Teaching with GenAI
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 Process Reflection Template.
- For a practical example of assessment design that uses scaffolding, reflection, and course-specific application to emphasize and evaluate students’ original thinking, see First-Year Drama Essay Assignment (Simon Fraser University).
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 OVPIUE’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 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 human-centred 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 Human-Centred 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 Oregon State University, offers a framework that reconsiders how to define meaningful 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 human-centred skills that do not posit an overreliance on AI tools.
To verify whether your course-level learning outcomes are measuring human-centred 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)?
- Does the learning outcome encourage the development of robust conceptual knowledge, i.e., the “why” behind the “what”?
Figure 1: Bloom’s Taxonomy Revisited Version 2.0, Oregon State University
Example: Modifying Learning Outcomes to Emphasize Human-Centred Skills in a GenAI Context
Consider clarifying which human-centred skills you are assessing in the context of GenAI. The following examples show how learning outcomes can be revised to emphasize these skills, regardless of whether generative AI use is integrated into assessments.
Philosophy
Pre-modified Example: Students will analyze the similarities and differences between various types of knowledge (empirical, rational, testimony, revelation).
Modified Example: Students will critically evaluate and compare types of knowledge (empirical, rational, testimony, revelation) within ethical contexts.
Medicine
Pre-modified Example: Students will compare and contrast how promotional health information and resources effectively and accurately present information to patient care.
Modified Example: 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 Example: Students will analyze data using exploratory data analysis techniques and statistical modeling methods.
Modified Example: Students will apply exploratory data analysis techniques and statistical modeling methods to real-world datasets in statistical computing environments, drawing meaningful conclusions.
Including Learning Outcomes That Encourage AI literacy
AI literacy does not necessarily require students to use or prompt AI tools, but focuses on being critical consumers of AI-generated content. Developing this critical perspective is essential as generative AI becomes more prevalent. Building AI literacy into your course helps students critically evaluate and reflect on AI technologies, even if they do not create AI models themselves (Laupichler et al., 2023: 2).
To integrate AI literacy effectively, consider applying Universal Design for Learning (UDL) principles by explicitly linking course learning outcomes to AI literacy. This approach helps students understand AI’s relevance and supports their engagement and persistence.
Rather than seeing AI literacy as a fixed skill, consider it as a set of competencies across different levels (Almatrafi et al., 2024):
- Know & understand: Ability to explain the basic functions of AI and how to use AI applications.
- Use & apply: Ability to use and adapt AI tools to achieve an objective.
- Evaluate & create: Ability to analyze the outcomes of AI applications critically.
- Navigate ethically: Ability to understand and judge ethical issues related to generative AI, such as privacy, bias, misinformation, and ethical decision-making.
Example: AI Literacy Learning Outcomes
Philosophy: Students will critically analyze and evaluate the ethical implications raised by generative AI technologies.
Relevant AI literacy skill: evaluate & create, navigate ethically
Medicine: Students will assess the potential benefits and limitations of using generative AI systems for medical diagnosis and treatment planning.
Relevant AI literacy skill: evaluate & create
Statistics: Students will interpret AI model outputs and performance metrics in real-world applications.
Relevant AI literacy skill: use & apply, evaluate & create
2: Designing or Revising Assessments
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. As you design or update your assessments—whether to integrate generative AI as a learning supplement or to prevent its unauthorized use—consider how you may foster critical thinking, creativity, and the authentic application of knowledge beyond AI-generated content.
To support authentic learning, consider the following questions:
- How can you create opportunities for students to develop relevant AI literacy skills? For example, incorporating tutorials, workshops, or activities to help students build necessary generative AI literacy skills for the assessment.
- How can you scaffold assessments to encourage critical engagement and independent thinking, whether or not generative AI use is permitted? For example, implementing checkpoints throughout the process to provide timely feedback and support student progress.
- How can you design real-world tasks that require students to apply their own knowledge and critical thinking, whether or not generative AI tools are used? For example, asking students to use generative AI tools to generate initial ideas or data, then critically analyze, refine, and apply these outputs to solve complex, course-relevant problems.
Approach 1: Structuring Assessments to Prevent Unauthorized Generative AI Use
- 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.
- 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 and encourage students to make connections between information sources, course materials, and their own interpretations.
