Teaching with Generative AI

Navigating Generative AI: Six Suggestions for Every Instructor 

The following suggestions have been prepared by the Centre for Teaching Support & Innovation based on engagement with U of T instructors and current recommended practice.  As you consider the impacts of generative AI on your teaching, you may wish to respond by: 

1. Clarifying expectations with your students by discussing your expectations and providing guidelines around using generative AI tools in your course. Add clear language to your syllabus and assignments regarding allowable use.
2. Preparing for a conversation with your students about responsible use of generative AI for learning in relation to your course and discipline.
3.  Rethinking both learning outcomes and corresponding assessments with the potential impacts of use by students in mind. Take time for critical consideration of teaching with generative AI.
4. Talking to your TAs about expectations for use of generative AI in relation to their role and to your expectations for appropriate use/non-use by students in the course. Consider sharing TATP’s TA-focused resource on generative AI with your TAs.
5. Familiarizing yourself with tools that align with the University’s privacy and data protections. If leveraging the capability of generative AI, you can use Microsoft Copilot in Protected Mode to protect your data and privacy.
6. Exploring applications of generative AI tools and their outputs to gain a better understanding of their capabilities and limitations. There are a number of workshops and resources available through the Centre for Teaching Support & Innovation.  

For more information:  
https://ai.utoronto.ca/faculty/ 

This content is available in several formats for download for use in a range of instructor support contexts: PDF Format, Word Format, PPT Format.

Introduction to Generative AI

Before deciding whether and how to integrate Generative AI into your course, it is essential to have a solid understanding of its capabilities in supporting learning, as well as the challenges it presents. This section provides a basic overview of generative AI as it relates to teaching and learning.

1. What is Generative AI?

Tools that leverage generative artificial intelligence (GenAI) and large language models (LLMs) to generate new code or text (e.g., Copilot, ChatGPT, Claude, Gemini, etc.) are becoming increasingly available and are likely to have long-term impacts and on what and how we teach. This section is intended to offer some guidance around how to approach the use of such tools in your teaching practice. 

Generative AI refers to artificial intelligence that can generate new content, including text, images, and other media, based on predictive modeling. It uses machine learning algorithms, specifically neural networks, to process and learn from large datasets. Large language models (LLMs) are one class of generative AI, and they have the ability to generate human-like text. 

You can learn more about the University of Toronto’s institutionally-approved generative AI tools, including Microsoft Copilot, by visiting the Generative AI Tools section of this website.  

2. What new opportunities and challenges does generative AI present for classroom instruction & learning? 

Higher education has faced similar disruptions with previous technology innovations, including calculators, Google search, and Wikipedia. While these innovations can be disruptive to our practices of teaching and assessment, incorporating them into our teaching practice is also an opportunity to prepare our learners to live and thrive in a changing world. When intentionally leveraged for classroom instruction, generative AI technologies may also provide new possibilities for enhancing accessibility and engagement for students with varied learning needs. 

When integrating GenAI into your courses, there are several opportunities to foster an inclusive learning environment:

• Encourage AI literacy and future readiness. As generative AI tools develop in capabilities, they will continue to shape what distinctive human skills are prioritized across disciplines and fields of work. By incorporating opportunities within courses to explore, use, and assess generative AI tools, students are better prepared to strategically engage with and critically reflect on these emerging technologies. 
• Encourage metacognition, creativity, and critical thinking. By designing carefully constructed assessments and forms of active engagement, students have the opportunity to explore different viewpoints, self-reflect, and engage in analysis and knowledge synthesis.
• Address barriers to equity and accessibility. Generative AI tools can be leveraged to provide multiple options for motivating and engaging learners, for representing information, and for inviting learners to express and communicate. This can create learning experiences that are personalized to students’ diverse needs and abilities. 

When integrating GenAI into your courses, there are several considerations to ensure equitable and responsible use, including:

• Availability. While many generative AI tools are currently freely available, their availability could change at any time. For any third-party software that is not approved by the University or your Division, there are several considerations related to privacy, security and student intellectual property that should be considered before asking your students to use non-approved generative AI tools. 
• Accuracy and bias. Text created by generative AI technology may be biased and may not be correct. 
• Academic integrity. The University is discouraging the use of tools that claim to be able to detect AI generated text.  See Generative Artificial Intelligence in the Classroom: FAQ’s for more information. 
• Privacy and security. A version of Microsoft Copilot is currently available to the public (and U of T students), however the public version does not have full privacy and data protections in place. U of T has access to the enterprise edition of Microsoft Copilot, which conforms to the University’s privacy and data protections, unlike the public version. Note that other publicly available generative AI chatbots like ChatGPT may not offer such privacy and data protections. For details on institutionally-approved tools, please visit the Generative AI Tools section of this website.  
• Copyright and intellectual property. It is important to be mindful of what content is entered into generative AI platforms that do not have institutional data protections in place. Never input confidential information or intellectual property for which you do not have the rights or permissions. All content entered may become part of the tool’s dataset and may inadvertently resurface in response to other prompts. See the U of T Library’s Generative AI tools and Copyright Considerations for more information.  

