Using Generative AI in Research: Integrity & Citation Guide

The rapid adoption of generative AI tools necessitates a rigorous approach to academic integrity. As these models become integrated into scholarly workflows, researchers and students must adhere to standardized protocols to ensure transparency, accountability, and the ethical use of technology.

Defining Generative AI within Academic Frameworks

In scholarly environments, generative AI tools must be viewed as assistive resources rather than autonomous authors. The responsibility for the accuracy, logic, and integrity of any submission remains solely with the human author.

• Resources vs. Authors: AI models function as sophisticated drafting or analytical tools. They cannot assume legal or ethical accountability for the content they generate.
• Human Accountability: Researchers are fully responsible for verifying all AI-generated assertions, including citations and data analysis.
• Policy Variations: Institutional permissions vary significantly. Before utilizing AI in any capacity, researchers must consult their specific department or institutional handbook to confirm whether usage is permitted for specific tasks.

APA 7th Edition: Citation Protocols for LLMs

The American Psychological Association (APA) provides specific guidance for acknowledging AI interactions. Proper citation ensures that the source of information is transparent and verifiable.

Core Citation Requirements

When referencing an AI tool, you must acknowledge the model version and the developer. The reference list entry follows this format:
Developer. (Year). Name of model (Version) [Large language model]. URL

In-Text Citations

In-text citations should accompany any information derived from the AI. For example:
(OpenAI, 2024).

Documentation of Prompts

To maintain scholarly rigor, researchers should append transcripts of their interactions with the AI. These transcripts—including specific prompts used to generate the output—serve as essential supporting documentation for the methodology section or as an appendix.

Attribution Guidelines for AI-Generated Visual Media

Visual content generated by AI, such as images, diagrams, or video, requires specific attribution to differentiate between human-created and AI-assisted elements.

• Transparency: When using AI-generated visual media, clearly state the tool name, version, and the date of creation.
• Metadata Inclusion: Maintain a record of the original prompt and any subsequent refinements. This metadata should be included in the figure caption or within the descriptive text of the submission.
• Classification: Always distinguish between figures created entirely by AI and those where AI was used only for editing or enhancement of human-produced work.

Copyright, Attribution, and Intellectual Property Compliance

The legal landscape surrounding AI-generated content remains in flux. In academic settings, the primary concern is the prevention of plagiarism and the upholding of academic honesty standards.

• Labeling Requirements: All AI-generated content must be explicitly labeled. Failing to disclose AI usage can be classified as academic misconduct or plagiarism.
• Methodology Disclosure: If AI tools were utilized to synthesize data, perform coding tasks, or assist in structural drafting, this must be declared in the methodology or acknowledgments section of the document.
• Copyright Status: Be aware that current legal frameworks often deny copyright protection to content generated without significant human creative input. Authors should not claim original intellectual property over raw AI output.

Protocol for Institutional Compliance and Student Accountability

To mitigate the risk of accidental misconduct, follow a structured process for ethical AI deployment.

1. Verification: Always confirm that the tool you intend to use complies with the specific requirements set by your instructor or institution.
2. Integrity Checklist: Before final submission, cross-reference all AI-assisted claims against primary scholarly literature to ensure factual accuracy.
3. Ethical Documentation: Maintain a logbook of all AI tools used during the research project. This log should include the purpose, the inputs (prompts), and the outputs received, ensuring total transparency throughout the writing process.