U of T Teaching Examples

We are asking U of T instructors how they engage with generative AI tools in the teaching. This section features instructor profiles, as well as examples of AI-integrated assessments and learning activities.

Instructor Profiles

Elaine Khoo, Associate Professor, Teaching Stream; Centre for Teaching and Learning, English Language Development Support Coordinator, UTSC

Steve Easterbrook, Director, School of the Environment, Faculty of Arts & Science, UTSG

Dan Zingaro, Associate Professor, Teaching Stream and Associate Chair (CSC) Mathematical and Computational Sciences, UTM

Noa Yaari, Communication Instructor, Institute for Studies in Transdisciplinary Engineering Education and Practice (ISTEP), Faculty of Applied Science & Engineering, UTSG

Jessica Hill, Associate Professor, Teaching Stream; Department of Molecular Genetics, Temerty Faculty of Medicine, UTSG

Nazanin Khazra, Assistant Professor, Teaching Stream, Department of Economics, Faculty of Arts & Science, UTSG

Robert Bentley, Assistant Professor, Kinesiology & Physical Education, UTSG

Kenneth Yip, Assistant Professor, Department of Cell and Systems Biology, UTSG

Assessment Examples

Thinking Critically about Vaccine Hesitancy and AI Use - Jessica Hill, MGY277

Course: MGY277, Introduction to Medical Microbiology
Session: Fall 2023
Instructor: Jessica Hill, Associate Professor, Teaching Stream; Department of Molecular Genetics, Temerty Faculty of Medicine

Assessment Objectives

MGY277 (Introduction to Medical Microbiology) 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.

Assignment Process

Profile Generation: Students use the Microsoft Copilot, U of T’s institutionally-approved Generative AI tool, to create profiles of vaccine-hesitant individuals, including demographic information and reasons for hesitancy. To guide their AI literacy skill development, students are provided students with a sample prompt.
Conversation Simulation: Microsoft Copilot generates a dialogue between a vaccine-hesitant person and a friend trying to persuade them.
Critical Analysis: Students evaluate the AI-generated conversation using evidence-informed strategies for addressing vaccine hesitancy.
Source Evaluation: Students assess the credibility of sources used by Microsoft Copilot for profile generation.
Ethical Reflection: Students consider the ethical and social implications of using AI to generate profiles and conversations about vaccine-hesitant individuals, asking themselves: How accurate and realistic are the outputs? How might they affect my perceptions and attitudes towards vaccine-hesitant people? How might they influence my own decisions about vaccination?
Peer Discussion: Students compare their experiences and outputs with classmates on a discussion board.

Future-Focused Student Skill Development

This assignment aligns well with the University of Calgary’s STRIVE model for designing assessments that effectively incorporate generative AI. In particular, it exemplifies the goal of student-centeredness, as students are guided to engage with AI tools as a starting point for their learning, thereby promoting flexibility and critical thinking around AI-generated content. They are also encouraged to reflect on their own perceptions and decision-making processes regarding vaccination, which can foster awareness of self and others. In addition, this assessment aligns with the STRIVE model’s approach to integrity: It openly incorporates AI use into the learning process and specifies when and how students are to engage AI tools to support critical thinking. Students are both guided on how to cite AI-generated content, and how to effectively reflect on AI tool limitations.

Student Feedback

Professor Hill shares: “Feedback was collected from students regarding the use of Generative AI in the course specifically. The overall response was positive, with students expressing appreciation for its integration. For instance, one student remarked, “I liked the use of Bing AI/Copilot for assignment 1, as it was different to anything I’ve done in other courses. I also appreciated how we reflected on the validity and biases of the AI-generated responses. It seems as though a lot of my other courses are very against the use of AI, so I like the way it’s been introduced in this course as a means of success, rather than a means of cheating like the other courses make it out to be.””

Digital History Business Model Assignment - Noa Yaari, HIS393

Course: HIS393, Digital History
Session: Fall 2023
Instructor: Dr. Noa Yaari, Communication Instructor, Institute for Studies in Transdisciplinary Engineering Education and Practice (ISTEP), Faculty of Applied Science & Engineering, UTSG

Assessment Objectives

A learning objective for HIS393 (Digital History) was that students develop the ability to conceptualize a new business in the field of digital history, challenging them to think creatively and entrepreneurially in relation to real-world problems. In this assessment, Dr. Yaari guided students to develop a comprehensive business model; as part of this, they created a logo for their proposed business, and were allowed to use generative AI tools in a way that inspired critical reflection on the design process. This project aims to boost creativity, capitalize on historical knowledge and technological skills, and develop communication abilities. The assessment emphasizes realistic creativity, research skills, and the ability to articulate ideas clearly in both textual and visual format.

