Generative AI in Teaching and Learning

Academic Integrity

The use of generative AI in submitted work must be cited. The library has created a guide to citing generative AI. 

The unapproved and/or uncited use of generative AI is a form of academic misconduct as per Senate Policy 12.2 Student Code of Conduct: Academic Misconduct. Depending on the way in which generative AI is used, the misconduct may fall under clause 4.02.01 Plagiarism, 4.02.02 Cheating or copying, or 4.02.07 Unauthorized aids. Where it is reasonably suspected that a student has committed an offence under policies 4.02.01, 4.02.02, 4.02.07 faculty members can invoke the Procedures for Student Code of Conduct: Academic Misconduct. Communications will be sent to students that the use of generative AI in their courses is a form of academic misconduct unless explicitly permitted by the course instructor.

We do not recommend that instructors use tools that detect the use of generative AI. Finding evidence of the use of generative AI is increasingly difficult as AI is “learning” at an exponential rate and tools to detect its use are reactive and cannot identify generated content with certainty. AI-detection software estimates the probability that an AI-text generator produced the work and these tools have been found to incorrectly flag content that was written by humans as generated by AI. Further, these tools have not been approved by Laurier for privacy and security, and there is currently no way to control how the students’ intellectual property is used after it has been uploaded. A report from a generative AI detection tool is not sufficient evidence for a finding of academic misconduct.

Communicating with Your Students 

As a course instructor, you will need to clearly state within your course outline if and how students can use generative AI in your course. We have developed three sample course outline statements, but for courses where you permit some level of generative AI use, you will need to define the parameters of use. For example, some instructors may allow their students to use generative AI tools for finding general information about the topic but require students to write the assessment on their own. Other instructors may allow students to use generative AI only for editing their completed work.  

In order to support students in understanding and navigating academic integrity while at the same time harnessing potentially productive and ethical engagement with generative AI tools in courses, the following should be clearly defined for students: 

• a shared understanding of generative AI’s uses and limitations as a tool for scholarship;

• articulating appropriate uses of generative AI in course activities and assessments; 

• defining how students should cite generative AI when it is used;

• making students aware of privacy implications of how the data is collected and used.

Sound practices for encouraging students to engage productively and ethically with generative AI can include: 

• beginning a dialogue about generative AI in the classroom and then open discussion boards to continue the conversation and provide a forum for questions. 

• collaborating with your students to co-create a course policy on the use of generative AI, including how to cite generative AI if it is used in the course. Learn more about class contracts in Laurier’s Guide to Teaching, Learning and Assessment. 

• using in-class activities to model productive engagement with generative AI and address questions related to attributions, citations, and authorship. The next section provides ideas for ways to adapt activities and assessments to maximize ethical engagement with AI.

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Assessment Design Considerations

Generative AI is being integrated into the tools that we and our students use on a daily basis and employers are adopting generative AI into their workflows and requiring their employees to use the tools. As an example, Microsoft has invested $10 billion in Open AI, the company that created ChatGPT, and will be embedding the technology in their suite of business applications, including Word, Excel, and PowerPoint.

Re-designing your course assessments to either incorporate generative AI or to limit student use of generative AI can involve significant time and many of the suggestions listed in this resource require substantive time for grading.  Our Teaching Excellence and Innovation team recognizes the importance of your teaching context and will offer support with assessment re-design at the individual or department/program level, to find solutions that are sustainable within your class sizes, curriculum, and grading support capacity.  Familiarizing yourself through experimentation of generative AI programming will help you consider what generative AI can bring to your teaching.  Experimentation builds confidence in identifying the opportunities and the limitations of the technology and in turn, how it may impact your assessment design. When considering using generative AI as a component of your course, keep in mind that as generative AI tools proliferate, changes to terms of use may occur at any time and some may require payment for use.  Further, ChatGPT, the generative AI tool that we have heard the most about, is very popular and may not always be available for use due to high user volumes; it cannot necessarily be relied upon in-the-moment during course activities depending on user volume. 

Assessment design principles that respond to generative AI include: 

• Conducting tests and exams in class or other assessed in-person activities.  For many, the assessment format that most closely aligns with existing practices is assessing through in-person tests, assignments, and activities. This could include flipping some or all of your classroom, with students reading/watching recordings outside of class hours and working on assignments/assessments in-class.

