Teaching with Generative AI
This resource provides guides, explanations, and ideas to help you plan for and/or integrate generative AI into your courses. Visit U of T Teaching Examples to see how instructors have integrated AI use into course design across disciplines.
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:
- 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.
- Preparing for a conversation with your students about responsible use of generative AI for learning in relation to your course and discipline.
- 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.
- 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.
- 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.
- 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/
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For more downloadable GenAI resources: CTSI Resources
Share with Your Students: A Companion Resource
Using AI Tools for Learning at U of T is a student guide from the Centre for Learning Strategy Support (CLSS), offering six key tips for responsible, effective, and ethical GenAI use.
Proactive Strategies to Address Unauthorized GenAI Use
Instructors can promote academic integrity through the ethical and effective design, delivery, and assessment strategies (Eaton, 2024). In the context of generative AI (GenAI), consider:
Fostering Honesty via Course Design
- Clear syllabus statements: Explicitly define authorized/unauthorized GenAI use for your course. If you need language about GenAI for your syllabi, U of T has examples available.
- Purposeful assignment design: Create meaningful assessments that encourage students to focus on the process of learning, prioritizing critical thinking, reflection, and classroom-specific content; see CTSI’s Teaching with Generative AI.
- Scaffolded assignments: Break projects into smaller components with checkpoints to identify and assist struggling students early. This encourages students to focus on the learning process, rather than the final outcome; see CTSI’s Teaching Resources.
- Reflections on AI use: Ask students to explain if and how they integrated AI tools into their workflow. If none were used, have students describe their research, analysis, and creative process. This metacognitive practice can build intrinsic motivation; see CTSI’s Assessment Reflection Template.
- For practical examples of assessment designs from U of T Instructors that use scaffolding, reflection, and course-specific application to emphasize and evaluate students’ original thinking, see U of T Teaching Examples.
Educating Students on Best Practices
- Clarify expectations: Collaborate with your TAs to provide ongoing discussions of appropriate vs. inappropriate uses of AI tools; see TATP’s Teaching with GenAI.
- Model proper citation: Demonstrate how to properly cite AI when permitted on an assessment; see UTL resources on citing AI, image research, and copyright considerations.
- Emphasize the value of original thought: Encourage students to recognize that their unique voice, creativity, and critical thinking are invaluable and irreplaceable when completing assessments, whether they be writing, coding, or multimedia projects.
- Discuss AI limitations and risks: Explain the limitations of GenAI (hallucinations, fabricated references), emphasizing the importance of fact-checking and digital literacy.
Identifying Possible Misconduct Cases
- Before issues arise, familiarize yourself with traditional detection methods (e.g. the student cannot explain their work) and the standard academic misconduct process.
- AI-detection software programs are unreliable and biased against non-native English writers (Elkhatat et al., 2023; Liang et al., 2023; Saha and Feizi, 2025). U of T does not support the use of AI-detection tools; see the Office of the Vice-Provost, Teaching & Learning (OVPTL)’s FAQ on Generative AI.
- Personal intuition that a text is AI-generated has been shown to be inconsistent, even when evaluators are experienced with GenAI and confident in their abilities (Waltzer et al., 2024).
- For further guidance, contact the head of your academic unit.
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For more downloadable GenAI resources: CTSI Resources
Assessment Planning
This section offers considerations on how to intentionally begin assessment planning with generative AI in mind. You may want to revise course-level learning outcomes and restructure assessments, as well as develop a communication plan with students and teaching assistants (TAs).
1. Developing or Revising Learning Outcomes
Learning outcomes describe the knowledge or skills students should acquire by the end of a class, course, or program. They are not fixed: they may shift to reflect your discipline, the requirements of follow-up courses, students’ career paths, and the digital literacy skills students will need in a future where AI is increasingly used as a partner and collaborator. Outcomes that connect to students’ goals and skill development can also help sustain interest and motivation across a course.
As you decide whether and how to engage with generative AI, you might consider: which human-centred skills you want students to develop and how to express them as outcomes; whether AI use can align with your outcomes and teaching philosophy; how AI might be used to deepen thinking; and which digital literacy skills matter for your students.
