Assessment and Assignment Guidance in the GAI Era

CATL is often asked questions about how to approach assessments in the wake of easy access to generative artificial intelligence (GAI). We hope to crowd-source suggestions and examples from our own instructors so that we can build a repository of work from the UWGB community in Canvas. Please take our GAI Assignment Repository survey if you have any ideas or assignment samples that you are willing to contribute. We will collect results and share them once we have a critical mass. In the meantime, please feel free to use these resources from other institutions or professional organizations.

Deterring the Use of GAI

One of the most frequent concerns expressed by instructors is that students will simply use GAI to complete their assignments. It makes very good sense to type your own assignment prompt into Copilot or another GAI tool to see if it can complete the assessment – and modify the assignment if it can. Although these are not perfect strategies, multiple authors have suggested the following mitigation strategies:

  • Be very clear with students about your GAI policy and talk with them about the reasons for it and for completing their work independently. Carleton College has an interesting site aimed at a student audience about GAI use and evolving understandings of academic integrity that you could use to frame a discussion with your class.
  • Use shorter, more frequent assessments that build on each other to reduce the motivation to use GAI that can come with a small number of high-stakes/point assignments.
  • Scaffold your assignments such that each one builds on another.
  • Create assessments that involve integration of material from class (which GAI does not have access to) with other sources.
  • Require some element of reflection on the learning experience or completing the assignment.
  • Ask students to submit outlines, drafts, or save their document history in Word or Google Docs to show the evolution of a paper or project.
  • Consider the use of oral presentations, including question and answer, as a method for demonstrating understanding.
  • Employ in-class writing assignments such as Minute Papers for face-to-face courses.

The Office of Digital Learning at UN-Reno has created a more detailed document with strategies for re-designing assessments in the GAI era.

Teaching About the Use and Ethics of GAI

Some instructors are seeking strategies for teaching students about GAI and how to use it, as well as about related issues, such as the ethics of use. Instructors from the University of Central Florida collected more than 60 assignments related to GAI, including the teaching of prompt engineering. The New School has a shorter, more direct page of instructions for prompt creation. Co-Intelligence author Dr. Ethan Mollick and collaborator Dr. Lilach Mollick have created an extensive paper that outlines seven complex ways to use GAI in education, and they include sample prompts and a discussion of the potential risks of their ideas. Finally, as just one example of teaching about the ethics of AI use, consider this assignment designed, in part, to teach about cultural bias in GAI.

Creating Assessments that Make Use of GAI

Finally, there are instructors looking for creative assignments that use GAI as an intentional tool in an assessment to facilitate student learning. Numerous universities and organizations have assembled collections of such assignments, including the following:

Remember, we hope to create a collection of examples from UWGB instructors. Please complete our survey today to share your contribution with your colleagues!

Sample Assignments for Different Approaches to GAI Use

In a previous CATL article, we recommended using the traffic light model to guide students on the appropriate use of generative AI (GAI) in assignments and course activities. Assuming you’ve already included a policy on GAI in your syllabus, it’s also important to provide clear instructions in your assignment descriptions. Below are some examples of assignment descriptions, using the traffic light approach and graphic. Instructors will vary on whether they want to use that visual or simply explain in words. If you choose to use the stoplight visuals, please be sure to provide an accompanying description of what that means for your specific assignment. While tailored to specific subjects, these samples share common strategies.

Consider the following general suggestions when designing your assignments:

  • Be clear and specific about GAI use in your syllabi and assignments. Clearly outline when and how GAI can be used for assignments and activities. Avoid ambiguity so students know exactly what’s expected. For example, if brainstorming is allowed but not writing, specify that distinction.
  • Include GAI usage disclaimers in assignment directions. Regularly remind students by adding a GAI disclaimer at the beginning of assignment instructions. This will make them accustomed to looking for guidance on AI use before starting their work.
  • Explain the rational for AI use or nonuse. Help students understand the reasoning behind when GAI can or cannot be used. This can reinforce the learning objectives and clarify the purposes behind your guidelines.
  • Clarify the criteria for evaluating AI collaboration. Specify how assignments will be graded concerning AI use. If students need to acknowledge or cite their AI usage, provide specific instructions on how they should do so.
  • Define which AI tools students can use. Should students stick to Microsoft Copilot (available to them with their UWGB account, so they don’t have to provide personal information to a third party or pay a subscription fee) or can they use others like ChatGPT?
  • Use the TILT framework. Leading with transparent design for assignments and activities helps students clearly understand the purpose, tasks, and assessment criteria. This framework can also help instructors clarify how GAI should be used and assessed in assignments.