• Instituting course policies such as students must earn an overall passing grade on the in-person course assessments to pass the course.

• Scaffolding assessments throughout the course so that students iterate and respond to feedback. Have students “show their work” at each stage, respond to peer or instructor feedback as they go. 

• Integrating reflection into assessment. Reflection helps students connect their learning to various aspects of their lives.  Consider questions, such as “What does [Concept] mean to you?,” “How would you use [Concept] in your professional life?” into assessments. 

• Including metacognitive components. Questions like "Describe how you learned this concept," or “Summarize how you will integrate this into your current disciplinary knowledge?," can help students deepen their learning and develop metacognitive skills in the presence of generative AI use as well as in its absence.

• Encouraging deep connections between information and ideas. Create questions and assignments that require students to make connections between information sources (e.g. between course materials); with student-generated content (e.g. class discussions, discussion board posts, student presentations, etc.). 

• Leveraging student-generated content. Reference student-generated content (such as discussion board posts, group assignments, student presentations, class discussions, guest speakers, expert interviews, etc.) in test and exam questions and require student-generated content to be referenced in answers, assignment, projects, etc.

• Incorporating citations for generative AI. Generative AI is good at producing well-written, well-structured, and factually oriented information, but still struggles to accurately create properly cited quotations, paraphrasing, images.  Incorporating these can discourage the use of generative AI. When generative AI is used, require students to cite it as a source. 

• Oral components of larger written assessments. Oral assessments encourage student engagement in the learning process. Students can be asked questions about their written answers or to expand upon written answers during an in-person or virtual one-on-one meeting with the instructor.

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Incorporating Generative AI into Learning Activities

Some examples of ways to engage with generative AI in your course include:

• exploring bias, and ethics issues inherent to generative AI by interrogating its outputs.  Ask students to critique information that generative AI shares. This has the potential to broaden and deepen learning experiences, create rich connections, and support problem-solving associated with higher order thinking.

• unpacking assumptions about “neutral” academic writing and its inherent biases while helping students develop their own unique voices, and value their own lived experiences as an asset in their writing. 

• generating non-traditional examples. Generative AI can be engaged as a resource in lesson planning and to support student learning by harnessing its ability to provide a wide range of examples (e.g. “Give me an example of a two-legged omnivore” or “Give me an example of a prototype that was difficult to develop”) including examples that can extend the knowledge reach of the instructor, engage different perspectives, and spark new ways of approaching course topics.

• quickly summarizing basic information and factual content, freeing students for higher-order thinking, writing, and problem solving in their courses, labs, and research assistant work.

• summarizing text from different points of view. Use keywords to generate summaries focused on specific areas of interest or compare summary outputs when different keywords are selected.

• writing structural outlines or literature summaries that students can edit and incorporate into their final products. Students can use track changes to indicate where and why they edited or expanded the AI-generated content, explain how generative AI influenced the final product, and reflect on the impact of generative AI on their learning experience;

• collaborating. Generative AI can be an instant partner for ideas, examples, and feedback. It can help “talk” through ideas with students as they ideate, contribute to producing small amounts of written text (with citation), format citations and bibliographies, create graphics and images that can be incorporated into finished products, and give feedback on the finished product; 

• debating and refining arguments and ideas. Generative AI is good at summarizing, clarifying, and responding to information, and can help students “talk” through ideas to get past the blank page. Generative AI outputs can debate points as students develop and shape their own arguments. The longform AI-assisted discussions could be submitted along with the final product to make clear the role this technology played in the process of learning. Alternatively, students can compete with generative AI to provide the most complete or compelling responses to questions. Students can strive to “beat” a generative AI tool by exposing gaps, fallacies, and incorrect information in AI-generated output;   

• checking, giving feedback, and validating drafts and prototypes. Generative AI can be used to summarize and identify gaps or get quick feedback. For example, a language generation model like ChatGPT can provide feedback and suggest changes to grammar or structure to customize language for a variety of audiences, “talk” through development problems, debug computer code, or provide case study recommendations that students can check against their own work;

• increasing access. For students with accessibility challenges, engaging with generative AI has the potential to create new opportunities for students to express their ideas, synthesize information and engage in course work. For example, generative AI could summarize texts or lecture transcripts in a variety of ways for the benefit of students with cognitive or sensory processing challenges, and students with limited motor function could dictate inputs to an image generation AI to create art or design presentation slides more easily; 

• inventing new ways to engage with AI. Generative AI’s outputs can be fascinating for their accuracy as much as for their inaccuracy and biases, but discovering new applications for generative AI is a key way in which people are using tools like ChatGPT. Challenge yourself and your students to find the most creative, beneficial, and ethical uses of generative AI. 