Assessment Process

Project Initiation: Students form teams of up to 3 members or work individually
Research and Conceptualization: Identify and analyze at least two existing digital history projects, and use this to conceptualize a new business in digital history
Business Model Development: Students complete a business model template, addressing key components, including: problem identification; customer segment analysis; unique value proposition, solution details, and revenue streams, among other categories.
Logo Creation (GenAI Use): Students are tasked with designing a logo for their proposed digital history business, with the option to utilize AI applications for this purpose. If opting for AI-assisted design, students must provide the full prompt or input used, describe the entire creation process in detail, and critically reflect on their experience with the AI-assisted design. This approach encourages transparency in AI use while inspiring critical thinking about the role of AI in creative processes. See examples of impressive logos that students have created with AI tools.
Formatting and Presentation: Students ensure proper layout and multimedia integration.

Future-Focused Skilled Development

This assignment aligns well with the University of Calgary’s STRIVE model for designing assessments that incorporate generative AI. It exemplifies student-centeredness by encouraging direct interaction with AI tools for logo creation, allowing students to critically evaluate AI-generated content. The multi-component nature of the assignment, combining written analysis with AI-assisted visual design, ensures a comprehensive and valid assessment of students’ skills. In addition, by encouraging AI use for logo creation, the assignment provides equitable opportunities for all students to engage and develop their creativity and critical thinking skills, regardless of their graphic design abilities. As a whole, this assignment promotes multimodal engagement, reflective analysis, and the development of AI literacy skills.

Student Feedback

One students from the course, Hanne Gilbert Sandoval (Sofi) reflects on her Business Model, including the use of GenAI, in a 3-minute video she created for the final assessment in the course, the ePortfolio. As Sofi explains in the video, using text-to-image tools enabled her to move faster and more accurately toward communicating her vision, and eventually, ponder the potential new technology brings to History.

Dr. Yaari shares: “By incorporating AI into the logo design process, students not only learned about digital history and business concepts but also gained hands-on experience with emerging technologies that are likely to play a significant role in their future work environments.”

Literature Review Assignment Using AI Tools - Nazanin Khazra, ECO225

Course: ECO225, Big Data Tools for Economists
Session: Fall 2023
Instructor: Nazanin Khazra, Assistant Professor, Teaching Stream, Department of Economics

Assessment Objectives

The following assignment was part of the course, Big Data Tools for Economists (ECO225). The objective for this assignment was to familiarize students with conducting literature reviews using AI tools, enabling them to analyze and synthesize economic research effectively and to formulate research questions.

By leveraging AI tools like GPT and Research Rabbit, students streamlined their research process, gaining insights into economic topics while supporting their ability to critically evaluate past studies and their methodologies.

Assessment Process

Search and Identify Key Papers: Students begin by searching for key papers related to their research idea using databases like Google Scholar or academic journals. They identify core themes, methods, and findings from these papers.
AI-Assisted Literature Mapping: Using the AI tool Research Rabbit, students upload these papers and generate a visual cluster map that shows the relationships between different research areas. The map helps students visualize where their research fits within the broader academic landscape, highlighting connections across fields. This visualization finally made it possible for me to explain how different fields intersect and how to find one’s contribution to the relevant literature.
Lit Review Table: Finally, students use ChatGPT to summarize the core aspects of each paper, including citation, data, method, and results. They create a table to organize this information, which serves as a foundational tool for their literature review. Students are required to verify all information and ensure the AI-generated summaries are accurate.

Future-Focused Skill Development

This assessment aligns well with the University of Calgary’s STRIVE model, particularly in how it emphasizes transparency and supports responsibility. The exercise promotes transparency by openly incorporating AI tools like GPT and ResearchRabbit into the literature review process, requiring students to specify how they engage with these tools and acknowledge their use. It also fosters responsibility by guiding students to verify AI-generated information and critically evaluate its limitations. This not only supports students’ current research capabilities but also prepares them for the ethical considerations of AI integration in future economic research. By learning to balance the benefits of AI assistance with the need for human verification and critical thinking, students develop crucial skills for maintaining academic integrity.

Student Feedback

Professor Khazra shares: “Since Gen AI technologies are still relatively new, many students are not yet familiar with the effective and appropriate use of them. Guiding and supporting students in this area helps them develop valuable skills for using Gen AI in both research and professional settings. This is a skill that should be incorporated into higher education. Students found these exercises helpful during and post class.

One notable outcome of this exercise was that students revisited their literature review multiple times throughout the semester. In many instances, students were bringing their lit review tables to office hours asking about interpretations or comparing their estimates to other papers’ findings. Interpreting one’s estimates in the context of existing literature is a challenging task, even for experienced researchers. They also took these skills to the job market and their internships.”