U of T Instructor Example: Developing a Personal Framework for Sensible and Ethical AI Use
Alexandra MacKay, Professor, Teaching Stream Joseph L. Rotman School of Management, UTSG
The objectives for this activity (RSM230, Fall 2024) focus on developing students’ ability to engage with Generative AI in a thoughtful and ethical manner. The primary goal is for students to create a personalized framework that guides their use of GenAI in both academic and professional settings. This process is designed to enhance students’ self-awareness by prompting them to reflect deeply on their learning goals, core values, and career aspirations. Students are also expected to identify and evaluate the core competencies essential for achieving their academic and career objectives. A critical component of the activity is the requirement for students to assess the potential benefits and risks associated with using GenAI, particularly in terms of how it may augment their skills or potentially lead to deskilling in certain areas.
For more details on this assignment, visit Professor MacKay’s assessment example page.
Approach 2: Incorporating Generative AI as a Supplement to Learning
- Align with learning outcomes and skill development: Design assessments where generative AI use supports course-specific learning goals and AI literacy. Considering 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.
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.
The Artificial Intelligence Assessment Scale (AIAS)
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?
Example: GenAI Policy Syllabus Statment
A goal in this course is to teach you how to express your knowledge and skills around [insert course topic], through a variety of in-class interactions, online discussions, written essays and exams. Generative AI can serve as a useful resource by providing tutoring and l support.
In this course, we will use Microsoft Copilot to engage in critical thinking and writing activities and assessments. Students are expected to use Microsoft Copilot for specific aspects of writing assignments and must include with every assignment a short reflection on how they made use of the AI tool in the development of their assignment. No other AI tools are allowed to be used for assessments in this course.
Students may not use AItools when taking tests in this course, but students may use AI tools for other assignments as indicated. If you have any questions about the use of AI applications for course work, please reach out in office hours, by email, or in class.
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.
Example: GenAI Policy Discussion Prompts
To initiate collaborative reflection around generative AI policies and expectations, the first week of class may begin with an ungraded discussion exercise, centred around the following guiding questions:
- Have you used generative AI? For what purposes?
- What is your familiarity level with generative AI tools?
- What is our course policy on generative AI use, and what do you think is the reasoning behind it, given the course learning outcomes?
- What is an example of when you were impressed with/disappointed in output from generative AI?
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 It’s 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.
Example: Think-Pair-Copilot-Pair-Share
Think-Pair-Share (TPS) is a cooperative structure in which partners privately think about a question (or issue, situation, idea, etc.), then discuss their responses with one another. By incorporating generative AI into the activity, students will be exposed to additional perspectives to critically engage with.
- Think: Introduce the topic and encourage students to brainstorm as many ideas as possible, without the use of generative AI
- Pair: Have students pair up with a partner to share their thoughts
- Copilot: Ask students to individually conduct a search on Copilot to find more information on the topic, evaluating its output
- Pair: Have students pair up again with their partner to evaluate the examples and facts they found
- Share: Students share what they found with the whole class
Adapted from: Dillard, 2022
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.
Example: Dialoguing with Copilot for Course Reflection
Activity learning outcome: Students will evaluate and reflect on the use of Microsoft Copilot throughout the entire semester to support their course-level learning goals.
5 minutes: Personalize and insert one or more versions of the following prompt into Microsoft Copilot, so that you can have a conversation about your experiences and challenges in the course:
You are a helpful mentor, guiding me, an undergraduate student in [insert course name] to reflect on benefits, challenges, and lingering questions from a semester-long course, where generative AI tools were used to encourage critical thinking and idea generation. As a helpful mentor, you will dialogue with me (the student), asking thoughtful questions to help me identify the benefits and challenges that I faced using generative AI as a learning tool, as well as any suggestions I have for the instructor and any future students taking the course. You will ask me one question at a time, and you will not ask a further question until I provide a response. You will also ask follow-up questions when my answers are unclear.
20 minutes: Interact with Microsoft Copilot through a back-and-forth dialogue. Save a copy of the dialogue, to be submitted to the instructor.
15 minutes: End the activity by writing a short reflection on the benefits, challenges, and lingering questions that come up, based on your use of generative AI as a learning tool for [insert course topic].
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-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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