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Designing Assignments for Learning

The rapid shift to remote teaching and learning meant that many instructors reimagined their assessment practices. Whether adapting existing assignments or creatively designing new opportunities for their students to learn, instructors focused on helping students make meaning and demonstrate their learning outside of the traditional, face-to-face classroom setting. This resource distills the elements of assignment design that are important to carry forward as we continue to seek better ways of assessing learning and build on our innovative assignment designs.

On this page:

Rethinking traditional tests, quizzes, and exams.

  • Examples from the Columbia University Classroom
  • Tips for Designing Assignments for Learning

Reflect On Your Assignment Design

Connect with the ctl.

  • Resources and References

articles on designing assignments

Cite this resource: Columbia Center for Teaching and Learning (2021). Designing Assignments for Learning. Columbia University. Retrieved [today’s date] from https://ctl.columbia.edu/resources-and-technology/teaching-with-technology/teaching-online/designing-assignments/

Traditional assessments tend to reveal whether students can recognize, recall, or replicate what was learned out of context, and tend to focus on students providing correct responses (Wiggins, 1990). In contrast, authentic assignments, which are course assessments, engage students in higher order thinking, as they grapple with real or simulated challenges that help them prepare for their professional lives, and draw on the course knowledge learned and the skills acquired to create justifiable answers, performances or products (Wiggins, 1990). An authentic assessment provides opportunities for students to practice, consult resources, learn from feedback, and refine their performances and products accordingly (Wiggins 1990, 1998, 2014). 

Authentic assignments ask students to “do” the subject with an audience in mind and apply their learning in a new situation. Examples of authentic assignments include asking students to: 

  • Write for a real audience (e.g., a memo, a policy brief, letter to the editor, a grant proposal, reports, building a website) and/or publication;
  • Solve problem sets that have real world application; 
  • Design projects that address a real world problem; 
  • Engage in a community-partnered research project;
  • Create an exhibit, performance, or conference presentation ;
  • Compile and reflect on their work through a portfolio/e-portfolio.

Noteworthy elements of authentic designs are that instructors scaffold the assignment, and play an active role in preparing students for the tasks assigned, while students are intentionally asked to reflect on the process and product of their work thus building their metacognitive skills (Herrington and Oliver, 2000; Ashford-Rowe, Herrington and Brown, 2013; Frey, Schmitt, and Allen, 2012). 

It’s worth noting here that authentic assessments can initially be time consuming to design, implement, and grade. They are critiqued for being challenging to use across course contexts and for grading reliability issues (Maclellan, 2004). Despite these challenges, authentic assessments are recognized as beneficial to student learning (Svinicki, 2004) as they are learner-centered (Weimer, 2013), promote academic integrity (McLaughlin, L. and Ricevuto, 2021; Sotiriadou et al., 2019; Schroeder, 2021) and motivate students to learn (Ambrose et al., 2010). The Columbia Center for Teaching and Learning is always available to consult with faculty who are considering authentic assessment designs and to discuss challenges and affordances.   

Examples from the Columbia University Classroom 

Columbia instructors have experimented with alternative ways of assessing student learning from oral exams to technology-enhanced assignments. Below are a few examples of authentic assignments in various teaching contexts across Columbia University. 

  • E-portfolios: Statia Cook shares her experiences with an ePorfolio assignment in her co-taught Frontiers of Science course (a submission to the Voices of Hybrid and Online Teaching and Learning initiative); CUIMC use of ePortfolios ;
  • Case studies: Columbia instructors have engaged their students in authentic ways through case studies drawing on the Case Consortium at Columbia University. Read and watch a faculty spotlight to learn how Professor Mary Ann Price uses the case method to place pre-med students in real-life scenarios;
  • Simulations: students at CUIMC engage in simulations to develop their professional skills in The Mary & Michael Jaharis Simulation Center in the Vagelos College of Physicians and Surgeons and the Helene Fuld Health Trust Simulation Center in the Columbia School of Nursing; 
  • Experiential learning: instructors have drawn on New York City as a learning laboratory such as Barnard’s NYC as Lab webpage which highlights courses that engage students in NYC;
  • Design projects that address real world problems: Yevgeniy Yesilevskiy on the Engineering design projects completed using lab kits during remote learning. Watch Dr. Yesilevskiy talk about his teaching and read the Columbia News article . 
  • Writing assignments: Lia Marshall and her teaching associate Aparna Balasundaram reflect on their “non-disposable or renewable assignments” to prepare social work students for their professional lives as they write for a real audience; and Hannah Weaver spoke about a sandbox assignment used in her Core Literature Humanities course at the 2021 Celebration of Teaching and Learning Symposium . Watch Dr. Weaver share her experiences.  

​Tips for Designing Assignments for Learning

While designing an effective authentic assignment may seem like a daunting task, the following tips can be used as a starting point. See the Resources section for frameworks and tools that may be useful in this effort.  

Align the assignment with your course learning objectives 

Identify the kind of thinking that is important in your course, the knowledge students will apply, and the skills they will practice using through the assignment. What kind of thinking will students be asked to do for the assignment? What will students learn by completing this assignment? How will the assignment help students achieve the desired course learning outcomes? For more information on course learning objectives, see the CTL’s Course Design Essentials self-paced course and watch the video on Articulating Learning Objectives .  

Identify an authentic meaning-making task

For meaning-making to occur, students need to understand the relevance of the assignment to the course and beyond (Ambrose et al., 2010). To Bean (2011) a “meaning-making” or “meaning-constructing” task has two dimensions: 1) it presents students with an authentic disciplinary problem or asks students to formulate their own problems, both of which engage them in active critical thinking, and 2) the problem is placed in “a context that gives students a role or purpose, a targeted audience, and a genre.” (Bean, 2011: 97-98). 

An authentic task gives students a realistic challenge to grapple with, a role to take on that allows them to “rehearse for the complex ambiguities” of life, provides resources and supports to draw on, and requires students to justify their work and the process they used to inform their solution (Wiggins, 1990). Note that if students find an assignment interesting or relevant, they will see value in completing it. 

Consider the kind of activities in the real world that use the knowledge and skills that are the focus of your course. How is this knowledge and these skills applied to answer real-world questions to solve real-world problems? (Herrington et al., 2010: 22). What do professionals or academics in your discipline do on a regular basis? What does it mean to think like a biologist, statistician, historian, social scientist? How might your assignment ask students to draw on current events, issues, or problems that relate to the course and are of interest to them? How might your assignment tap into student motivation and engage them in the kinds of thinking they can apply to better understand the world around them? (Ambrose et al., 2010). 

Determine the evaluation criteria and create a rubric

To ensure equitable and consistent grading of assignments across students, make transparent the criteria you will use to evaluate student work. The criteria should focus on the knowledge and skills that are central to the assignment. Build on the criteria identified, create a rubric that makes explicit the expectations of deliverables and share this rubric with your students so they can use it as they work on the assignment. For more information on rubrics, see the CTL’s resource Incorporating Rubrics into Your Grading and Feedback Practices , and explore the Association of American Colleges & Universities VALUE Rubrics (Valid Assessment of Learning in Undergraduate Education). 

Build in metacognition

Ask students to reflect on what and how they learned from the assignment. Help students uncover personal relevance of the assignment, find intrinsic value in their work, and deepen their motivation by asking them to reflect on their process and their assignment deliverable. Sample prompts might include: what did you learn from this assignment? How might you draw on the knowledge and skills you used on this assignment in the future? See Ambrose et al., 2010 for more strategies that support motivation and the CTL’s resource on Metacognition ). 