Before the Course Starts

This section offers considerations on how to intentionally begin course planning with generative AI in mind. You may want to revise course-level learning outcomes and assessments, as well as develop a communication plan with students and teaching assistants (TAs).

1. Develop or revise your learning outcomes

In light of how students could use generative AI, you may want to revise the learning outcomes from previous iterations of the course. Learning outcomes are statements that describe the knowledge or skills students should acquire by the end of a particular class, course, or program. Rather than be unchanging, learning outcomes may shift to address the larger context in your discipline: the requirements of follow-up courses, potential student career paths post-graduation, and emergent digital literacy skills needed for future readiness, where AI will be increasingly used as a partner and collaborator. 

It is good practice to specify learning outcomes that are meaningful to students’ post-educational goals and overall skill development. This may help to stimulate student interest and maintain motivation across the course. To guide your decision-making process on whether and how you will engage with generative AI, you may reflect on one or more of the following questions: 

• What distinctive human skills do I want my students to develop, and how may I articulate them in the form of a learning outcome? 
• Can students’ use of generative AI tools align with my course learning outcomes and teaching philosophy and if so, how?
• How may I leverage generative AI tools to facilitate deeper thinking?
• What digital literacy skills do I want my students to develop?

Modifying learning outcomes to foreground distinctive human skills 

While learning outcomes vary across courses, there are some cross-disciplinary cognitive skills that you may consider relevant to your students’ development. According to Bloom’s Revised Taxonomy of Active Verbs, an educational framework for categorizing learning objectives, there are six levels of skill and complexity that instructors may measure in relation to student learning. “Bloom’s Taxonomy Revisited” (see Figure 1), created at Oregan State University, offers a framework that reconsiders how to define each of the six levels of learning in your disciplinary context, given the capabilities of AI tools. For instance, in the “analysis” level, students critically examine and break information into parts by identifying motives, causes, and relationships. Generative AI, however, is currently proficient in many analysis-related tasks, including comparing and contrasting and inferring themes. Given the analytic capabilities of generative AI, you may want to update how you frame learning outcomes at this level, so that you are pointing to distinctive human skills. 

To verify whether your course-level learning outcomes are measuring distinctive human skills, you may wish to reflect on one or more of the following questions:   

• Does the learning outcome encourage students to interpret and relate to authentic problems, decisions, and choices?  
• Does the learning outcome encourage students to engage higher-order thinking skills (critical analysis, synthesis, evaluation), which generative AI cannot fully engage in?  
• Does the learning outcome encourage the development of robust conceptual knowledge, i.e., the “why” behind the “what”? 

Figure 1: Bloom’s Taxonomy Revisited, Oregon State University

During the First Week

After developing your course-level learning outcomes, assessment structure, syllabus, and communication plan, you are ready to begin preparing for your first class.  In this first week, it can be especially useful to ensure that students are aware of course policies and expectations regarding generative AI.  

1. Communicate with your students about your expectations for generative AI use in the course 

The first day is an important opportunity to model how you hope and expect that classes will proceed throughout the course. Building a sense of community through active participation around course policies will help set a tone that supports responsible use of generative AI. 

Following Universal Design for Learning (UDL), you may consider providing options for recruiting attention and engagement, thereby optimizing what is relevant, valuable, and meaningful for each learner. In consideration of this, you may want to explicitly communicate with your students about generative AI expectations, drawing on one or more of the following strategies:  

• Create a community agreement. Ask students to collaboratively reach consensus about generative AI in course activities and assessments. See the CTSI resource for more suggestions on building these community agreements. 
• Explain what your policy is and why. Rather than reiterate the language of the policy statement on your syllabus, you may wish to elaborate on what reasons you chose this policy. How does that policy encourage students to effectively reach the learning outcomes of the course? 
• Facilitate debates. Arrange a classroom debate about generative AI as it relates to your course topics and/or themes.  
• Create space for discussion and reflection around generative AI policies and expectations. Prior to explicitly discussing the policies, you may wish to create space for a broader discussion on learning and generative AI. Active learning activities like low-stakes writing, reflective writing, think-pair-share, and jigsaws can be effective ways of generating and recording ideas.  

2. Demonstrate how to responsibly use generative AI tools 

If you are choosing to encourage or allow students to use generative AI, you may wish to begin the course with a demonstration of how to use relevant institutionally approved tools. While the interactive, chat functions of generative AI can be engaging for students, making the most of these tools can require time and patience for both instructors and students. In line with Universal Design for Learning (UDL), students may benefit from there being options for how they may engage in information processing and visualization.