1.1 Modifying Learning Outcomes to Foreground Human-Centred Skills
Some cross-disciplinary cognitive skills are worth foregrounding as AI tools become more capable. Bloom’s Revised Taxonomy categorizes learning objectives across six levels of complexity. Oregon State University’s “Bloom’s Taxonomy Revisited” (Figure 1) reconsiders what meaningful learning looks like given AI’s capabilities. At the “analysis” level, for instance, generative AI is already proficient at tasks such as comparing, contrasting, and inferring themes—so you may want to reframe outcomes at this level toward human-centred skills that do not invite overreliance on AI.
To check whether an outcome targets human-centred skills, consider whether it asks students to interpret and relate to authentic problems and choices; to engage higher-order thinking (critical analysis, synthesis, evaluation); and to develop conceptual knowledge—the “why” behind the “what.”
Bloom’s Taxonomy Revisited: A framework for aligning course activities and assessments with higher-order thinking skills, updated to reflect the evolving role of generative AI in education. Oregon State University Ecampus, CC BY-NC 4.0
Example: Modifying Learning Outcomes to Emphasize Human-Centred Skills in a GenAI Context
The following examples show how outcomes can be revised to emphasize human-centred skills, whether or not generative AI is integrated into the assessment.
Philosophy
- Pre-modified: Students will analyze the similarities and differences between various types of knowledge (empirical, rational, testimony, revelation).
- Modified: Students will critically evaluate and compare types of knowledge (empirical, rational, testimony, revelation) within ethical contexts.
Medicine
- Pre-modified: Students will compare and contrast how promotional health information and resources effectively and accurately present information to patient care.
- Modified: Students will assess the accuracy, reliability, and relevance of promotional health information and resources, identifying biases and areas for improvement in patient care.
Statistics
- Pre-modified: Students will analyze data using exploratory data analysis techniques and statistical modeling methods.
- Modified: Students will apply exploratory data analysis techniques and statistical modeling methods to real-world datasets in statistical computing environments, drawing meaningful conclusions.
1.2 Including Learning Outcomes That Encourage AI literacy
AI literacy does not necessarily require students to use or prompt AI tools; it focuses on being a critical, responsible user of AI-generated content. Building it into your course helps students evaluate and reflect on AI technologies even if they never build AI models themselves. Connecting these outcomes to Universal Design for Learning (UDL) principles can help students see why AI literacy is relevant to their learning.
The University of Toronto Libraries’ Framework for AI Literacy offers a useful, discipline-adaptable structure built around three frames that learners move through fluidly rather than in fixed order:
- Understand: Build foundational knowledge of how AI tools work, how they are trained, and the ethical, legal, and social implications of their use—including privacy, bias, labour, and copyright.
- Use: Engage with AI tools responsibly and effectively, in line with relevant policies and an awareness of data and privacy considerations.
- Evaluate: Critically engage with AI tools to judge their credibility and accuracy, and the broader impacts these tools have on learning and on the world.
You can link course outcomes to whichever frame fits your goals, and your liaison librarian can help tailor AI literacy instruction to your discipline. To bring AI literacy directly into your course, you can also draw on U of T’s GenAI Literacy Course Modules: customizable, ready-to-use content for building students’ AI literacy across disciplines.
Example: AI Literacy Learning Outcomes
Philosophy:
- Students will critically analyze and evaluate the ethical implications raised by generative AI technologies. (Understand; Evaluate)
Medicine
- Students will assess the potential benefits and limitations of using generative AI systems for medical diagnosis and treatment planning. (Evaluate)
Statistics
Students will interpret AI model outputs and performance metrics in real-world applications. (Use; Evaluate)
Once you have clarified which human-centred skills and AI literacy competencies your outcomes foreground, you can carry them directly into assessment design. The skills you decide students must demonstrate independently are the ones your assessments will need to keep visible—and that decision is what guides whether you design generative AI in or out of a given task, as discussed in the next section.