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Assessment Strategies

Explore ways to support academic integrity in assessments and course activities, as well as maximize productive engagement with generative AI in your assessments. 

Click to jump to each assessment type: 

• Assessed Active Learning & Participation
• Annotated Bibliographies
• Case Studies
• Close Reading with Questions
• Collaborative Essays / Assignments
• Concept Mapping
• Content Summaries
• Exams and Tests
• Experiments
• Fact Sheets and Policy Briefs
• Individual Research Essays
• Infographics
• Literature Reviews
• Observational Assessment
• Open Book "Take Home" Exams or Tests
• Peer Evaluations
• Portfolios
• Poster Presentations
• Presentations
• Prototyping
• Reflection Papers
• Scaffolded Assessment
• Three-Minute-Thesis-Style Presentations
• Timelines

Supporting Integrity

• While generative AI can produce relevant and well-written annotated bibliographies, these are unlikely to include very recent sources (sources published in the last 12-24 months) and its assessments of sources may not be course- or assignment-specific, speak to the relevance of the article to the student’s research topic, or address the reliability of the sources.

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Case Studies

A case study is an in-depth, detailed, and multi-faceted presentation and/or examination of a particular event, scenario, person, group, or organization within a real-world context.

Supporting Integrity

• While generative AI tools like ChatGPT can write and analyze cases in ways that are stylistically in-line with case writing style, its outputs lack depth and its ability to find novel solutions to complex problems is limited. 

• Use case studies focused on very recent events (i.e. no more than 18 months old). Generative AI output is only as current as the data set it was trained on thus limiting its ability write and analyze very current cases. 

• Have students analyze an imaginary case study. Incorporating fictional or fictitious characters, imaginary places, and fantastical events into a case study can frustrate the ability of generative AI to produce meaningful output.

Incorporating Generative AI

• Have generative AI write a case study and have students correct it, expand upon it,  consider it from gendered lenses or Indigenous perspectives make it more inclusive, add citations, add relevant academic references, or analyze it in one or more course-specific manners. For example, using ChatGPT, begin your query with “write a case study about” to get output in a more traditional case writing style (e.g. “write a case study about a small food service business trying to attract new customers with limited marketing budget”).

• After students write their own case studies, have generative AI solve them using different input instructions to help students see their cases from different perspectives. 

• Have generative AI summarize sections of student-written case studies to help students see salient points more easily as they build their executive summaries.  

Find more strategies for assessing using Case Studies in this guide. 

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Close Reading with Questions

Students read an instructor-selected text and respond to questions focused on specific content, skills, and learning outcomes.

Supporting Integrity

• Create prompts that focus on making connections with personal experiences (e.g. How does this relate to something you learned or experienced in a previous week’s class? Outside of class? In another course? When you were younger?) and with student-generated content (e.g. How does this relate to a class discussion, discussion board post, student presentation, etc.?)

• Have student engage in social reading via whole class or small group book clubs or literature circles. Social reading gives students opportunities to design their own learning, gain confidence in discussions, analyze and critique their peers’ arguments, consider perspectives different from their own, and develop social skills and connections.

• Have students engage in social annotation. Social annotation requires learners to comment on assigned text with or without reading prompts, review and engage with the comments of others, connect the assigned text with prior knowledge, and position the assigned text within the context of personal experience. 

Incorporating Generative AI

• Have generative AI produce a summary of a chosen text and have students interrogate it for bias, relevancy to the course, authenticity, etc.

• Have students compare their answers to prompts with generative AI’s answers to the same prompts and then consider questions such as: What are the similarities and differences? How do you feel about generative AI’s answers? To what extent do self-authored answers versus generative AI’s answers impact your knowledge, understanding, skills, and progress toward learning outcomes? This can be used as a synchronous/in-class learning activity, as a graded or ungraded assessment, or as a learning artifact for inclusion in a portfolio. 

Find more strategies for assessing using Close Reading with Questions in this guide.

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Collaborative Essays / Assignments

Students work in pairs, groups, or teams to produce a specific end product such as an essay, report, case study, prototype, presentation, poster, etc. 