Provide students with opportunities to practice

Design your assignment to be a learning experience and prepare students for success on the assignment. If students can reasonably expect to be successful on an assignment when they put in the required effort ,with the support and guidance of the instructor, they are more likely to engage in the behaviors necessary for learning (Ambrose et al., 2010). Ensure student success by actively teaching the knowledge and skills of the course (e.g., how to problem solve, how to write for a particular audience), modeling the desired thinking, and creating learning activities that build up to a graded assignment. Provide opportunities for students to practice using the knowledge and skills they will need for the assignment, whether through low-stakes in-class activities or homework activities that include opportunities to receive and incorporate formative feedback. For more information on providing feedback, see the CTL resource Feedback for Learning . 

Communicate about the assignment 

Share the purpose, task, audience, expectations, and criteria for the assignment. Students may have expectations about assessments and how they will be graded that is informed by their prior experiences completing high-stakes assessments, so be transparent. Tell your students why you are asking them to do this assignment, what skills they will be using, how it aligns with the course learning outcomes, and why it is relevant to their learning and their professional lives (i.e., how practitioners / professionals use the knowledge and skills in your course in real world contexts and for what purposes). Finally, verify that students understand what they need to do to complete the assignment. This can be done by asking students to respond to poll questions about different parts of the assignment, a “scavenger hunt” of the assignment instructions–giving students questions to answer about the assignment and having them work in small groups to answer the questions, or by having students share back what they think is expected of them.

Plan to iterate and to keep the focus on learning 

Draw on multiple sources of data to help make decisions about what changes are needed to the assignment, the assignment instructions, and/or rubric to ensure that it contributes to student learning. Explore assignment performance data. As Deandra Little reminds us: “a really good assignment, which is a really good assessment, also teaches you something or tells the instructor something. As much as it tells you what students are learning, it’s also telling you what they aren’t learning.” ( Teaching in Higher Ed podcast episode 337 ). Assignment bottlenecks–where students get stuck or struggle–can be good indicators that students need further support or opportunities to practice prior to completing an assignment. This awareness can inform teaching decisions. 

Triangulate the performance data by collecting student feedback, and noting your own reflections about what worked well and what did not. Revise the assignment instructions, rubric, and teaching practices accordingly. Consider how you might better align your assignment with your course objectives and/or provide more opportunities for students to practice using the knowledge and skills that they will rely on for the assignment. Additionally, keep in mind societal, disciplinary, and technological changes as you tweak your assignments for future use. 

Now is a great time to reflect on your practices and experiences with assignment design and think critically about your approach. Take a closer look at an existing assignment. Questions to consider include: What is this assignment meant to do? What purpose does it serve? Why do you ask students to do this assignment? How are they prepared to complete the assignment? Does the assignment assess the kind of learning that you really want? What would help students learn from this assignment? 

Using the tips in the previous section: How can the assignment be tweaked to be more authentic and meaningful to students? 

As you plan forward for post-pandemic teaching and reflect on your practices and reimagine your course design, you may find the following CTL resources helpful: Reflecting On Your Experiences with Remote Teaching , Transition to In-Person Teaching , and Course Design Support .

The Columbia Center for Teaching and Learning (CTL) is here to help!

For assistance with assignment design, rubric design, or any other teaching and learning need, please request a consultation by emailing [email protected]

Transparency in Learning and Teaching (TILT) framework for assignments. The TILT Examples and Resources page ( https://tilthighered.com/tiltexamplesandresources ) includes example assignments from across disciplines, as well as a transparent assignment template and a checklist for designing transparent assignments . Each emphasizes the importance of articulating to students the purpose of the assignment or activity, the what and how of the task, and specifying the criteria that will be used to assess students. 

Association of American Colleges & Universities (AAC&U) offers VALUE ADD (Assignment Design and Diagnostic) tools ( https://www.aacu.org/value-add-tools ) to help with the creation of clear and effective assignments that align with the desired learning outcomes and associated VALUE rubrics (Valid Assessment of Learning in Undergraduate Education). VALUE ADD encourages instructors to explicitly state assignment information such as the purpose of the assignment, what skills students will be using, how it aligns with course learning outcomes, the assignment type, the audience and context for the assignment, clear evaluation criteria, desired formatting, and expectations for completion whether individual or in a group.

Villarroel et al. (2017) propose a blueprint for building authentic assessments which includes four steps: 1) consider the workplace context, 2) design the authentic assessment; 3) learn and apply standards for judgement; and 4) give feedback. 


Ambrose, S. A., Bridges, M. W., & DiPietro, M. (2010). Chapter 3: What Factors Motivate Students to Learn? In How Learning Works: Seven Research-Based Principles for Smart Teaching . Jossey-Bass. 

Ashford-Rowe, K., Herrington, J., and Brown, C. (2013). Establishing the critical elements that determine authentic assessment. Assessment & Evaluation in Higher Education. 39(2), 205-222, http://dx.doi.org/10.1080/02602938.2013.819566 .  

Bean, J.C. (2011). Engaging Ideas: The Professor’s Guide to Integrating Writing, Critical Thinking, and Active Learning in the Classroom . Second Edition. Jossey-Bass. 

Frey, B. B, Schmitt, V. L., and Allen, J. P. (2012). Defining Authentic Classroom Assessment. Practical Assessment, Research, and Evaluation. 17(2). DOI: https://doi.org/10.7275/sxbs-0829  

Herrington, J., Reeves, T. C., and Oliver, R. (2010). A Guide to Authentic e-Learning . Routledge. 

Herrington, J. and Oliver, R. (2000). An instructional design framework for authentic learning environments. Educational Technology Research and Development, 48(3), 23-48. 

Litchfield, B. C. and Dempsey, J. V. (2015). Authentic Assessment of Knowledge, Skills, and Attitudes. New Directions for Teaching and Learning. 142 (Summer 2015), 65-80. 

Maclellan, E. (2004). How convincing is alternative assessment for use in higher education. Assessment & Evaluation in Higher Education. 29(3), June 2004. DOI: 10.1080/0260293042000188267

McLaughlin, L. and Ricevuto, J. (2021). Assessments in a Virtual Environment: You Won’t Need that Lockdown Browser! Faculty Focus. June 2, 2021. 

Mueller, J. (2005). The Authentic Assessment Toolbox: Enhancing Student Learning through Online Faculty Development . MERLOT Journal of Online Learning and Teaching. 1(1). July 2005. Mueller’s Authentic Assessment Toolbox is available online. 

Schroeder, R. (2021). Vaccinate Against Cheating With Authentic Assessment . Inside Higher Ed. (February 26, 2021).  

Sotiriadou, P., Logan, D., Daly, A., and Guest, R. (2019). The role of authentic assessment to preserve academic integrity and promote skills development and employability. Studies in Higher Education. 45(111), 2132-2148. https://doi.org/10.1080/03075079.2019.1582015    

Stachowiak, B. (Host). (November 25, 2020). Authentic Assignments with Deandra Little. (Episode 337). In Teaching in Higher Ed . https://teachinginhighered.com/podcast/authentic-assignments/  

Svinicki, M. D. (2004). Authentic Assessment: Testing in Reality. New Directions for Teaching and Learning. 100 (Winter 2004): 23-29. 

Villarroel, V., Bloxham, S, Bruna, D., Bruna, C., and Herrera-Seda, C. (2017). Authentic assessment: creating a blueprint for course design. Assessment & Evaluation in Higher Education. 43(5), 840-854. https://doi.org/10.1080/02602938.2017.1412396    

Weimer, M. (2013). Learner-Centered Teaching: Five Key Changes to Practice . Second Edition. San Francisco: Jossey-Bass. 