Given that the use of generative AI tools may be relatively new for some, and that all learners have diverse abilities in summarizing and categorizing information, you may wish to consider one or more of the following: 

• Offer step-by-step demonstration of prompt writing. Rather than only offer a general introduction to generative AI, you may wish to model how students could responsibly use relevant tools for upcoming assessments in the course. You may want to spend focused time on showing what makes an effective prompt. A prompt is natural language text describing the task that an AI agent or chatbot should perform. Prompt writing (sometimes known as prompt engineering) is the process of structuring an effective prompt that can be interpreted and understood by the AI system. To learn more on prompt writing, see our Tool Guide under “How can I prompt with Copilot?.” 
• Create collaborative space for experimentation and feedback. Students have varying levels of experience with generative AI tools, and the first class can be a great space to gauge their level of familiarity. In addition to providing a demonstration, you may wish to encourage your students to share helpful tips and reflections as they independently experiment with the tools.  
• Draw attention to learning resources that support the responsible use of generative AI. When students know what resources are available to them, they will be more likely to find ways to overcome academic challenges. Even if it is mentioned on the syllabus, you may wish to model to students how they may connect with relevant academic supports across the University of Toronto – including writing centres and learning strategists. By doing this, students may be more likely to responsibly use any permitted generative AI tools.  

3. Communicate with teaching assistants about your expectations regarding their use of Generative AI  

When connecting with your course teaching assistants (TAs) during the initial course meeting, it is good practice to communicate whether and how your course will engage or limit the use of generative AI. To clarify TAs’ roles and responsibilities, as well as your expectations, you may wish to consider discussing one or more of the following topics: 

• Grading and rubrics. Communicate with teaching assistants whether and how grading rubrics will be adjusted to account for generative AI capacities, so that human skills are being prioritized for evaluation. Hear from TAs about their prior experiences in grading assessments since generative AI tools have become more available; their insights may be a useful resource as you consider the grading protocol of your course. 
• Student communication plan. Provide suggestions to teaching assistants on how they should communicate with students about course generative AI policies and recommended practices. 
• Tools training. If generative AI tools are part of the course, provide training to teaching assistants about how to use the tools. Consider showing them how they may model the responsive use of generative AI tools, if their contract involves student interaction in tutorials, labs, or office hours.  
• Check-in plan. Discuss a plan for course check-ins, so that there is an open line of communication. By doing this, teaching assistants will have a clear idea of how they may raise any questions or concerns that come up regarding generative AI expectations, protocol, and policies. 

Week to Week

As you move through the weeks of the course, it is essential to maintain open communication with students about generative AI. This section will outline how to introduce assessments and integrate discussions about AI on a weekly basis, helping students understand the purpose of assessments and whether and how they are permitted to use AI tools. By checking in and communicating regularly with students, they will be less likely to seek out generative AI for (mis)information.   

1. As assessments are introduced, outline their rationale and procedures 

Since generative AI is new technology and its allowed uses will vary across courses, students will require clear guidelines, reiterated throughout the course.  The following strategies will ensure that your communication about assessments is accessible to all learners, aligning with a Universal Design for Learning (UDL) approach. Consider doing one or more of the following: 

• Communicate the value of the assessments and what students can gain from completing them in relation to course-level learning outcomes. If generative AI tools play a role in completing the assessments, you may show students again how to engage with them and why. You may alternatively encourage your teaching assistants to take on that role, if they are running tutorials. 
• Review assignment instructions in class and provide an opportunity for questions. Clearly explain the extent of allowed AI use for the assignment, and engage students in a conversation about why you chose to encourage or discourage the use of generative AI, and how generative AI may or may not help them with the assignment. 
• Include an integrity statement for students to complete when submitting their assignments, affirming their adherence to the course’s Generative AI policy.
• Share a “ready to submit” summary assignment sheet or checklist to guide and motivate students through the learning process, so that they know what to submit and when they are done (Bowen and Watson, 2024). Given the choice between harder and easier work, students will be more likely to use generative AI responsibly if they understand the value of the added discomfort. Consider addressing the following questions on the assignment sheet or checklist: 
  • Purpose: What skills or knowledge will I gain? How will I be able to use this? 
  • Task: What needs to be submitted? Is there a recommended process? When is this due? Where can I do this work and with whom? 
  • Criteria: What are the parts? How will I know what’s expected? 
  • Process: Most assignments require processes that are more obvious to faculty than to students; specify when and which AI might be a useful tool and how it may enhance learning.

2. Use generative AI to support student engagement  

With intentional planning on the part of the instructor, generative AI may offer novel opportunities for personalized practice, tailored to students’ needs. 

