Generative AI in the class

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A Generative AI Task Force at the University of Virginia wrote that generative AI “may shape what students learn, how they learn, and how their learning is assessed.”  This means that students will need to develop AI-specific literacies and skills within and outside of their educational contexts, that AI may play several roles alongside the instructor in the classroom, and that our traditional approaches to assessment will need to be revamped. What roles can generative AI productively play in the classroom? And what does incorporating generative AI look like? There are no definitive answers to these questions, but through exploration and experimentation, instructors may be able to develop goals and plans for generative AI in their courses.

Generative AI roles in learning

As you consider how generative AI fits into your classes, consider the role(s) that those tools could play in supporting student learning. Mollick and Mollick (2023) shared a framework for generative AI in learning that includes suggestions for using it as:

  • A tutor. For example, students identify concepts or problems they are struggling with in class and prompt an AI tool to provide feedback and guidance to them as they try to understand or solve those issues. Sample prompt
  • A simulator. For example, the AI tool creates scenarios that require students to practice class skills/knowledge, such as a a simulation of a conversation in another language, or a simulation of a problem in a chemistry lab.
  • A teammate. For example, working in teams, students might ask a generative AI tool to identify resources or strategies to move their group work forward.

Some of these approaches may work for your class, some may not. As you think about what might work in your class, consider bottlenecks students encounter or areas where students need lots of practice and feedback. As always, you can consult with a DLINQ staff member to explore ideas for supporting student learning with generative AI in your class.


When working with generative AI tools, prompting matters. You and your students will need to develop skills in prompting to elicit usable outputs from generative AI tools. A common prompting approach is known as RTRI, which stands for:

  • Role – tell the tool what or who to act as
  • Task – give a summary or description of what the AI needs to do
  • Requirements – add requirements that specify what the output should include, contain, be, etc.
  • Instructions – include what the AI should do to act on or complete the prompt

Experts also suggest chain-of-thought prompting as an effective methodology for prompting.

Good prompting isn’t just about the first prompt. Most interactions with generative AI tools require several interactions to improve and refine results. Encourage your students to keep interacting with generative AI tool past the first prompt and to reflect on what approaches to prompting led to the best outputs. The ability to prompt AI and to improve their prompting will serve students well in their education and beyond.