2. Adapting Assessments to Make Learning Visible
Once you have revisited your course-level learning outcomes, the next step is to design or revise your assessments so that they align with those outcomes. The human-centred skills you foregrounded are the ones your assessments now need to keep visible—and deciding which students must demonstrate independently is what guides whether you design generative AI in or out of a given task.
A useful shift can be to move from “Can students use generative AI?” to “What learning needs to remain visible?” When the final product is the only thing assessed, it can be harder to tell how much of the thinking was the student’s own. Process-oriented assessment instead captures how students plan, reason, and revise. The Digital Education Council frames this durability as AI-resilience: rather than relying on a “No AI” rule and student compliance, instructors might structurally design tasks so the skills at the core of their outcomes cannot easily be outsourced—which takes redesign, not just rules or detection tools (Corbin et al., 2025).
To make student learning visible, consider the following questions:
- How can you shift the focus from output to process, so that what you assess is how students think and adapt, not only what they hand in?
- How can you scaffold assessments with checkpoints to encourage critical engagement and independent thinking, whether or not generative AI is permitted?
- How can you design real-world tasks that require students to apply their own knowledge and critical thinking?
- Is developing AI literacy a goal for your course—and if so, how might you create opportunities for students to build those skills?
The two approaches below show how this plays out when generative AI is designed out of a task (to prevent unauthorized use) and designed in (as a supplement to learning). For practical examples of assessment designs from U of T instructors that make student thinking visible, see U of T Teaching Examples.
Approach 1: Designing Generative AI Out to Keep Specific Learning Visible
For assessments designed to develop or test students’ unaided thinking and foundational skills, the most reliable approach is to make AI use structurally difficult or unnecessary, rather than prohibiting it and hoping for compliance (Digital Education Council, 2025). Consider the following strategies:
- Human-centred skills: Focus assessments on evaluating skills that generative AI cannot easily replicate, such as critical analysis and creative problem-solving.
- Process emphasis: Design scaffolded assessments that value the learning process, not just the final product, making unauthorized generative AI use less advantageous.
- Traceable development: Require intermediate artifacts—outlines, drafts, revision notes, or annotated planning documents—submitted alongside the final work, so the evolution of a student’s thinking is visible and individual to them (Digital Education Council, 2025).
- Synchronous and supervised formats: Where appropriate for high-stakes or summative moments, shift from asynchronous to synchronous tasks—in-class writing, oral exams, live presentations, or classroom discussion—which are structurally resistant to AI interference because they remove access during task performance (Digital Education Council, 2025).
- Reflection on learning: Integrate metacognitive components into assignments, encouraging students to contemplate their learning journey and examine their thought processes (e.g., “Describe the reasoning behind your approach”). See CTSI’s Assessment Process Reflection Template.
- Personalization: Design assignments that require students to link course concepts to personal experiences or individual reflections.
- Local context: Focus assessment materials and prompts on topics local to the course—recent in-class discussions, current events, or course-specific data—that are not easily accessible or interpretable by AI, and encourage students to connect information sources, course materials, and their own interpretations.
U of T Instructor Example: Reading Annotation Assignment (No GenAI Engagement)
S. Trimble, Assistant Professor, Teaching Stream & Associate Undergraduate, Women and Gender Studies Institute, UTSG
As part of the third-year course, Playing, Sports, Cultures (WGS331, Winter 2025) this assignment requires students to engage deeply with one assigned reading. Using the Hypothesis annotation tool on Quercus, students annotate the text with comments and questions that address both the content (“what” the article says) and the form (“how” it says it). The assignment is designed to prevent generative AI misuse by emphasizing a process-driven approach: students must demonstrate individualized, critical engagement by identifying main arguments, defining key concepts, raising questions, and making personal or course-specific connections. This focus on unique analysis and transparent process limits the usefulness of AI-generated content and supports academic integrity.
For more details on this assignment, visit Professor Trimble’s assessment example page.