Wiggins, G. (2014). Authenticity in assessment, (re-)defined and explained. Retrieved from https://grantwiggins.wordpress.com/2014/01/26/authenticity-in-assessment-re-defined-and-explained/

Wiggins, G. (1998). Teaching to the (Authentic) Test. Educational Leadership . April 1989. 41-47. 

Wiggins, Grant (1990). The Case for Authentic Assessment . Practical Assessment, Research & Evaluation , 2(2). 

Wondering how AI tools might play a role in your course assignments?

See the CTL’s resource “Considerations for AI Tools in the Classroom.”

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Creative Ways to Design Assignments for Student Success

articles on designing assignments

There are many creative ways in which teachers can design assignments to support student success. We can do this while simultaneously not getting bogged down with the various obstructions that keep students from both completing and learning from the assignments. For me, assignments fall into two categories: those that are graded automatically, such as SmartBook® readings and quizzes in Connect®; and those that I need to grade by hand, such as writing assignments.  

For those of us teaching large, introductory classes, most of our assignments are graded automatically, which is great for our time management. But our students will ultimately deliver a plethora of colorful excuses as to why they were not completed and why extensions are warranted. How do we give them a little leeway to make the semester run more smoothly, so there are fewer worries about a reading that was missed or a quiz that went by too quickly? Here are a few tactics I use. 

Automatically graded assignments: 

Multiple assignment attempts  

  • This eases the mental pressure of a timed assignment and covers computer mishaps or human error on the first attempt. 
  • You can deduct points for every attempt taken if you are worried about students taking advantage. 

Automatically dropped assignments  

  • Within a subset or set of assignments, automatically drop a few from grading. This can take care of all excuses for missing an assignment. 
  • Additionally, you can give a little grade boost to those who complete all their assignments (over a certain grade). 

Due dates  

  • Consider staggering due dates during the week instead of making them all due on Sunday night.  
  • Set the due date for readings the night before you cover the material, so students are prepared.  


  • If we want our students to read, then make a reading assignment a requirement of a quiz. 

The tactics above might be applied to written assignments, too. An easy way to bolster a student’s interest and investment in these longer assignments is to give them a choice. This could be in the topic, location of study, or presentation style. For example, if you want them to analyze the susceptibility of a beach to hurricane threat, why not let them choose the location? In this way, you will also be gaining a lot of new information for your own use. 

With a small amount of effort, we can design our classes, so students concentrate on learning the subject matter rather than the logistics of completing the assignments. 

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Designing Assignments and Activities with ChatGPT and Generative AI in Mind

Generative AI, such as ChatGPT, can be a powerful tool to engage students in learning and creativity. Essentially, generative AI tools are those that create content on their own without human intervention. It can be useful for writing text, generating ideas, creating images, writing and editing code, and more. By designing assignments that incorporate generative AI technology, instructors can provide students with opportunities to explore, create, and problem-solve. However, as an instructor, you may also want to create assignments that challenge students to demonstrate their own knowledge and skills without relying heavily on AI-generated content. In this article, we will review different assignment ideas and strategies to create prompts and assignment ideas in different disciplines.

Table of Contents

Syllabus statements and student input, is ai use cheating.

  • AI Detection
  • Design Assignments to Limit AI Use
  • Design Assignments to Work with AI
  • Registration
  • Recording from August, 2023
  • Workshop Slides

Intelligent.com conducted a poll of more than 1,000 current college students in May 2023 regarding their use of ChatGPT for coursework. 30% of students used ChatGPT for coursework during the 2022/2023 academic year, and of that group, 46% utilized it frequently. Users of ChatGPT and other generative AI tools like Bing Chat and Google Bard continue to grow with some flattening of the upward trend in summer 2023. Generative AI is rapidly advancing and becoming more prevalent in education, work, and our daily lives. As an educator, it’s a good idea to help students be aware of the ethical considerations surrounding the use of generative AI.

  • Consider adding an acceptable use statement in your syllabus. Here are some guidelines and examples.
  • How do you think generative AI can be applied to the course assignments in this class?
  • Can you share any specific examples of generative AI being used in educational settings?
  • How can we ensure that AI tools are used in a way that promotes skill development in our course?
  • After reviewing the assignment directions and grading information, what would be some helpful uses of AI tools that will still allow you to learn the content and demonstrate your learning?
  • Based on various surveys and instructor experiences, not all students believe it is ethical to use AI on assignments. Be sure to include a discussion/policy about how AI can or cannot be used in group work.

There is no standard for determining if AI use by students qualifies as plagiarism or cheating . There is also no consistent standard for citing or crediting work using an AI tool. It may be useful to check with your professional organizations and journals and share any of their policies with students. Currently, AI is part of retail and other business careers, education in personalized learning, systems that make recommendations, human resources decisions, healthcare, agriculture, gaming, marketing, finance, and more .

Organization and publication examples:

  • RTDNA Journalism Association
  • NIH Grants Peer Review Policy
  • IEEE Journal Submission Policy

Citation Style Guidance:

  • APA: How to Cite ChatGPT
  • MLA: How Do I Cite Generative AI in MLA Style?
  • Chicago Style Manual

It may be useful to reflect on how you define plagiarism and cheating and then help guide students to think about it. Review this image from Matt Miller @DitchThatTextbook to help guide your thinking.

Plagiarism and cheating graphic with a spectrum showing "Bot-Created" to "Student-Created" to help guide teachers in thinking about what counts as plagiarism and what does not.

No True Detection of AI is Possible

There is no “fool-proof” way to detect AI use in student projects, and there have been many stories published about false positives and negatives using various AI detectors.

At NC State University, we provide access to Turnitin, which has an AI detector if you would like to get some input on if students have used AI to craft their writing. That said, do not use Turnitin as sole evidence that a student has cheated or plagiarized. Please review the academic integrity guidance and policies from the Office of Student Conduct. Note that the Division of Academic and Student Affairs also encourages faculty to notify students if they plan on using Turnitin.

  • Turnitin at NC State
  • Turnitin AI Detection
  • Article on AI detection issues with Turnitin

AI detection and AI detector workaround programs are regularly being created and released. Here are some common tools and videos guiding students and content creators on how to get around AI detection.

  • AI Text Classifier by OpenAI
  • AI Content Detector: Writer  
  • AI Writing CheckWriter’s AI Content Detector
  • Video: How to Not Get Caught Using ChatGPT at School
  • Video: New Way to Bypass AI Detection

There are also some red flags you can look for in reviewing student work. It’s helpful (albeit difficult in large classes) if you know your students writing and can determine if an assignment does not fit their typical way or level of writing. What to look for:

  • A factual error or made-up citation
  • Missing required assignment data sources or article text
  • “Too perfect” in terms of grammar and usage
  • Overly formal, detached, or impersonal style/tone
  • Predictable formations – -like a five-paragraph essay from middle school language arts
  • The writing too directly and repetitively parallels the assignment directions

Note: Students who are good at prompt writing and provide context, follow-up questions, a voice for the AI, etc., may not produce writing that exhibits these flaws. You may also want to consider having a conversation with a student about their work and topic if you have concerns. ChatGPT-4 (a paid option) is significantly better at avoiding these style issues, and Bing Chat is powered by GPT-4 (free).

Designing Assignments to Limit AI Usage

There are ways to design assignments that can make generative AI use more difficult for students. However, as tools become more sophisticated, assignment revisions may not be enough to truly prevent students from using AI; however, these strategies are a good start.