Sample Assignment Instructions on AI Use

Red Light Approach: No GAI Use Permitted Assignment Example

The example below is for a writing emphasis course and the assignment purpose is to evaluate students’ own writing. For this assignment, GAI tools are not allowed. The instructor includes an explanation of this description to further clarify the assignment’s purpose.

Yellow Light Approach: GAI Use Permitted for Specific Tasks/Tools Examples

The yellow-light approach can be hard to define depending on what you want students to practice and develop for a given assignment. We’ve provided two samples below that each take a slightly different approach, but all clearly label what tools and for what tasks AI can be used and why.

Green Light Approach: All GAI Use Permitted

Instructors may choose to take a green light approach to AI for all assignments or allow AI use for selected assignments. The example below takes a low-stakes approach, permitting full AI use to encourage experimentation. Even with this method, instructors should provide clear assignment expectations.

Learn More

Explore even more CATL resources related to AI in education.

Hands of students completing a cloud-shaped puzzle which reads "Online Collaboration"

Up and Running with Remote Group Work

A Case for Group Work

Group work can elicit negative reactions from instructors and students alike. Often enough, students groan about doing it and instructors dread grading it. The process is ripe for communication breakdowns resulting in stress from both perspectives. On top of this, the digital learning environment tends to compound these issues. Why then is group work so prevalent?

The answer is that, when done well, group activities help foster engagement and build relationships. Collaborative work helps students develop important skills like effectively articulating ideas, active listening, and cooperation with peers. Collaborative assignments correlate strongly with student success positioning them as one of eight high-impact practices identified by the Association of American Colleges and Universities. Making group work a worthwhile experience for students requires extra consideration and planning, but the positive gains are worth the effort.

Designing Group Work for Student Success

How can we design collaborative activities that are a quality learning experience for students? Scaffolding makes sure students are confident in their understanding of and ability to execute the activity. UW-Extension has created a helpful guide on facilitating group work that outlines three key suggestions to get you started. First, be sure students understand the purpose of the activity, in terms of what they are supposed to learn from it and why it is a group activity. Second, provide support so students have the necessary tools and training to collaborate. You are clear how and when students are to collaborate or provide suggestions. You ensure students understand how to use the needed technologies. Finally, providing opportunities for peer- and self-evaluation can alleviate frustrations of unequal workload by having students evaluate their own and their peers’ contributions. As challenges arise, guide groups toward solutions that are flexible but fair to all members. When embarking on group projects, be prepared to provide students with guidance about what to do when someone on the team is not meeting the group’s expectations.

One example of this as you design your group projects is to ask yourself whether it’s important students meet synchronously. If so, how might you design the project for students with caregiving responsibilities or with full-time or “off hours” work schedules? These students may not be able to meet as regularly or at the same time as other students. You might also consider whether all students need to hold the same role within the group, or if their collective project be split up based on group roles.

Consider how the group dynamics can impact student experiences. Helping students come up with a plan for group work and methods of holding one another accountable promotes an equitable learning environment. Consider any of these tools to help your students coordinate these efforts:

Assessing Group Work

Equitable, specific, and transparent grading are crucial to group-work success. The Eberly Center for Teaching Excellence of Carnegie Mellon University has a great resource on how to assess group work, including samples. This resource breaks grading group work down into three areas. First, assess group work based on both individual and group learning and performance. Include an individual assessment component to motivate all students to contribute and help them to feel their individual efforts are recognized. Also assess the process along with the product. What skills are you hoping students develop by working in groups? Your choice of assessment should point to these skills. One way to meet this need is to have students complete reflective team, peer, or individual evaluations as described above. Finally, outline your assessment criteria and grading scheme upfront. Students should have clear expectations of how you will assess them. Include percentages for team vs. individual components and product vs. process components as they relate to the total project grade.