Approach 2: Designing Generative AI In to Support Learning
When generative AI is permitted, the same principle applies: determine which parts of the task require students’ independent work, and design those elements to remain resilient to inappropriate AI use, while making the human contribution—and the reasoning behind it—the focus of assessment (Digital Education Council, 2025).
- Align with learning outcomes and skill development: Design assessments where generative AI use supports course-specific learning goals and AI literacy. Consider allowing students to use AI tools for studying, brainstorming, editing, and code debugging as part of building these skills.
- Real-world and complex applications: Integrate generative AI in ways that mirror professional practice and challenge students with multifaceted problems requiring human judgment and creativity.
- Develop critical analysis skills: Ask students to critique and improve AI-generated content, and clearly communicate how their ability to evaluate and use AI outputs will be assessed.
- Encourage reflection: Ask students to submit written reflections on how they used generative AI, analyzing its impact on their learning and approach. See CTSI’s Assessment Process Reflection Template.
- Citation requirements: Share citation resources (such as University of Toronto Libraries “Citing Artificial Intelligence (AI) Generative Tools (including ChatGPT)” guide) and incorporate citation requirements for generative AI use, including the prompts used.
U of T Instructor Example: Thinking Critically about Vaccine Hesitancy and AI Use
Jessica Hill, Associate Professor, Teaching Stream; Department of Molecular Genetics, Temerty Faculty of Medicine
MGY277 (Introduction to Medical Microbiology, Fall 2023) is a large, online, asynchronous course that serves a roughly equal split of second-, third- and fourth-year students who are primarily enrolled in life sciences programs. A course learning objective is to boost scientific literacy related to vaccine hesitancy by analyzing its causes, developing strategies to address it, and reflecting on biases towards vaccine-hesitant individuals. While maintaining this objective, Professor Hill adapted the assignment to incorporate generative AI, guiding students to create profiles of vaccine-hesitant individuals, engage in simulated dialogues with these profiles, and critically analyze the resulting conversations.
For more details on this assignment, visit Professor Hill’s assessment example page.
Approach 3: Validating Across Assessments, Not Just Within Them
Not every assessment can be made fully AI-resilient on its own, and it does not have to be. Where a single task is difficult to protect, validity can instead be established across a connected chain of assessments, with each task building on the student’s earlier work in a way that is contextual to them. In this model, confidence in student learning comes from the coherence and progression across tasks rather than from any single submission (Digital Education Council, 2025). This shifts the picture of learning from a one-time snapshot toward a pattern of growth over the term, and pairs well with distributing evidence across several smaller, varied touchpoints rather than concentrating it in one or two high-stakes products.
It can also help to consider that generative AI in assessment may be a “wicked problem”: one that resists a single correct solution and is context dependent (Corbin et al., 2025b). Framed this way, the goal may not be a perfectly AI-proof assessment, but a workable balance between what is meaningful to assess and what is sustainable for you and your students. There is no need to immediately make every assessment fully resilient, or to redesign everything at once. The difficulty sits in the problem itself: adapting over time, and weighing teaching goals against workload, is sound practice.
The Artificial Intelligence Assessment Scale (AIAS)
As you consider how to define appropriate generative AI use in your course and structure your assessments, you may refer to the updated Artificial Intelligence Assessment Scale (AIAS). This framework is designed to guide instructors in integrating generative AI into assessments while maintaining alignment with course learning outcomes and addressing academic integrity considerations.
| Level of AI Use | Description | Examples |
|---|---|---|
| No AI | Assess independent knowledge and skills in controlled environments. | In-class exams, supervised tasks, oral presentations, and practicums. |
| AI Planning | Allow generative AI use in pre-task activities, but the final work must be independent. | Research design planning, structured brainstorming, argument mapping, literature review preparation, and thesis statement refinement. |
| AI Collaboration | Use generative AI as a supportive tool across the assessment process. Students critically evaluate AI-generated outputs, maintain their unique voices, and transparently document generative AI contributions. | Collaborative writing, peer reviews, technical documentation, and analytical reports. |
| Full AI | Positions generative AI as a co-creator in achieving specific learning outcomes. Students direct generative AI tools to complete defined elements of tasks while students demonstrate their ability to guide and critically evaluate AI contributions. | Problem-based learning projects, case study analyses, policy evaluations, and research synthesis projects. |
| AI Exploration | Students and instructors collaborate to design and implement novel generative AI applications, focusing on field-specific problem-solving and advancement. | Applied research projects, creative projects, innovative solution design, and interdisciplinary investigations. |
Note: If your assessment falls under the “No AI” level of the AIAS, students would be prohibited from using AI tools to complete the assessment. At U of T, students would still be permitted to use AI tools as learning aids, such as by summarizing information related to course readings.