Research Question Assignment Using AI Tools - Nazanin Khazra, ECO225

Course: ECO225, Big Data Tools for Economists
Session: Fall 2023
Instructor: Nazanin Khazra, Assistant Professor, Teaching Stream, Department of Economics

Assessment Objectives

The following assignment was part of the course, Big Data Tools for Economists (ECO225). As with the literature review, the objective for this assignment was to familiarize students with conducting literature reviews using AI tools, enabling them to analyze and synthesize economic research effectively and to formulate research questions.

Students learned to formulate narrow research questions that address gaps in existing literature and improve their skills in interpreting and presenting data visually.

Assessment Process

Initial Question Generation: Students begin by uploading a sample of their dataset or providing a detailed explanation of the data to ChatGPT. They then prompt ChatGPT to generate ten potential research questions based on the dataset. If they have ideas of their own, they can share it at this stage.
Selection and Refinement: From the list of ten questions generated by ChatGPT, students select five questions they find most interesting and relevant. They are required to critically assess these questions by identifying potential challenges or limitations associated with each one. This step encourages students to think about the feasibility and scope of their research.
Final Research Question: After refining the list, students choose one research question to pursue. This exercise saves time and encourages deeper engagement, as students can quickly see a wide range of possibilities before narrowing their focus. Students refine this finalized question through the semester as they work on their paper and adjust based on their data work.

Future-Focused Skill Development

As with the accompanying literature review assignment described above, this assessment aligns well with the University of Calgary’s STRIVE model. The exercise especially promotes equity by offering a space for personalized learning. In allowing students to generate and refine research questions based on their own datasets or interests, the assessment creates space for diverse approaches to engagement, reflection, and knowledge creation. This approach recognizes that students have unique ways of interacting with information, developing insights, and demonstrating understanding. By tailoring the research process to individual interests and datasets, students can explore topics through methods that resonate with their personal learning preferences, fostering deeper engagement and more meaningful outcomes. In addition, the exercise allows students to revisit and refine their work throughout the semester, accommodating different learning paces and styles, and ultimately supporting an inclusive learning environment.

Student Feedback

Professor Khazra shares: “This is a direct quote from a student email in August of 2024: “While the programming skills I gained from ECO225 were undoubtedly invaluable [in my internship], I think the most important thing I took away from the course was how to use ChatGPT effectively and efficiently. I think that could a great selling point for the course considering how in demand the usage of gen AI has become (and how bad most are at using it!)””

Introductory Laboratory and AI Analysis Assignment - Robert Bentley, KPE360

Course: KPE360: Advanced Cardiorespiratory Physiology
Session: Fall 2024
Instructor: Robert Bentley, Assistant Professor, Kinesiology & Physical Education, UTSG

Assessment Objectives

The introductory laboratory aims to familiarize students with the experimental environment and develop data collection skills essential for subsequent full laboratory experiments. In addition, the assessment introduces students to the use and critical evaluation of generative AI tools within scientific contexts.

Assessment Process

Experimental Setup: Students set up equipment including PowerLab, blood pressure sensors, and ECG electrodes.
Data Collection: Students perform a series of trials, including Upright Rest (2 minutes), Squat-to-Stand (2 minutes rest + 1 minute squat + 2 minutes recovery), Stand-to-Lying down (2 minutes rest + 2 minutes lying down) and a Valsalva Maneuver (2 minutes rest + 10 sec Valsalva + 2 minutes recovery).
Data Analysis: Students analyze heart rate and blood pressure data using LabChart software.
Generative AI Component: Students use ChatGPT or Microsoft Copilot to generate a response to the question “In 250 words, explain why I feel light headed when rising from a squat?” They then critically evaluate and correct the AI-generated response based on provided scientific literature.
Report Writing: Students prepare a report including: Title Page; Methods; Written Results; Tables/Figures; and Generative AI Question response.

Future-Focused Skilled Development

This laboratory assignment exemplifies key aspects of the University of Calgary’s STRIVE model, particularly in terms of student-centeredness and validity. The student-centered approach is evident in how the laboratory engages students directly with experimental equipment and data analysis software, promoting hands-on learning and critical thinking; students collect their own physiological data, analyze it using LabChart software, interpret the results, and analyze Generative AI output. This active engagement allows students to take ownership of their learning process and develop practical skills essential for junior scholars of kinesiology and physical education. The validity of the assessment is ensured through its multi-component nature and clear alignment with specific learning objectives. The inclusion of a generative AI component further supports the validity by requiring students to critically evaluate AI-generated content against scientific literature, developing their digital literacy and critical thinking skills. This comprehensive approach ensures that the assessment accurately measures a range of skills and knowledge relevant to the course objectives and future professional practice.

