Student Perceptions of Generative AI in Teaching and Learning

As McMaster has taken the approach of having each instructor decide whether and how to incorporate generative AI into a course or assignment, you may be wondering why you might want to do so. What benefits, if any, does generative AI pose for student learning? What learning outcomes could its use support or enhance? This chapter assumes your familiarity with the risks and challenges of generative AI for post-secondary (e.g. academic integrity, assessment design, hallucinations) and imagines what benefits their might be and what opportunities for preparing students for a generative AI supported learning experience.

You can think of the possibilities in two domains:

  1. supporting personalized learning and
  2. generating academic content.

Generative AI has many capabilities in supporting personalized learning, some of which we detail below. Chief among them is providing actionable, timely and relevant feedback on drafted student content. This feedback might be focused on the grammar or style of the draft, or on the logic of the argument, organization of the piece, or further examples to consider.

With respect to generating academic content or performing academic skills, you want to think carefully about what the core learning outcomes are for the course, and whether and how students can demonstrate these outcomes. Those skills or knowledge that are not essential to the core learning outcomes might be appropriate for ‘cognitive offloading’ to a generative AI tool. Cognitive offloading refers to the use of external resources or tools to change the information processing requirements of a task so as to reduce cognitive demand.[1]

For instance, if your course learning outcomes require students to demonstrate abilities to generate multiple hypotheses to explain a phenomenon, using generative AI to generates these hypotheses would be inappropriate. However, if your course learning outcomes were focused on having students test a hypothesis it in a laboratory setting, having a generative AI tool generate the hypothesis which the student would then test would be an example of appropriate cognitive offloading.

In what follows we offer some concrete examples of how generative AI can be used to support personalized learning or generate academic content. For each example we remind you of the importance of first deciding whether this is a task the student needs to complete themselves to fulfill the course learning outcomes, or whether this is a task that would benefit from cognitive offloading. If you have questions or want to discuss, please reach out to an educational developer at the MacPherson Institute at

Supporting Personalized Learning

Invite students to use a generative AI tool to:

Generating Academic Content

Invite students to use a generative AI tool to:

Expand or condense text

Brainstorm / Generate ideas

Find sources or references

Identify and analyze data

Interact with spreadsheets

Code with natural language prompts

With all of these uses it’s important to remind students that what the generative AI tool generates may have hallucinations or biases. Students should be reminded to review and evaluate the output from the generative AI tool to ensure its accuracy and evaluate its effectiveness.

You may be wondering – or your students may wonder – what generative AI tool to use for these tasks. This review essay by Ethan Mollick summarizes the capabilities of the major generative AI tools and makes suggestions on the best tool to use for a specific task. You can also visit “There’s an AI for That” to find new generative AI tools for specific educational tasks.


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Generative Artificial Intelligence in Teaching and Learning at McMaster University Copyright © 2023 by Paul R MacPherson Institute for Leadership, Innovation and Excellence in Teaching is licensed under a Creative Commons Attribution 4.0 International License, except where otherwise noted.

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