Generative AI and Inclusive Assessment Design
Whether or not you allow generative AI in assessments, consider applying Universal Design for Learning (UDL) principles to proactively enhance access and build student agency. While UDL does not replace specific accommodations for students with disabilities, it helps close gaps between diverse student needs and instructional design, facilitating better support overall.
When designing or revising assessments with generative AI in mind, consider these strategies to reduce learning barriers:
- Encourage critical engagement with AI outputs: Ask students to analyze, fact-check, and improve AI-generated texts, code, or data. For example, have them identify gaps in AI responses, suggest more nuanced answers, or critique the clarity and accuracy of AI-generated content.
- Support creativity and skill development: Considering allowing students to use AI tools for brainstorming, expanding code, synthesizing data, or creating visuals and multimedia. Scaffold assignments with drafts and feedback to develop skills and reduce unauthorized AI use.
- Offer flexible assessment options: Given that there is no one means of action and expression that is optimal for all learners, you may wish to provide multiple ways for students to demonstrate their knowledge—written, visual, or multimodal—some of which may involve generative AI. This increases accessibility and student agency.
- Align assessments with real-world tasks: Design assignments that mirror professional or real-world challenges relevant to your discipline. Encourage students to use evidence and reasoning, not just AI-generated content, to address current debates or problems.
- Be explicit about expectations: Clearly communicate when and how generative AI tools may be used. Provide rubrics, grading criteria, and clear instructions to ensure students understand expectations and academic integrity standards.
3. Developing a Communication Plan with Students and TAs
After finalizing your learning outcomes and assessments, consider establishing clear course policies on generative AI and plan how to communicate them. Students will have different levels of familiarity with AI tools and will look to you for guidance on permitted uses and their impact on learning.
The University of Toronto has created sample syllabus statements to include in course syllabi and course assignments to help inform students what AI technology is, or is not, allowed in the course. In addition to providing an explicit course policy statement on generative AI for your syllabus, you may want to plan how you will share these expectations to students in class and on Quercus. Consider the following strategies:
- Discuss the rationale behind your AI policy and its connection to course outcomes.
- Outline best practices for using and citing generative AI in assessments, if they are permitted.
- Direct students to learning strategy and writing support across campuses: at UTSG through the Centre for Learning Strategy Support (CLSS) and Writing Centres; at UTM via the Student Resource Hub at the Robert Gillespie Academic Skills Centre; and at UTSC through the Centre for Teaching and Learning and the Academic Advising and Career Centre. These resources offer appointments, workshops, and peer coaching to support academic skills development.
- Develop a plan for how you will communicate to your course teaching assistants their roles and responsibilities: How will they evaluate assessments and communicate course policies regarding generative AI use?
U of T Instructor Example: Co-Constructing a GenAI Course Policy
Daniel Corral, Assistant Professor, Department of Leadership, Higher and Adult Education, Ontario Institute for Studies in Education (OISE)
LHAE 5815 (Postsecondary Finance and Accountability) is an online, synchronous graduate course focused on how postsecondary institutions serve historically underrepresented students. Rather than arriving to class with a pre-written GenAI policy, Professor Corral developed the course policy through a structured co-construction process with students grounded in metacognitive principles and students-as-partners pedagogy. The process moved from sharing institutional guidance, to small-group discussion of fair and ethical GenAI use, to a refined final policy distinguishing between GenAI as a learning aid (clarifying concepts, editorial support) versus uses that bypass learning (generating ideas or written content). The resulting policy includes assignment-specific guidelines in an accessible table format and a reflection requirement for any permitted GenAI use.
