Research Assistance

Use AI to clarify concepts, generate new ideas, narrow or expand a topic, suggest outlines that you can then edit and expand upon. Ask questions for clarification and greater understanding. Explore diverse perspectives. Get feedback on ideas.

Academic Honesty and Plagiarism

The use of AI in academic research requires honesty and accountability. Most writing and citation styles ( e.g., APA, MLA, Turabian) require disclosure of AI assistance for transparency and to avoid plagiarism. For style recommendations and guidelines see the following

APA Style - opens new windowHow to cite ChatGPT
MLA Style - opens new windowHow do I cite generative AI in MLA style?
CSE Style - The Council of Science Editors recommends treating AI-generated content as personal communication. See the Citations page for citation details.

Always follow your professor's instructions on the scope of using AI in your course.

Within academia, using artificial intelligence (AI) and AI-generated results creates challenges, including bias, accountability, integrity, and  transparency in AI. Students must critically evaluate AI information to ensure the validity, reliability, and trustworthiness of their findings.

The AI challenges listed below align with the Association of College & Research Libraries' (ACRL) Framework for Information Literacy for Higher Education and address aspects of information literacy, including responsible and effective use of AI in academic research.

Bias Awareness

• Recognize and address biases in researchers, articles, selection, and data analysis.
• Be aware that AI technology may inadvertently have biases in data collection, interpretation of questions, algorithm design that favors certain groups, model design or human-decision making that may not ensure decision-making fairness, transparency and equitably. AI systems may have biases.
• Students should learn how to recognize biases and understand the impact on fairness and accuracy of AI results. They should critically evaluate information sources and recognize that biases can influence AI-generated results.

Authority is Constructed and Contextual (Framework for Information Literacy for Higher Education) - Information resources reflect their creators' expertise and credibility, and authority is a type of influence recognized in various forms and contexts. Information possesses several dimensions of value, including as a commodity, as a means of education, as a means to influence, and as a means of negotiating and understanding the world.

Accountability

• Student research involving AI, accountability includes transparent reporting of methods and findings to ensure the integrity and ethical standards of scholarly inquiry.
• Students must recognize that information has value and must be handled responsibly to ensure transparency, honesty, and integrity in scholarly inquiry and reporting methods and findings.
• Using something written by a language model that you had not thought of, generally raises questions about authorship, originality, and intellectual property, though the "model" (e.g., GPT) is not considered an "author."
• Accountability prevents unethical use and creates and fosters responsible knowledge creation and dissemination.

Information Has Value (Framework for Information Literacy for Higher Education) - Information possesses several dimensions of value, including as a commodity, as a means of education, as a means to influence, and as a means of negotiating and understanding the world.

Data Integrity

• Also know as hallucinating, data and content integrity is crucial in student academic research with AI in order to ensure that information used remains accurate and reliable.
• Students must ensure that scholarly research maintains transparency, credibility, and ethical standards.
• Critical evaluation of information sources is important and crucial when using AI tools.
• Recognizing the value of accurate and reliable information ensures that students maintain high ethical standards in their research.

Information Creation as a Process (ACRL Framework for Information Literacy for Higher Education) - Information in any format is produced to convey a message and is shared via a selected delivery method. The iterative nature of researching means recognizing this process and the various ways information is created and disseminated.

Transparency and Openness

• Understanding the limitations and potential biases of AI generated information can help avoid negative impacts on learning and research outcomes.
• Understanding AI limitations and potential biases of AI systems directly ties into recognizing that authority of AI generated information is influenced by the input data and system algorithms.
• Understanding AI-generated sources and context helps students evaluate the authority and reliability.

Authority Is Constructed and Contextual (Framework for Information Literacy for Higher Education) - Information resources reflect their creators' expertise and credibility, and authority is a type of influence recognized in various forms and contexts.

Information Creation as a Process (Framework for Information Literacy for Higher Education) - Knowing how AI systems produce information promotes helps student awareness of limitations and potential biases.

Publisher submission policies and academic publishing standards vary on whether or not to allow AI-generated images, videos, and/or text. As AI tools and use evolve, policies and standards are likely to be revised.

Some publishers strictly prohibit AI-generated text and images that are not pre-approved by the editor, while other publishers may allow AI-generated text, but not AI-generated images or videos. Or, publishers that allow AI-generated text may require disclosure of the AI text and models used.