Using Generative AI to Support Instructional Efficiency

Many instructors have used generative AI to produce instructional materials including varied examples, multiple explanations, low stakes testing content, summarize key themes in a course, rubric ideas, and more. Ethan Mollick and Lilach Mollick provide example generative AI prompts for the material types noted above and other strategies in their article, Using AI to Implement Effective Teaching Strategies in Classrooms: Five Strategies, Including Prompts.

However, we encourage you to take caution with pasting student work or copyrighted materials into generative AI tools without the author's express consent. Because many of the generative AI tools train on data inputs, the author's intellectual property may become part of the AI model without the author's consent. For details on United States copyright laws for instructors and students, see Fair Use and Copyright.

Also, it is important to scrutinize the output of the generative AI tool for accuracy prior to using the output. The tools may provide false information. Just like with student use, instructors should provide citations to other credible resources as necessary to support the information generated by the tool.

Instructors should also demonstrate responsible use of generative AI to their students by disclosing and citing their own use of generative AI. The RIT Libraries provides an InfoGuide on How to Cite Generative AI Tools in MLA, APA, and Chicago Style.

For other considerations while using generative AI, review the page on generative AI from the RIT Information Security Office.

Contact the Center for Teaching and Learning to discuss using generative AI to produce instructional materials.

Below are example use cases shared by RIT Faculty. Many examples could be adapted for any domain. Expand each area to view details.

Last Updated: 12/24/2024