For more details on this approach, visit Professor Corral’s teaching example page.
Engaging Your Students
As you move through the weeks of the course, it is essential to maintain open communication with students about generative AI. This section offers considerations on how to introduce assessments and engage with students in a way that fosters responsible generative AI use.
1. Setting Clear Expectations for Student GenAI Use
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.
U of T Instructor Example: Co-Creating a GenAI Class Policy Through Collaborative Inquiry
Safieh Moghaddam, Associate Professor, Teaching Stream, Department of Language Studies, University of Toronto Scarborough
LINC10 (Argumentation and Analysis) is an undergraduate linguistics course that serves first- and second-year students. Rather than presenting students with a pre-written GenAI policy, Professor Moghaddam designed a structured in-class activity where students collaboratively built the policy themselves. Working in small groups, students used a handout organized into four sections — Allowed Uses, Not Allowed, Gray Areas, and Justification — to work through where they drew the line on GenAI use and why. Groups then shared their positions in a full-class debrief, arriving at a shared policy through consensus. The activity surfaced student anxiety around AI detection tools, which Professor Moghaddam addressed openly, and the class concluded by agreeing to include an AI Acknowledgment section in their final projects.
For more details on this approach, visit Professor Moghaddam’s teaching example page.
2. Modeling Responsible GenAI Tool Use
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.
Resource: GenAI Literacy Course Modules
The GenAI Literacy Course Modules are open educational resources developed to help University of Toronto instructors introduce and integrate Generative AI literacy into their courses. These modules are designed for flexibility, allowing instructors to use them as-is or adapt them to fit a wide range of course contexts and learning outcomes.
How the Modules Can Be Used
- Integrate directly into existing courses to build GenAI awareness and critical engagement
- Use as standalone resources for self-guided student learning
- Adapt content to prompt classroom discussion, reflection, or formative assessment
- Scaffold assignments or support discipline-specific applications of GenAI
Available Formats
- Standalone Quercus shell for open access
- Importable Quercus modules for easy integration into course shells
- Downloadable files in Microsoft PowerPoint, Word, and PDF formats
- Instructor guide with integration suggestions and reference links
3. Clarifying GenAI Expectations for Teaching Assistants
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.
- Tool 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.
Example: Instructor-TA Team Meeting Plan
During the initial instructor-TA team meeting, the instructor and teaching assistants will review and sign the Description of Duties and Allocation of Hours (DDAH) forms. This is a good opportunity to clarify and discuss the responsibilities and communication protocol for each teaching assistant.
The instructor will organize the conversation around the following questions:
- How should TAs handle student questions related to use of generative AI in the course?
- How do assessments address the potential for generative AI use?
- What is the protocol if a TA is concerned about a student using an unauthorized generative AI tool in an assessment?
- How can TAs encourage student participation in discussions related to generative AI and course policies?
- What potential challenges might arise due to the course size and generative AI use? How can TAs and the instructor collaborate to address these challenges effectively?
4. Explaining Assessment Rationale and Procedures
Since generative AI is a 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: Clarify what students can gain from completing assignments 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: Provide opportunities for assignment discussion in class. 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 or reflection form: By including space for students to reflect on their use of resources, it may support their adherence to the course’s generative AI policy.
- Share a “ready to submit” summary assignment sheet or checklist: This resource may 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. On the assignment sheet or checklist, briefly address: the purpose (skills/knowledge gained), the task (what and how to submit, deadlines, and collaborators), the criteria (what’s expected), and the process (when and how AI tools may be used to support learning).
Resource: Assessment Process Reflection Template
CTSI’s Assessment Process Reflection Template is intended to help students thoughtfully document how they completed an assignment, including their use of AI and other supports. It encourages academic integrity and self-awareness, and can be adapted for any course or platform. The goal is to make students’ learning processes visible in a meaningful way.
What's It For
- Guides students to reflect on their assessment process and resource use, including AI tools.