Supporting Integrity

• Require students to reference the sources used to produce the end product. Language model generative AI mines a huge sample of text taken from the internet and uses it to generate outputs. Requiring both academic and non-academic references from a combination of specified and students’ choice materials can discourage students from submitting AI-generated assignments.

• Require students to engage with feedback. Build an instructor, TA, or peer review process into assignments and require students to make revisions or otherwise respond to feedback in a staged submission process.

• Laurier instructors can find more strategies for scaffolding writing assignments on Connect. 

Incorporating Generative AI

• Use generative AI as collaborator. Have students and their AI collaborator give each other feedback on their writing. This can help students reflect on how their writing style is unique, improve their ability to communicate what they want to say, identify biases in generative AI outputs, and find an authentic voice in their writing. Students can be asked to produce a reflection on the positive and negative impacts generative AI had on their ability to communicate effectively and authentically.

• Juxtapose generative AI and human collaboration. Have pairs of students, each student with their own research question, collaborate with a generative AI tool and each other to produce a final product. Students work together to coach a generative AI tool for assistance with each research question and also give feedback to each other at various points through the assignment process. Students can be asked to use track changes to show how their essay (or other final product) was improved along the way. After assignment submission, students can be asked to reflect on the process of working with the generative AI tool and their student partner with prompts such as: What worked well and what didn’t? How did your student partner help you produce a better final product? How did the generative AI tool help you produce a better final product? What were the benefits and drawback of your student partner versus your generative AI partner? 

• Generative AI can be used to suggest effective wording or alternate phrasing for written assessments. 

Find more strategies for assessing with Collaborative Essays or Assignments in this guide. 

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Concept Mapping

A concept map is a visual representation of information and relationships. Concept maps can take the form of diagrams, flow charts, tables, and more. 

Supporting Integrity

• Because concept maps combine multiple modes of information through diagrams, drawings, and text, they are excellent ways for students to show their thinking process but are too multimodal for AI to complete well. 

• Concept maps, diagrams, and notes can be included as assignment components. Multiplying the ways students are asked to represent their learning can diminish the usefulness of academically dishonest AI use as well as support more authentic assessment of learning. 

• Concept maps can be assigned after an assignment has been completed as a means for students to represent the knowledge and understanding they realized through the assignment. 

• Ask students to develop a concept map over time, adding to it or revising it at the end of each week or unit, or at midterm and end of term based on new course content students read, watched, listened to, or thought about. Students can also be given other students’ concept maps to add to or revise. These activities can be done individually, in pairs, or in groups.

Incorporating Generative AI

• Ask generative AI to summarize a large concept or course topic in writing. Students can then be challenged to interrogate the output as they create concept maps that that are robust, relevant to the course, inclusive, and equitable by, for example: making decisions about what content to include and exclude, filling in gaps, making connections, adding citations, and addressing biases. 

• Have students use a generative AI output such as an AI-generated Infinite Conversation to create a concept maps that shows connection and/or plots fundamental arguments, as well as reveals gaps, exposes biases, and shows superficiality. Students can then be tasked with making improvements to the original concept map through further research or by integrating their course-based learning.

Find more strategies for assessing with Concept Maps in this guide. 

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Content Summaries

A content summary provides a synopsis and the most salient or pertinent components of a journal article, book, film, or other source material. 

Supporting Integrity

• Students can be asked to include specific types of connections in their content summaries, for example, connections between the source material and specific course content, one or more specific course materials, other courses they’ve taken, personal experiences, a pop-culture reference, a specific artifact (image, song, artifact), etc.

• Request that content summaries speak to the source material’s relevance to the course, a specific unit or theme of the course, a specific assignment or research question, etc.

• Students can be asked to comment on source materials’ credibility, currency, objectivity, etc.

Incorporating Generative AI

• Have generative AI summarize the last course essay or another piece of course material. Ask students to point out the shortcomings and missed nuance of the AI output and then have them reflect on how nuance shapes their overall understanding of a topic. 

• Have generative AI summarize several pieces of course or essay content and build a wider literature review. Consider prompts such as: What themes has the generative AI tool focused on? What themes has it ignored? What do its biases and gaps appear to be and why are those important for understanding the topic? 

Find more strategies for assessing with Content Summaries in this guide. 

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