Tools for Working Collaboratively

Picking the right tool among the many of what is available is an important step. First, consider how you would like students to collaborate for the activity. Is it important that students talk or chat synchronously, asynchronously, or both? Will students share files?

The following suggestions include the main collaboration tools supported at UWGB. Click to expand the sections for the various tools below.

If you are interested in learning more about any of these tools, consider scheduling a consultation with a CATL member.

Canvas discussions are one option for student collaboration. Operating much like an online forum, discussions are best suited for asynchronous communication, meaning students can post and reply to messages at any time, in any order. If you have groups set up in Canvas, you can create group discussions in which group members can only see one another’s posts. You can also adjust your course settings so that students can create their own discussion threads as well.

Hypothesis is a Canvas integration that lets instructors and students collaboratively annotate a digital document or website. Hypothesis annotation activities can be completed synchronously, such as over a Zoom call, or asynchronously on students' own time. Activities can be created for either the whole class or for small groups and are a great way for students to bounce around ideas about a text or reading. 

Office 365 refers to the online Microsoft Office Suite, including Word, PowerPoint, and Excel. Students can work collaboratively and asynchronously on projects using online document versions of any of these software, which updates changes in nearly real time. Microsoft Office 365 has partial integration with Canvas, allowing students to set up and share Office documents from within Canvas using the Collaborations feature. Students will have to log in to Office 365 through their Canvas course before they can use most features of Canvas and Office 365 integration.

Zoom is one of two web conferencing tools supported by the university, the other being Teams. The Zoom Canvas integration allows instructors to set up meetings within a Canvas course. Students can then access meeting and recording links from within the Canvas course. As such, it is generally easy to for students to access and use. One downside to Zoom is that it is a purely synchronous meeting tool, so students will have to coordinate their schedules or find other ways of including members that may not be able to attend a live meeting. Students that wish to set up meetings amongst themselves are not able to set up meetings with the Canvas integration, though they can use the Zoom desktop app or web portal and their UWGB account.

Microsoft Teams is a collaboration tool that combines web conferencing, synchronous and asynchronous text communications (in the form of chat and posts), and shared, collaborative file space. Microsoft Teams also has partial integration with Canvas, meaning students and instructors can create and share Teams meeting links within the Rich Content Editor of Canvas (in pages, announcements, discussions, etc.).

Putting It into Practice

When we ask students to work collaboratively, it’s important we reveal the “hidden curriculum” by building in the steps they should take to be a successful team. As a starting point, asking students to answer these questions helps clarify the work of the group:

  • “Who’s on the team?”
  • “What are your tasks as a group?”
  • “How will you communicate?” (Asynchronously? Synchronously?)
  • “How will you ensure everyone can meet the deadlines you set?”
  • “If or when someone misses a meeting, how will you ensure that everyone has access to the information they’ll need to help you all complete the project on time?”
  • “When will you give each other feedback before you turn in the final assignment?”

For a ‘bare bones’ group assignment, take the above considerations on designing and assessing groupwork into account and create a worksheet for the student groups to fill out together. Create a Canvas group assignment to collect those agreements, assign it points that will be a part of the whole project grade, and set the deadline for turning it in early so that students establish their plan early enough for it to benefit their group. Scaffolded activities that give students enough structure and agency is a delicate balance, but these kinds of guided worksheets and steps can help students focus their energy on the project, assignment, or task once everyone is on the same page.

Let’s keep the conversation going!

Do you have some tried and tested strategies for helping students coordinate and complete group work online? Send them our way by emailing: CATL@uwgb.edu or comment below!

Generative Artificial Intelligence (GAI) and Acknowledging or Citing Use

UW-Green Bay’s libraries have an excellent student-facing webpage on how to acknowledge or formally cite the use of GAI. This blog is intended to supplement that resource with information more specific to instructors. Professors will be vital in helping students understand both the ethics and practicalities of transparency when employing GAI tools in our work. Please keep the following caveats in mind as you explore this resource.