- Promotes honest self-reflection and transparent attribution.
- Can be used as a graded component, a completion-based activity, or a standalone reflection
How It Works
- Attribution Overview: List all tools, resources, and people (including AI) used, describe their contributions, and explain how you credited them.
- Resource Use Reflection: Write about why you chose each resource, how you used it in your work, and how you checked its accuracy and alignment with your goals.
- Assessment Process Reflection: Reflect on your approach to planning, drafting, and revising your assessment, how your ideas evolved, and what challenges you faced.
- Attach Materials: Submit relevant supporting documents, such as AI prompt logs, drafts, or feedback.
5. Leveraging GenAI to Support Engagement and AI Literacy
With intentional planning on the part of the instructor, generative AI may offer novel opportunities for personalized practice, tailored to students’ needs.
While students may have used generative AI, they may not be familiar with best practices for using AI tools to enhance knowledge retention and critical thinking. If your course has exam components or introduces students to new conceptual material, consider adapting and sharing sample prompts from CTSI’s “AI Virtual Tutor – Effective Prompting Strategies” resource.
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.
U of T Instructor Example: AI-Integrated Concept Map Activity
Nirusha Thavarajah, Associate Professor, Teaching Stream, Department of Physical & Environmental Sciences, University of Toronto Scarborough
CHMA10 (Introductory Chemistry) is an undergraduate chemistry course at UTSC. As part of a lab experiment on determining acetic acid content in vinegar, Professor Thavarajah designed a structured assignment using Microsoft Copilot to help students visualize and organize complex titration procedures through concept maps. Students crafted and iteratively refined prompts to generate an accurate concept map, documenting each prompt and what it improved. A required 250–300 word critical reflection — completed without AI — asked students to evaluate Copilot’s accuracy, identify its limitations, and consider what the process revealed about their own learning. The assignment builds both disciplinary understanding and transferable AI literacy skills, including prompt engineering, critical evaluation of AI output, and metacognitive awareness of when and how AI can genuinely support learning.
For more details on this activity, visit Professor Thavarajah’s teaching example page.
Wrapping Up Your Course
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. Collecting 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:
Reflecting 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.
- 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?
Reflecting With 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.
Reflecting With 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 teaching assistants may comfortably share their concerns. If having a conversation, consider preparing the meeting with guiding questions about their experiences, strategies, and suggestions for improvement.
U of T Instructor Example: Reflecting on AI Use Across the Term
Beth Fischer, Associate Professor, Woodsworth College
Professor Fischer asks students to append a short reflection to each of two research papers, noting which AI tools they used and why, the exact prompts, how they verified the output, and what helped or did not. Because the reflection is completed twice across the term, students can look back and notice how their AI use shifted over time—making the reflection a record of their own development rather than a one-time check. The reflection is ungraded, and each paper is graded before its reflection is read, which encourages candour. An in-class discussion then pools the reflections, turning individual experience into shared insight about where AI helped and where it fell short. Consistent with Universal Design for Learning, the approach meets students where they are, with no penalty for how much—or whether—they used AI.
For more details on this approach, visit Professor Fischer’s teaching example page.
2. Reflecting and Planning Your Next Steps
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-or-minus-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 share approaches with colleagues that integrate best practices in assignment and assessment design in the context of using or preventing the use of generative AI?
- Professional development: By leveraging generative AI, what new skills have I acquired, and what new areas I have identified for further development?
Resource: CTSI Consultations
As you conclude a course, reflecting on your experiences and student feedback can guide your next steps as an instructor. In addition to teaching dossier reviews, CTSI offers consultations to instructors in areas related to teaching and generative AI:
- Teaching strategies in the context of generative AI
- Enhancing student engagement through generative AI tools
- Course design, development, and review
- Revising learning outcomes to address AI literacy
- Using generative AI educational technology such as Microsoft Copilot for in-person, online, and hybrid classrooms
- Interpreting course evaluation data
- Research on pedagogical topics (Scholarship of Teaching and Learning or SoTL) related to generative AI and learning
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