Student Feedback

Professor Bentely shares: “Overall, students appreciated the incorporation of generative AI, and the resulting development of AI literacy, given its rapidly developing relevance. Further, some students were surprised that the generated AI response was seemingly unrelated to the provided prompt while other students thought the AI did a reasonable layman’s explanation but lacked detail. It seems the takeaway by the students is that while generative AI may provide a starting point, critical assessment is required.“

Learning Activity Examples

AI Chatbots for Large Undergraduate Biology Courses - Kenneth Yip, BIO130 & BIO230

Courses: BIO130H1: Molecular and Cell Biology and BIO230H1: From Genes to Organisms
Session: Winter LEC5101 Thursdays 6pm-9pm; Fall LEC0101 Tuesdays 12pm-1pm Thursdays 1pm-2pm; Fall LEC5101 Tuesdays 6pm-9pm
Instructor: Kenneth Yip, Assistant Professor, Cell and Systems Biology

Learning Activity Objectives

The course-specific generative AI chat tools, named ChatBIO130 and ChatBIO230, aim to support student learning by:

Providing personalized, on-demand assistance with course content and concepts
Offering space for students to engage in self-directed practice that prepares them for summative assessments
Integrating lecture and laboratory material to create a comprehensive learning resource
Improving the overall student curricular experience in large molecular and cellular biology courses

Learning Activity Process

AI Chatbot Interaction: Students engage with ChatBIO130 or ChatBIO230 as an optional resource throughout the course.
Personalized Learning: The chatbots provide tailored explanations and examples based on individual student queries.
Formative Assessment: Students can practice with multiple-choice and short-answer questions generated by the AI, receiving immediate feedback.
Laboratory Integration: The chatbots incorporate laboratory, connecting theoretical concepts with practical applications.
Guided Usage: Students are provided with comprehensive documentation on appropriate chatbot use, limitations, and alternative resources.
Continuous Improvement: The teaching team regularly updates the chatbots’ knowledge base and capabilities based on student feedback and usage data.

Fostering Inclusive Learning Environments

Overall, the integration of the virtual chatbot supports the creation of an inclusive and effective educational environment, which can be challenging in the context of large courses. First, the chatbot supports student-centered learning by inviting students to explore course content at their own pace and focus on areas they find challenging, promoting autonomy and personalized learning. In addition, it supports differentiated instruction: The chatbots’ ability to provide personalized responses supports diverse learning styles and needs, a cornerstone of inclusive education. Lastly, the chatbots provide a unique opportunity for students to actively engage with course material in real-time, even in large classes where individual interaction with instructors may be limited. This allows students to ask questions, seek clarification, and explore topics in depth at their own pace, promoting a more interactive and personalized learning experience, despite the challenges of large class sizes.

Student Feedback

The implementation of ChatBIO130 and ChatBIO230 has demonstrated exceptional student engagement and educational impact. There is particularly intensive usage observed during key academic periods such as pre-examination preparation. The completely optional nature of these tools makes their widespread adoption especially noteworthy, suggesting that students find genuine value in the resource.

Survey responses highlight three key benefits:

Reduced Academic Stress: A significant majority of students reported decreased anxiety levels when having access to the chat tools
Accessibility: Students particularly value the 24/7 availability of the resource, allowing them to seek help at any time
Learning Support: The ability to ask sequential questions enables students to develop deeper understanding through progressive inquiry

The overwhelming student support for continued development of these tools suggests that they are meeting a crucial need in large-format biology courses, particularly in providing personalized learning support at scale.

Submit an Example Assessment or Activity

We are asking U of T instructors how they engage with generative AI tools in the teaching. As this section grows, we will include brief profiles and examples of assessments and offerings.

If you would like to share an assessment that uses generative AI, or an example of how you and your students engage with generative AI in your course, please complete this online form.

There are a growing number of generative AI tools available and the capabilities of these tools is evolving at a rapid rate. Currently, Microsoft Copilot is the recommended generative AI tool to use at U of T. When a user signs in using University credentials, Microsoft Copilot conforms to U of T’s privacy and security standards (i.e., does not share any data with Microsoft or any other company). In addition, Contact North AI Tutor Pro and Contact North AI Teacher’s Assistant Pro conform to U of T’s privacy and security standards. Please be aware that any other generative AI tool used within a U of T course or organization that has not been vetted for privacy or copyright concerns should be used with caution. If you would like to learn more about the tools available in U of T’s academic toolbox, please visit ARC’s New Tools.