  • As with all things GAI, new developments are rapid and commonplace, which means everyone needs to be on the alert for changes.
  • Instructors are the ones who decide their specific course policies on disclosing or citing GAI. The information below provides some options for formatting acknowledgments, but they are not exhaustive.
  • Providing acknowledgment for the use of GAI may seem straightforward, but it is actually a very nuanced topic. Questions about copyright implications, whether AI can be considered an “author,” and the ethics of relationships between large AI entities and publishing houses are beyond the scope of this blog. Know, though, that such issues are being discussed.
  • Please remember that it is not only important for students to acknowledge or cite the use of GAI. Instructors need to do so with their use of it, as well.

Acknowledgment or Citation of GAI

There is a difference between acknowledging the use of GAI with a simple statement at the end of a paper, requiring students to submit a full transcript of their GAI chat in an appendix, and providing a formal citation in APA, MLA, or Chicago styles.

  • UWGB Libraries have some excellent acknowledgment examples on their page.
  • UWM’s library page provides basic templates for citations intended to be consistent with APA, MLA, and Chicago styles.
  • There are also lengthy blog explanations and detailed citation examples available directly from APA, MLA, and the Chicago Manual of Style.

Regardless of the specific format being used, the information likely to be required to acknowledge or cite GAI includes:

  1. The name of the GAI tool (e.g., Copilot, ChatGPT)
    Microsoft Copilot, OpenAI’s ChatGPT 4.o (May 23, 2024 version), etc.
  2. The specific use of the GAI tool
    “to correct grammar and reduce the length in one paragraph of a 15-page paper”
  3. The precise prompts entered (initial and follow-up)
    “please reduce this paragraph by 50 words and correct grammatical errors”; follow-up prompt: “now cut 50 words from this revised version”
  4. The specific output and how it was used (perhaps even a full transcript)
    “specific suggestions, some of which were followed, of words to cut and run-on sentences to revise”
  5. The date the content was created
    August 13, 2024

Ultimately, instructors decide what format is best for their course based on their field of study, the nature and extent of GAI use permitted, and the purpose of the assignment. It is important to proactively provide specific information to students about assignments. Professors who are particularly interested in whether students are using GAI effectively may focus on the prompts used or even ask for the full transcript of a session. If, in a specific assignment, the instructor is more interested in students learning their discipline’s citation style, then they might ask for a formal citation using APA format. Although the decision is up to the professor, they should tell students in advance and strongly encourage them to have separate Word documents for each of their classes in which they save any GAI chats (including prompts and output) and their date. That way they have records to go back to; If they use Copilot with data protection, it does not save the content of sessions.

What Messages Might I Give to Students about Using, Disclosing, or Citing GAI?

Instructors should consider how they will apply this information about acknowledgments and citations in their own classes. CATL encourages you to do the following in your work with students.

  1. Decide on a policy for acknowledging/citing GAI use for each course assignment and communicate it in your syllabus and any applicable handouts, Canvas pages, etc.
  2. Reinforce for students that GAI makes mistakes. Students are ultimately responsible for the accuracy of the work they submit and for not using others’ intellectual property without proper acknowledgment. They should be encouraged to check on the actual existence of any sources cited by a GAI tool because they are sometimes “hallucinated,” not genuine.
  3. Talk to students about the peer review and publication processes and what those mean for source credibility compared to the “scraping” process used to train GAI models.
  4. Explain that GAI is not objective. It can contain bias. It has been created by humans and trained on data primarily produced by humans, which means it can reflect their very real biases.
  5. Communicate that transparency in GAI use is critical. Instructors should be clear with their students about when and how they may use GAI to complete specific assignments. At the same time, one of the best ways instructors can share the importance of transparency and attribution is through modeling it themselves (e.g., an instructor disclosing that they used Copilot to create a case study for their course and modeling how to format the disclosure).
  6. Remind students that even if the specific format varies, the information they are most likely to have to produce for a disclosure/acknowledgment or citation is: a) the name of the tool, b) the specific use of the tool, c) the prompts used, d) the output produced, and e) the date of use.
  7. Finally, encourage students to copy and paste all GAI interaction information, including an entire chat history, into a Word document for your course and to save it for future reference. One advantage of Microsoft Copilot with data protections is that it does not retain chat histories. That’s wonderful from a security perspective, but it makes it impossible to re-create that information once a session has ended. They should also know that even GAI tools that save interactions and use them to train their model are unlikely to re-produce a session even if the same prompt is entered.