Ask ChatGPT

Ask ChatGPT to provide assignment examples in your field that would be difficult for it to complete. Include context, specific learning outcomes, and more to get a more specific list of suggestions. Prompt Example:

  • You are a professor for an introductory course in {subject area} at the college level. You are trying to design assignments that would be tricky for students to use AI to complete. What are some assignment ideas and topics within the field that would be difficult for Bard to complete successfully?
  • You are a professor for a college statistics course. Students are expected to recognize and be able to explain the central role of variability in the field of statistics. They also must be able to find variability when interpreting data. What are some course assignments that students can complete to show they have met these objectives and that are difficult for ChatGPT to complete? Explain how the assignment will help students demonstrate their understanding and what makes it complicated for a generative AI tool like ChatGPT. See the results here!

Require Specific Data Sources to be Used in the Assignment

ChatGPT is not connected to the web. It’s a “pretrained” tool that has not been trained on information post-2021. So, incorporating specific texts into assignments can make things more difficult for ChatGPT. You can ask students to write and cite sources/text from specific articles or videos. You can also provide data sets that students must use in their work.

Google Version History

Require that students submit written work using Google Docs, Slides, Sheets, etc., and use version history to validate that the writing and input occurred over time vs. in large chunks suggesting that students may have copied and pasted from another source like ChatGPT. Students have also used time stamps in Google Docs version history to exonerate themselves from false positives picked up by AI detectors.

Incorporate Student Discussion and Collaboration

In-person student discussions that reference past class activities, readings done outside of class, previous lectures, and so on can be integrated into your course. Examples:

  • Ask students in a chemistry course to compare and contrast two models that they read about for homework or that were shared in a recorded lecture. Ask students to come up with examples in class (or on a discussion board) with a partner based on the reading assignment.
  • Use Perusall and set the auto-grading (ai-assisted) feature to highly weight active engagement time and getting responses. Manually grade and let students know that credit comes from their in-text conversations with each other.

Reflective Assignments

AI tools are not truly reflective and aren’t likely (even fictionally) to make good connections between course content and personal experience or learnings. Examples:

  • Write a reflection on a time when you struggled with a {subject area} concept. What was the concept? How did you eventually understand it? What advice would you give to other students who are struggling with the same concept?
  • Compare and contrast two different ways of solving a problem {in your content area}. What are the advantages and disadvantages of each method? When would you use one method over the other?

Real-World & Localized Connections in Assignments

Some AI tools are not connected to the internet and will not have an understanding of local references or the most recent sources. Others may not be able to draw connections that make sense to humans who understand those “smaller” contexts. For example, we asked Bard to write a short story set in a modern-day context in Raleigh, North Carolina on the NC State Campus and gave it some specific guidelines. In addition to writing a formulaic story , Bard regularly referenced “The Old Well” which is part of the UNC-Chapel Hill campus. Prompt example:

  • Analyze the impact of a recent policy change {content-specific} or ask students to choose a policy change that has been implemented in the last year. Research the policy change and its implications for the economy. Write a report that includes the expected impact, strengths and weaknesses of the change, and recommendations for how the policy change could be improved.

Take Assignments through a Process

Asking students to complete an assignment with a process including steps like brainstorming, mapping, drafting, peer review, an interview, and a final product can make it difficult for them to find successful ways to use AI. It may be able to help students with sections of the assignment but not the entire product or process. You can also ask students process-oriented questions along the way. You can also include ambiguous questions or those that require positions on controversial topics. Examples:

  • Compare your answers to your team’s answers. Discuss any differences.
  • Explain the process you followed to arrive at your conclusion.
  • Analyze the ethical implications of each step in the process and propose alternatives if necessary.
  • Explain the long-term consequences of implementing this process and how they might evolve over time.
  • Discuss the role of creativity and innovation in…
  • Identify potential biases, assumptions, and problems that could arise and suggest methods to mitigate them.

Retrieval Practice Activities

Retrieval practice activities allow students to practice recalling information from class activities, lectures, readings, and so on. If specific to course content, AI would not be helpful in these activities (particularly if completed in person). More on retrieval practice .

Multi-Step with a Creative Component

Create projects in which students demonstrate their learning. Essentially find ways to ask them to take what they’ve learned, organize it, and make something with it. Examples:

  • Short story writing in which students must use content information, specific vocabulary, and maybe even primary sources to craft a story.
  • Ask students to create a comic strip based on a concept, vocabulary, a reading, etc.
  • Students creating a public service announcement video to demonstrate learning

Blended Instruction or Flipping

You may also want to consider using blended or flipping formats for your course in order to limit AI use. In this model, students would learn content outside of class time and then use class time for the application of what they learned.

Designing Assignments to Work with AI

AI tools are likely to be used by students in future careers and likely in their coursework, so one approach is to incorporate the tools directly and intentionally into assignments and activities.

“Am I going to teach students to write or to write with AI tools like ChatGPT? Derek Bruff

Consider these assignment reflection questions from Derek Bruff’s article “Assignment Makeovers in the AI Age.”

  • Why does this assignment make sense for this course?
  • What are the specific learning objectives for this assignment?
  • How might students use AI tools while working on this assignment?
  • How might AI undercut the goals of this assignment? How could you mitigate this?
  • How might AI enhance the assignment? Where would students need help figuring that out?
  • Focus on the process. How could you make the assignment more meaningful for students or support them more in the work?

Consider these ideas for assignments that can work with AI tools:

  • Use AI to generate multiple explanations for a concept and ask students to critique the AI-generated explanations. Ask them to cite/use specific course readings, notes from lectures, etc., in their critiques.
  • Save time in reviewing student writing by asking them or requiring them first to get an AI review of their work, then reflect on the review, make edits, and then submit their final work.
  • Include an AI tool in a “Think-Pair-Share” activity in class. Students pair with another person in class and then with an AI tool.
  • Ask students to predict what responses they will get from AI to specific course content questions, problem sets, etc.
  • Provide several responses from AI and ask students to make a better or different product using those drafts/responses. They might make a mind map from a narrative created by AI and then find three additional sources to support or expand on different sections of the mind map.
  • Assign a peer teaching project in which students will teach a concept or review a concept for their peers. Encourage students to get help from AI with the content and in designing a short activity that can be done as part of the peer teaching. Make students responsible for answering questions from peers and instructors. Use any gaps to adjust your own teaching.
  • Ask students to debate an AI tool — students on one side and ChatGPT on the other.
  • Ask students to find evidence for an AI-created “main points” of an article. First, copy and paste an article into ChatGPT (or a link to an article into Bing or Bard) and ask the tool to summarize the key points of the article. Then provide that to students and ask them to find quotes or details that expand on each point.