Following Universal Design for Learning (UDL), you may wish to offer multiple means of engagement; by varying forms of involvement and interaction, students may more likely be motivated to apply their knowledge. To extend or transform in-class engagement, you may consider using generative AI for one or more of the following:  

• Learn through tutoring. You may use generative AI tools to engage students in metacognitive reflection, whereby they identify gaps in their knowledge, consider alternative perspectives, and establish connections within complex bodies of information. This may encourage students to self-regulate, sustain effort, be goal-directed, and monitor their progress in learning. 
• Learn through simulations. You may wish to create AI-based scenarios to serve as controlled spaces for applying knowledge in a low-stakes context. In role playing, the student may assume the identity of someone else; in goal playing, the student maintains their identity while applying their knowledge and skills. In these spaces, the AI may play the role of mentor while also creating the narrative set-up. 
• Learn through critique. AI can provide students with multiple “peers”, prompting the student to help the “AI student” understand class material. For instance, students can critique whether an AI-generated scenario applied a course concept correctly, thereby giving space to demonstrate their knowledge.  
• Provide multiple examples and explanations. Generative AI I tools can be used to generate a wide variety of examples related to a given topic, to model and problematize a thought process, and to offer alternative explanations. In all these cases, you may consider creating discussion space to critique the AI-generated output, thereby supporting students’ analytic skill development. 
• Gather formative feedback on generative AI use in the class. It can be useful to collect mid-term feedback on the course, to gauge and respond to student experiences with generative AI. These evaluations may be used to make adjustments to the course that will affect the rest of the semester. For instance, you may ask students to submit exit-ticket responses at the end of a class, or one-minute papers, where students provide responses to class activities or assignments (see Angelo & Cross, 1993). 

Adapted from: Instructors as Innovators: A future-focused approach to new AI learning opportunities, with prompts,
Mollick and Mollick, 2024.

At the End of the Term

The end of the term is a valuable time to learn from students’ experiences, using those insights to guide how you will move forward with future iterations of the course. You may consider connecting with your students and TAs around how the course engagement with generative AI. 

1. Collect student reflections and feedback 

In addition to collecting mid-term course feedback, you may also consider creating space for informal feedback and discussion at the end of the term. By doing this, you can directly learn from students and teaching assistants about how you may adjust the course for future teaching. 

To collect feedback and encourage reflection on generative AI’s application to teaching, you may consider one or more of the following: 

• Reflective writing activity about generative AI. Incorporate an assignment or optional end-of-term activity, where students share their experiences and challenges using generative AI to support their learning in the course. To guide students’ reflection, consider offering prompts, such as: 
  • How did you use generative AI tools in the course? What were the benefits and challenges? 
  • What strategies did you employ to effectively use the output of these tools? 
  • What suggestions would you give to a student taking the same course next semester, so that they get the most of using these tools? 
• Reflective activity using generative AI. Moving a step further than a conventional reflective writing activity, consider inviting students to use generative AI to dialogue with their thoughts. For instance, you may provide prompts to students, so that they have a “conversation” with Copilot, where it asks progressive questions and feedback. From there, you may invite students to critique and expand on the summaries from Copilot, strengthening their AI literacy. 
• Gather feedback from teaching assistants. Since teaching assistants often work directly with students, it will be valuable to gather their feedback from the semester on how course activities and assessments—including those that engaged generative AI—impacted student learning. Consider using anonymous feedback methods, so that TAs may comfortably share their concerns. If having a conversation, consider preparing the meeting with guiding questions about their experiences, strategies, and suggestions for improvement. 

2. Reflect and plan your next steps in teaching  

Student evaluations and informal feedback can serve as very useful resources for your long-term development as an instructor. By drawing on these responses, as well as you own observations, you can develop a plan for your next steps. 

There are many ways that you can structure your plan for refining your teaching with generative AI for future courses. As you organize informal feedback, formal evaluations, and your own observations, you may consider following Universal Design for Learning (UDL) Plus-One Approach: What moments in the course were “pinch points,” where your incorporation of generative AI didn’t go the way you anticipated? By starting with addressing these moments, you will move towards reducing the most significant barriers and finding the most important areas of your course where you can address learning variability. You may consider reflecting on one or more of the following topics:  

• Assessing student learning. Has there been any notable changes in student engagement or learning since incorporating generative AI into my teaching? 
• Refining assessments. Which assessments worked well, and which should I focus on redesigning, so that students’ skill-building is prioritized? 
• Evaluating teaching strategies. Which strategies have been most helpful in helping students to critically and responsibly engage with course material and the use of generative AI in your discipline? 
• Policies and guidelines. What modifications can I make, to further address ethical and academic integrity considerations? 
• Share Ideas. How may I engage with colleagues in my discipline and across U of T? How may I share approaches that integrate best practices in assignment and assessment design in the context of using or avoiding the use of generative AI? 
• Professional development. By leveraging generative AI, what new skills have I acquired, and what new areas I have identified?