Indicating Generative AI Assignment Permissions with the Traffic Light Model (Red Light, Yellow Light, Green Light)

CATL recommends using the red, yellow, and green light approach to clearly label what level of generative AI (GAI) use is permitted for each of your course assignments. The traffic lights will be useful, but students will also need precise written instructions to supplement them on each assignment’s instructions. In general, you should include: a) whether GAI use is permitted, b) what tasks it can (e.g., brainstorming topic ideas) and can’t (e.g., creating text) be used on, c) how it should be cited (if applicable), and d) a rationale for why it can/can’t be used. We have provided brief examples below, but keep in mind that lengthy assignments that involve complex GAI use might require much more detailed instructions of even a page or more. Note that the text in brackets [ ] is designed to provide some examples of words that might go there; you will need to choose and insert your own text.

Red Light Approach: No GAI Use Permitted

A red traffic light illuminated with an “x” symbol.Collaboration with any GAI tool is forbidden for this activity. This assignment’s main goal is to develop your own [e.g., writing, coding] skills. Generative AI tools cannot be used because doing so will not be helpful to your own skill development and confidence in those abilities.

Yellow Light Approach: GAI Use Permitted for Specific Tasks and/or Using Specific Tools

A yellow traffic light illuminated with an “!” symbol.You may use the GAI tool Copilot – and only Copilot – for specific tasks in this assignment, but not for all of them. You may use GAI tools to [brainstorm a research topic], but not for [writing or editing your research proposal]. You will need to properly cite or disclose your generative AI using [e.g., APA Style]. If you are unsure or confused about what GAI use is permitted, please reach out to me.

OR

You may use GAI tools on this assignment to [e.g., create the budget for your grant proposal], but not to do anything else, such as create text, construct your persuasive arguments, or edit your writing. You will need to properly cite or disclose your generative AI using [e.g., APA Style]. Although other tools are permitted, you are strongly encouraged to use Microsoft Copilot with data protections for reasons of security, equity, and access to GBIT technical support.

Green Light Approach: All GAI Use Permitted

A green traffic light illuminated with a checkmark symbol. You are encouraged to use GAI tools for this assignment. Any generative AI use will need to be disclosed and cited using the methods described in your syllabus. For this assignment, you may use GAI tools to [e.g., brainstorm, create questions, text, or code, organize information, build arguments, and edit]. You will need to properly cite or disclose how/where you used generative AI using [e.g., APA Style]. If you would like feedback on your GAI tool use or have questions, please reach out to me.

 

Outlining When and How Students May Use GAI

An instructor may want to outline specific tasks when using the traffic light approach. Consider some of the examples below.

You may use AI to “[task(s)]”, but not to “[task(s)]”:

  • Analyze Data
  • Brainstorm Ideas, Thesis Statements, etc.
  • Build Arguments
  • Conduct Peer Review
  • Create Discussion Posts
  • Create Questions
  • Create Study Guides
  • Develop Thesis Statements
  • Edit Content
  • Format Documents/Presentations
  • Generate Citations
  • Generate New Text, Code, Art, etc.
  • Generate Research Questions
  • Generate Samples/Examples
  • Organize Information
  • Provide Explanations/Definitions
  • Research a Topic
  • Search for Research Articles
  • Summarize Text/Literature/Article
  • Write Self-Reflections

Learn More

Explore even more CATL resources related to AI in education:

If you have questions, concerns, or ideas specific to generative AI tools in education and the classroom, please email us at catl@uwgb.edu or set up a consultation!