NC State Office of Faculty Excellence: Navigating the Landscape of Higher Education in the Age of Artificial Intelligence

Writing Instructors –> Tim Laquintano, Carly Schnitzler, and Annette Vee — TextGenEd: An Introduction to Teaching With Text Generation Technologies (Assignment examples for AI Literacy, Creative Explorations, Ethical Considerations, and more – access at the bottom of the article)

Writing Instructors –> Anna Mills (Curator). AI Text Generators and Teaching Writing: Starting Points For Inquiry

AI Writing Detection: Red Flags

Ethan & Lilach Mollick — Using AI to Implement Effective Teaching Strategies in Classrooms: Five Strategies, Including Prompts

Ethan Mollick — Assigning AI: Seven Ways of Using AI in Class and The Homework Apocalypse  

Jeffrey Young — EdSurge Instructors Rush to Do ‘Assignment Makeovers’ to Respond to ChatGPT” 

Derek Bruff

  • Assignment Makeovers in the AI Age: Essay Edition
  • Assignment Makeovers in the AI Age: Reading Response Edition

Tyler Cowen & Alexander Tabarook How to Learn & Teach Economics with Large Language Models, Including GPT

Sam Lau & Philip Guo Teaching Programming in the Age of ChatGPT – O’Reilly  

AI Prompts for Teaching  

Impact Research: K-12 Teachers & Students ChatGPT Use

Torrey Trust — Essential Considerations for Addressing the Possibility of AI-Driven Cheating, Part 1 | Faculty Focus  

Ideas to Limit AI Use in Assignments from Google Bard  

Educause Review: Artificial Intelligence

An introduction to prompting generative AI like ChatGPT for teaching and learning  

ChatGPT, Chatbots and Artificial Intelligence in Education – Ditch That Textbook

Artificial Intelligence and the Future of Teaching and Learning (PDF)  

Rethinking your Problem Sets in the World of Generative AI – MIT

Hybrid Teaching: Best Practices

Blended Learning | Columbia CTL  

How We Use AI to Enhance Your Writing | Grammarly 

30 AI tools for the classroom – Ditch That Textbook  

College of Education ChatGPT Resources

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Designing Assessments of Student Learning

Image Hollie Nyseth Brehm, ​​​​​Associate Professor, Department of Sociology  Professor Hollie Nyseth Brehm was a graduate student the first time she taught a class, “I didn’t have any training on how to teach, so I assigned a final paper and gave them instructions: ‘Turn it in at the end of course.’ That was sort of it.” Brehm didn’t have a rubric or a process to check in with students along the way. Needless to say, the assignment didn’t lead to any major breakthroughs for her students. But it was a learning experience for Brehm. As she grew her teaching skills, she began to carefully craft assignments to align to course goals, make tasks realistic and meaningful, and break down large assignments into manageable steps. "Now I always have rubrics. … I always scaffold the assignment such that they’ll start by giving me their paper topic and a couple of sources and then turn in a smaller portion of it, and we write it in pieces. And that leads to a much better learning experience for them—and also for me, frankly, when I turn to grade it .”


Have you ever planned a big assignment that didn’t turn out as you’d hoped? What did you learn, and how would you design that assignment differently now? 

What are students learning in your class? Are they meeting your learning outcomes? You simply cannot answer these questions without assessment of some kind.

As educators, we measure student learning through many means, including assignments, quizzes, and tests. These assessments can be formal or informal, graded or ungraded. But assessment is not simply about awarding points and assigning grades. Learning is a process, not a product, and that process takes place during activities such as recall and practice. Assessing skills in varied ways helps you adjust your teaching throughout your course to support student learning

Instructor speaking to student on their laptop

Research tells us that our methods of assessment don’t only measure how much students have learned. They also play an important role in the learning process. A phenomenon known as the “testing effect” suggests students learn more from repeated testing than from repeated exposure to the material they are trying to learn (Karpicke & Roediger, 2008). While exposure to material, such as during lecture or study, helps students store new information, it’s crucial that students actively practice retrieving that information and putting it to use. Frequent assessment throughout a course provides students with the practice opportunities that are essential to learning.

In addition we can’t assume students can transfer what they have practiced in one context to a different context. Successful transfer of learning requires understanding of deep, structural features and patterns that novices to a subject are still developing (Barnett & Ceci, 2002; Bransford & Schwartz, 1999). If we want students to be able to apply their learning in a wide variety of contexts, they must practice what they’re learning in a wide variety of contexts .

Providing a variety of assessment types gives students multiple opportunities to practice and demonstrate learning. One way to categorize the range of assessment options is as formative or summative.

Formative and Summative Assessment

Opportunities not simply to practice, but to receive feedback on that practice, are crucial to learning (Ambrose et al., 2010). Formative assessment facilitates student learning by providing frequent low-stakes practice coupled with immediate and focused feedback. Whether graded or ungraded, formative assessment helps you monitor student progress and guide students to understand which outcomes they’ve mastered, which they need to focus on, and what strategies can support their learning. Formative assessment also informs how you modify your teaching to better meet student needs throughout your course.

Technology Tip

Design quizzes in CarmenCanvas to provide immediate and useful feedback to students based on their answers. Learn more about setting up quizzes in Carmen. 

Summative assessment measures student learning by comparing it to a standard. Usually these types of assessments evaluate a range of skills or overall performance at the end of a unit, module, or course. Unlike formative assessment, they tend to focus more on product than process. These high-stakes experiences are typically graded and should be less frequent (Ambrose et al., 2010).

Using Bloom's Taxonomy

A visual depiction of the Bloom's Taxonomy categories positioned like the layers of a cake. [row 1, at bottom] Remember; Recognizing and recalling facts. [Row 2] Understand: Understanding what the facts mean. [Row 3] Apply: Applying the facts, rules, concepts, and ideas. [Row 4] Analyze: Breaking down information into component parts. [Row 5] Evaluate: Judging the value of information or ideas. [Row 6, at top] Create: Combining parts to make a new whole.

Bloom’s Taxonomy is a common framework for thinking about how students can demonstrate their learning on assessments, as well as for articulating course and lesson learning outcomes .

Benjamin Bloom (alongside collaborators Max Englehart, Edward Furst, Walter Hill, and David Krathwohl) published Taxonomy of Educational Objectives in 1956.   The taxonomy provided a system for categorizing educational goals with the intent of aiding educators with assessment. Commonly known as Bloom’s Taxonomy, the framework has been widely used to guide and define instruction in both K-12 and university settings. The original taxonomy from 1956 included a cognitive domain made up of six categories: Knowledge, Comprehension, Application, Analysis, Synthesis, and Evaluation. The categories after Knowledge were presented as “skills and abilities,” with the understanding that knowledge was the necessary precondition for putting these skills and abilities into practice. 

A revised Bloom's Taxonomy from 2001 updated these six categories to reflect how learners interact with knowledge. In the revised version, students can:  Remember content, Understand ideas, Apply information to new situations, Analyze relationships between ideas, Evaluate information to justify perspectives or decisions, and Create new ideas or original work. In the graphic pictured here, the categories from the revised taxonomy are imagined as the layers of a cake.

Assessing students on a variety of Bloom's categories will give you a better sense of how well they understand your course content. The taxonomy can be a helpful guide to predicting which tasks will be most difficult for students so you can provide extra support where it is needed. It can also be used to craft more transparent assignments and test questions by honing in on the specific skills you want to assess and finding the right language to communicate exactly what you want students to do.  See the Sample Bloom's Verbs in the Examples section below.

Diving deeper into Bloom's Taxonomy

Like most aspects of our lives, activities and assessments in today’s classroom are inextricably linked with technology. In 2008, Andrew Churches extended Bloom’s Taxonomy to address the emerging changes in learning behaviors and opportunities as “technology advances and becomes more ubiquitous.” Consult Bloom’s Digital Taxonomy for ideas on using digital tools to facilitate and assess learning across the six categories of learning.

Did you know that the cognitive domain (commonly referred to simply as Bloom's Taxonomy) was only one of three domains in the original Bloom's Taxonomy (1956)? While it is certainly the most well-known and widely used, the other two domains— psychomotor and affective —may be of interest to some educators. The psychomotor domain relates to physical movement, coordination, and motor skills—it might apply to the performing arts or other courses that involve movement, manipulation of objects, and non-discursive communication like body language. The affective domain pertains to feelings, values, motivations, and attitudes and is used more often in disciplines like medicine, social work, and education, where emotions and values are integral aspects of learning. Explore the full taxonomy in  Three Domains of Learning: Cognitive, Affective, and Psychomotor (Hoque, 2017).

