model development, machine learning methodologies, or other specialized AI practices. Instead, it offers ethical and procedural considerations to support responsible use of AI tools within research workflows. Researchers engaged in AI development should consult discipline-specific best practices, Virginia Tech’s Responsible and Ethical AI Framework, and relevant professional society guidelines for technical or methodological considerations beyond the scope of this document.
Given the rapidly changing landscape of AI, these considerations will be reviewed and updated on a routine basis. Feedback from the research community is encouraged and will be reviewed as part of the ongoing update process. Questions and recommendations should be submitted to the Privacy and Research Data Protection Program in the Office of Research and Innovation (prdp@vt.edu).
Examples of Discipline Specific Guidance
The following examples illustrate how professional organizations are establishing discipline-specific standards and ethical expectations for AI. Researchers should review the guidance relevant to their field to ensure their use of AI aligns with both Virginia Tech policies and their profession’s standards of practice.
Psychology
American Psychological Association (APA)
Ethical Guidance for AI in Health Service Psychology
Medicine
American Medical Association (AMA)
Principles for Augmented Intelligence Development, Deployment, and Use
Communications
International Association of Business Communicators (IABC)
Standards for Ethical Use of AI
Note: This list is illustrative, not exhaustive. Researchers are responsible for identifying and adhering to the AI-related standards and policies established by their own professional or disciplinary organizations.
Suchikova Y, Tsybuliak N, Teixeira da Silva JA, Nazarovets S. GAIDeT (Generative AI Delegation Taxonomy): A taxonomy for humans to delegate tasks to generative artificial intelligence in scientific research and publishing. Account Res. 2025 Aug 8:1-27. doi: 10.1080/08989621.2025.2544331
Responsible and Ethical AI Framework for Virginia Tech (v1.0)
Data Risk Classification Standard. Virginia Tech Division of IT
Using (and Citing) AI: Tips & Tools from Your Librarians
Virginia Tech Policy 13020: Misconduct in Research
Virginia Tech Responsible and Ethical AI Principles
GAIDeT Declaration Generator: An open-source web tool for documenting and disclosing Generative AI use throughout the research lifecycle.
Source: Suchikova, Y., Tsybuliak, N., Teixeira da Silva, J. A., & Nazarovets, S. (2025). GAIDeT (Generative AI Delegation Taxonomy): A taxonomy for humans to delegate tasks to generative artificial intelligence in scientific research and publishing.
Gebru, T., Morgenstern, J., Vecchione, B., Wortman Vaughan, J., Wallach, H., Daumé III, H., & Crawford, K. (2018). Datasheets for Datasets. arXiv. Datasheets for Datasets
The Data Nutrition Project: An open-source initiative offering tools and templates for creating dataset “nutrition labels,” supporting responsible data stewardship, including bias detection, metadata quality, and dataset documentation. Retrieved from https://datanutrition.org on Dec. 17, 2025.