In Practice

Consider the following to make your assessments of student learning effective and meaningful.

Align assignments, quizzes, and tests closely to learning outcomes.

It goes without saying that you want students to achieve the learning outcomes for your course. The testing effect implies, then, that your assessments must help them retrieve the knowledge and practice the skills that are relevant to those outcomes.

Plan assessments that measure specific outcomes for your course. Instead of choosing quizzes and tests that are easy to grade or assignment types common to your discipline, carefully consider what assessments will best help students practice important skills. When assignments and feedback are aligned to learning outcomes, and you share this alignment with students, they have a greater appreciation for your course and develop more effective strategies for study and practice targeted at achieving those outcomes (Wang, et al., 2013).

Student working in a lab.

Provide authentic learning experiences.

Consider how far removed from “the real world” traditional assessments like academic essays, standard textbook problems, and multiple-choice exams feel to students. In contrast, assignments that are authentic resemble real-world tasks. They feel relevant and purposeful, which can increase student motivation and engagement (Fink, 2013). Authentic assignments also help you assess whether students will be able to transfer what they learn into realistic contexts beyond your course.

Integrate assessment opportunities that prepare students to be effective and successful once they graduate, whether as professionals, as global citizens, or in their personal lives.

To design authentic assignments:

  • Choose real-world content . If you want students to be able to apply disciplinary methods, frameworks, and terminology to solve real-world problems after your course, you must have them engage with real-world examples, procedures, and tools during your course. Include actual case studies, documents, data sets, and problems from your field in your assessments.
  • Target a real-world audience . Ask students to direct their work to a tangible reader, listener or viewer, rather than to you. For example, they could write a blog for their peers or create a presentation for a future employer.
  • Use real-world formats . Have students develop content in formats used in professional or real-life discourse. For example, instead of a conventional paper, students could write an email to a colleague or a letter to a government official, develop a project proposal or product pitch for a community-based company, post a how-to video on YouTube, or create an infographic to share on social media.

Simulations, role plays, case studies, portfolios, project-based learning, and service learning are all great avenues to bring authentic assessment into your course.

Make sure assignments are achievable.

Your students juggle coursework from several classes, so it’s important to be conscious of workload. Assign tasks they can realistically handle at a given point in the term. If it takes you three hours to do something, it will likely take your students six hours or more. Choose assignments that assess multiple learning outcomes from your course to keep your grading manageable and your feedback useful (Rayner et al., 2016).

Scaffold assignments so students can develop knowledge and skills over time.

For large assignments, use scaffolding to integrate multiple opportunities for feedback, reflection, and improvement. Scaffolding means breaking a complex assignment down into component parts or smaller progressive tasks over time. Practicing these smaller tasks individually before attempting to integrate them into a completed assignment supports student learning by reducing the amount of information they need to process at a given time (Salden et al., 2006).

Scaffolding ensures students will start earlier and spend more time on big assignments. And it provides you more opportunities to give feedback and guidance to support their ultimate success. Additionally, scaffolding can draw students’ attention to important steps in a process that are often overlooked, such as planning and revision, leading them to be more independent and thoughtful about future work.

A familiar example of scaffolding is a research paper. You might ask students to submit a topic or thesis in Week 3 of the semester, an annotated bibliography of sources in Week 6, a detailed outline in Week 9, a first draft on which they can get peer feedback in Week 11, and the final draft in the last week of the semester.

Your course journey is decided in part by how you sequence assignments. Consider where students are in their learning and place assignments at strategic points throughout the term. Scaffold across the course journey by explaining how each assignment builds upon the learning achieved in previous ones (Walvoord & Anderson, 2011). 

Be transparent about assignment instructions and expectations. 

Communicate clearly to students about the purpose of each assignment, the process for completing the task, and the criteria you will use to evaluate it before they begin the work. Studies have shown that transparent assignments support students to meet learning goals and result in especially large increases in success and confidence for underserved students (Winkelmes et al., 2016).

To increase assignment transparency:

Instructor giving directions to a class.

  • Explain how the assignment links to one or more course learning outcomes . Understanding why the assignment matters and how it supports their learning can increase student motivation and investment in the work.
  • Outline steps of the task in the assignment prompt . Clear directions help students structure their time and effort. This is also a chance to call out disciplinary standards with which students are not yet familiar or guide them to focus on steps of the process they often neglect, such as initial research.
  • Provide a rubric with straightforward evaluation criteria . Rubrics make transparent which parts of an assignment you care most about. Sharing clear criteria sets students up for success by giving them the tools to self-evaluate and revise their work before submitting it. Be sure to explain your rubric, and particularly to unpack new or vague terms; for example, language like "argue," “close reading,” "list significant findings," and "document" can mean different things in different disciplines. It is helpful to show exemplars and non-exemplars along with your rubric to highlight differences in unacceptable, acceptable, and exceptional work.

Engage students in reflection or discussion to increase assignment transparency. Have them consider how the assessed outcomes connect to their personal lives or future careers. In-class activities that ask them to grade sample assignments and discuss the criteria they used, compare exemplars and non-exemplars, engage in self- or peer-evaluation, or complete steps of the assignment when you are present to give feedback can all support student success.

Technology Tip   

Enter all  assignments and due dates  in your Carmen course to increase transparency. When assignments are entered in Carmen, they also populate to Calendar, Syllabus, and Grades areas so students can easily track their upcoming work. Carmen also allows you to  develop rubrics  for every assignment in your course. 

Sample Bloom’s Verbs

Building a question bank, using the transparent assignment template, sample assignment: ai-generated lesson plan.

Include frequent low-stakes assignments and assessments throughout your course to provide the opportunities for practice and feedback that are essential to learning. Consider a variety of formative and summative assessment types so students can demonstrate learning in multiple ways. Use Bloom’s Taxonomy to determine—and communicate—the specific skills you want to assess.

Remember that effective assessments of student learning are:

  • Aligned to course learning outcomes
  • Authentic, or resembling real-world tasks
  • Achievable and realistic
  • Scaffolded so students can develop knowledge and skills over time
  • Transparent in purpose, tasks, and criteria for evaluation
  • Collaborative learning techniques: A handbook for college faculty (book)
  • Cheating Lessons (book)
  • Minds online: Teaching effectively with technology (book)
  • Assessment: The Silent Killer of Learning (video)
  • TILT Higher Ed Examples and Resource (website)
  • Writing to Learn: Critical Thinking Activities for Any Classroom (guide)

Ambrose, S.A., Bridges, M.W., Lovett, M.C., DiPietro, M., & Norman, M.K. (2010).  How learning works: Seven research-based principles for smart teaching . John Wiley & Sons. 

Barnett, S.M., & Ceci, S.J. (2002). When and where do we apply what we learn? A taxonomy for far transfer.  Psychological Bulletin , 128 (4). 612–637.  doi.org/10.1037/0033-2909.128.4.612  

Bransford, J.D, & Schwartz, D.L. (1999). Rethinking transfer: A simple proposal with multiple implications.  Review of Research in Education , 24 . 61–100.  doi.org/10.3102/0091732X024001061  

Fink, L. D. (2013).  Creating significant learning experiences: An integrated approach to designing college courses . John Wiley & Sons. 

Karpicke, J.D., & Roediger, H.L., III. (2008). The critical importance of retrieval for learning.  Science ,  319 . 966–968.  doi.org/10.1126/science.1152408  

Rayner, K., Schotter, E. R., Masson, M. E., Potter, M. C., & Treiman, R. (2016). So much to read, so little time: How do we read, and can speed reading help?.  Psychological Science in the Public Interest ,  17 (1), 4-34.  doi.org/10.1177/1529100615623267     

Salden, R.J.C.M., Paas, F., van Merriënboer, J.J.G. (2006). A comparison of approaches to learning task selection in the training of complex cognitive skills.  Computers in Human Behavior , 22 (3). 321–333.  doi.org/10.1016/j.chb.2004.06.003  

Walvoord, B. E., & Anderson, V. J. (2010).  Effective grading: A tool for learning and assessment in college . John Wiley & Sons. 

Wang, X., Su, Y., Cheung, S., Wong, E., & Kwong, T. (2013). An exploration of Biggs’ constructive alignment in course design and its impact on students’ learning approaches.  Assessment & Evaluation in Higher Education , 38 (4). 477–491.  doi.org/10.1016/j.chb.2004.06.003  

Winkelmes, M., Bernacki, M., Butler, J., Zochowski, M., Golanics, J., & Weavil, K.H. (2016). A teaching intervention that increases underserved college students’ success.  Peer Review , 18 (1/2). 31–36. Retrieved from  https://www.aacu.org/peerreview/2016/winter-spring/Winkelmes

Related Teaching Topics

A positive approach to academic integrity, creating and adapting assignments for online courses, ai teaching strategies: transparent assignment design, designing research or inquiry-based assignments, using backward design to plan your course, universal design for learning: planning with all students in mind, search for resources.

Duke Learning Innovation and Lifetime Education

Artificial Intelligence and Assignment Design

Generative ai assignments.

There are both academic and practical reasons you may choose to incorporate generative AI assignments into your course. For example, you may believe that AI will be a skill needed in the students’ future careers in your field. Perhaps you see AI as a tool to help students deepen their understanding of and engagement with your content. You may see the introduction of AI into your classroom as a way to open a conversation about its ethical and academic implications. Integrating AI ironically allows instructors to think deeply about how to design assignments that cannot be easily generated by AI alone to deter plagiarism and cheating. This guide comes from the perspective that you are open to developing AI assignments.

Note, it is critical to develop AI policies for your course along with policies for specific AI assignments.

Considerations for Developing an AI Assignment

Alignment with your course goals.

In the development of AI assignments, the primary consideration is whether the use of AI will help your students achieve the learning goals of the course. Ask yourself, does this assignment help student gain skills and knowledge central to your course and field? Furthermore, consider whether the assignment is engaging enough to warrant incorporating AI. Are you asking students to go above and beyond the AI-generated content? An impactful assignment will challenge students to transform, expand upon, correct, or critique the information and content generated by AI or learning about themselves in relationship to AI. Educational pedagogy expert Derek Bruff gives further insight into how to think about AI assignments as they relate to course design in his blog post about AI and writing assignments .

Guidelines for Use

If you integrate AI into your assignments, be sure to discuss your expectations with your students. It is essential that they understand why you have decided to allow AI in the course and its role in their learning. Furthermore, students can be engaged in wider conversations about AI and its personal impact on their lives. The University of Calgary has developed a set of recommendations of how to start these conversations. One strategy is writing a code of conduct that emphasizes critical thinking and sets guardrails of proper use. You can provide a prewritten list of guidelines or work with the students from scratch by posing questions about AI and learning.

For example, the class may have guidelines such as:

  • We will only use AI to help our intellectual development, not replace it.
  • We will be transparent in our use of AI.
  • We will not submit AI generated text without attribution.
  • We will follow guidelines of when AI is appropriate to use.

Assignment Mechanics

Detailed instructions for an AI assignment will raise the chances for a successful learning experience. Students are not familiar with the processes of this novel type of intellectual work, and thinking through the different facets of the activity will help you to execute and evaluate the assignment confidently. Consider the following questions:

  • Are you allowing ample time to complete the assignment considering it is a new tool for students?
  • Is it better to do the assignment together in class or out of class?
  • Have you practiced using the technology together?
  • How should AI be cited? Are there specific steps for showing how the original AI text is changed?
  • What kind of prompts are allowed? What functions can AI be used for?
  • How will you provide feedback on their use of AI?

AI Literacy

Both you and your students should have a level playing field when it comes to understanding generative AI. You cannot count on students to understand the pitfalls and limitations of AI or even how to use the tools. There are existing resources on AI literacy developed specifically for students that can be a starting point. This library guide from the University of Arizona instructs students on AI, plus there is a companion guide for instructors as well.

Ethical Concerns

There are ethical issues to using AI beyond questions of plagiarism, copyright and academic integrity that should be considered. First, to minimize threats to the privacy of your students and yourself, personal information should not be shared. To dive deeper into privacy concerns, speak with students about the implications of AI services using our data to train their tools.

Second, students may not have equal access to the internet or sufficient funds for subscriptions to AI tools. Be sure to suggest several different AI tools and confirm that students are able to access at least one tool without paying for it. Not all students may take to generative AI equally and will not have the skills to architect effective prompts for your discipline or type of assignment. You can support them by modeling prompt generation or forming groups in class that can work together with AI.

Finally, for instructors who do allow AI for learning, there should be considerations for students who do not want to use it on ethical grounds. This could be solved by making AI assignments low-stakes or optional.

Types of Generative AI Assignments

Below are some general ideas of how to incorporate AI into your course. We encourage you to seek out examples from your discipline or related to the core skills of your course. Some resources worth exploring are ChatGPT asssignments to use in your classroom today (an open source book from the University of Central Florida) and a publication on coding and generative AI by an international group of computer science instructors. Instructors may also wish to leverage generative AI to help with routine tasks and lesson planning .

Brainstorming Ideas and Defining Concepts

Generative AI excels at summarizing content and explaining concepts. Warning to students, it is not necessarily 100% correct!

  • Users can ask AI to brainstorm research questions. “What are some examples of bank failures due to fractional reserve banking ?” Or, “What are some of the major events of the Cold War?”
  • Users can ask AI for clarification of concepts or terms they don’t understand.  “Explain fractional reserve banking in simple terms. ” Or, “What are the Federalist papers and why are they important?”
  • Instructors can ask for resources or ideas of how to teach students content.   “Provide an explanation of fractional reserve banking that discusses the pros and cons of its use .” Or, “What are some exercises to do in the classroom to teach the lifecycle of a butterfly?”

Writing Assistance

While it is possible to use generative AI to correct an entire essay, students can be instructed to prompt AI to provide limited feedback on specific aspects of their writing. Prompts could be limited in scope. For example, students can ask AI to:

  • Rate the clarity of an argument “How well did I explain X? ” Or, “Does this writing contain all of the standard sections of a case study ?”
  • Suggest alternatives “Rewrite the conclusion to better summarize the content.” Or, “What is another way to explain this idea?”
  • Comment on writing mechanics “Review the sentence structure in this essay.” Or, “Check this essay for passive voice.”
  • Provide advice for improvement “List the common grammar mistakes in the essay and provide an explanation of the errors.” Or, “How can I make this writing more upbeat?”

Collaborative Writing

One popular assignment helps instructors show why writing for yourself is important intellectual work. Students read an AI-generated essay and grade it with a rubric. As a class the students discuss its strengths and weaknesses. As a follow-up students can submit a revised essay. In one Yale course, the instructor told students to ask ChatGPT to write its own version of a writing prompt after the students had completed an assignment so they could compare their writing against it.

Another approach to collaboration is to ask AI to write a first draft of an assignment. Students then improve it by doing independent research to double-check the AI content and refining (or rejecting) the AI arguments. Students should record both the questions they asked and the generated text. Students can also be asked to write summaries describing what they learned from the AI search and what they changed. The SPACE framework is a powerful model for organizing these types of writing assignments; the article details the cycle of prompting AI, evaluating its output, and rewriting AI generated content.

Arguably, the greatest strength of generative AI tools may be their ability to write code. Computer scientists are especially concerned about assignments in entry-level programming classes. The way coding is taught may change over time due to AI, but there are short-term strategies that incorporate AI but demand student input. 

  • AI could be asked to generate small snippets of code that students integrate into a larger programming project. Students test, debug and refine the code.
  • After completing a coding assignment, students prompt AI to write a different implementation of the problem and analyze which is more efficient and why.
  • Instructors or students write faulty code and use ChatGPT to generate test cases and/or to fix the errors. 
  • Instructors take advantage of AI to generate more coding assignments and review questions for exams.

Two researchers from UC San Diego published the findings of a study about the attitudes of computer scientists to generative AI and possible directions for teaching coding in the future.

ChatGPT and other generative AI tools do not produce expository content only. They are also able to generate content in many creative genres, often with laughable results ( “Write a pop song in the style of Shakespeare” ) The breadth of the kinds of writing generative AI can mimic might provide the chance for humans to use generative AI to spark creativity in themselves. Student might ask AI to describe the life in the Middle Ages from the perspective of a midwife as inspiration to write a modern version, or as background information for writing in another genre. Generative AI can help instructors deliver content in new ways, for example introducing games into teaching. Instructors might ask AI to develop trivia questions for exam review or a game of 20 questions as an in-class activity.

Generative AI can be a coach for learning that supports both instructors and students. Students can easily get more information about what they don’t understand. AI can be an agent for adaptive learning allowing students to “pass” certain learning objectives and get additional practice on concepts and skills they haven’t mastered. By the same token, it can assist instructors who need to provide additional assistance to students and are pressed for time to find resources. Instructors can get ideas for teaching a skill or subject with activity descriptions and lesson plans. AI can generate practice problems or review questions for exam prep, which frees up time for instructors for other class prep.

There are also positive gains in equity when generative AI is used in a tutoring setting. A neurodiverse student may find conversations with a bot to be non-judgmental and less stressful when needing help. Non-native speakers can ask for word and concept definitions to level up their understanding of course content and context. The review and tutoring capabilities of AI can help all students to practice concepts and receive feedback on their progress.

Looking Ahead

Incorporating generative AI into education is not without peril. Students’ reliance on AI content could potentially lead to losing skills in academic writing. There is the risk that students might mistakenly believe that AI is inherently better at developing ideas and expressing information; leaving students uncomfortable adding their own voice to writing. Without training on how to check the validity of AI content and conduct independent research, students may miss out on how to evaluate sources and compare ideas.

Like it or not, at this moment it lands on educators to design courses and assignments to mitigate these risks and to have hard and timely conversations with students. It may feel like AI is encroaching on teaching and learning, but we should remember that there are many aspects of teaching that are as important as delivering content. These are skills that only human instructors can perform, such as

  • Providing real-time feedback on complex tasks
  • Grading or producing subjective or substantive work
  • Providing social or emotional support 
  • Teaching complex, interconnected concepts
  • Engaging in personal interactions

The future of teaching may increasingly focus on those skills that our students need to make sense of their world, engage with others productively and make connections across disciplines and concepts.

General Resources for AI Assignments

A Teacher’s Guide to Prompt ChatGPT , Andrew Herft

AI in the Classroom , UC Riverside

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Designing assignments.

Making a few revisions to your writing assignments can make a big difference in the writing your students will produce. The most effective changes involve specifying what you would like students to do in the assignment and suggesting concrete steps students can take to achieve that goal.

Clarify what you want your students to do…and why they’re doing it

Kerry Walk, former director of the Princeton Writing Program, offers these principles to consider when designing a writing assignment (condensed and adapted from the original): “At least one sentence on your assignment sheet should explicitly state what you want students to do. The assignment is usually signaled by a verb, such as “analyze,” “assess,” “explain,” or “discuss.” For example, in a history course, after reading a model biography, students were directed as follows: ‘Your assignment is to write your own biographical essay on Mao, using Mao’s reminiscences (as told to a Western journalist), speeches, encyclopedia articles, a medical account from Mao’s physician, and two contradictory obituaries.’ In addition, including a purpose for the assignment can provide crucial focus and guidance. Explaining to students why they’re doing a particular assignment can help them grasp the big picture—what you’re trying to teach them and why learning it is worthwhile. For example, ‘This assignment has three goals: for you to (1) see how the concepts we’ve learned thus far can be used in a different field from economics, (2) learn how to write about a model, and (3) learn to critique a model or how to defend one.’”

Link course writing goals to assignments

Students are more likely to understand what you are asking them to do if the assignment re-uses language that you’ve already introduced in class discussions, in writing activities, or in your Writing Guide. In the assignment below, Yale professor Dorlores Hayden uses writing terms that have been introduced in class:

Choose your home town or any other town or city you have lived in for at least a year. Based upon the readings on the history of transportation, discuss how well or how poorly pedestrian, horse-drawn, steam- powered, and electric transportation might have served your town or city before the gasoline automobile. (If you live in a twentieth-century automobile-oriented suburb, consider rural transportation patterns before the car and the suburban houses.) How did topography affect transportation choices? How did transportation choices affect the local economy and the built environment? Length, 1000 words (4 typed pages plus a plan of the place and/or a photograph). Be sure to argue a strong thesis and back it up with quotations from the readings as well as your own analysis of the plan or photograph.

Give students methods for approaching their work

Strong writing assignments not only identify a clear writing task, they often provide suggestions for how students might begin to accomplish the task. In order to avoid overloading students with information and suggestions, it is often useful to separate the assignment prompt and the advice for approaching the assignment. Below is an example of this strategy from one of Yale’s English 114 sections:

Assignment: In the essays we have read so far, a debate has emerged over what constitutes cosmopolitan practice , loosely defined as concrete actions motivated by a cosmopolitan philosophy or perspective. Using these readings as evidence, write a 5-6-page essay in which you make an argument for your own definition of effective cosmopolitan practice.

Method: In order to develop this essay, you must engage in a critical conversation with the essays we have read in class. In creating your definition of cosmopolitan practice, you will necessarily draw upon the ideas of these authors. You must show how you are building upon, altering, or working in opposition to their ideas and definitions through your quotation and analysis of their concepts and evidence.

Questions to consider:  These questions are designed to prompt your thinking. You do not need to address all these questions in the body of your essay; instead, refer to any of these issues only as they support your ideas.

  • How would you define cosmopolitan practice? How does your definition draw upon or conflict with the definitions offered by the authors we have read so far?
  • What are the strengths of your definition of cosmopolitan practice? What problems does it address? How do the essays we have read support those strengths? How do those strengths address weaknesses in other writers’ arguments?
  • What are the limitations or problems with your definition? How would the authors we have read critique your definition? How would you respond to those critiques?

Case Study: A Sample Writing Assignment and Revision

A student responding to the following assignment felt totally at sea, with good reason:

Write an essay describing the various conceptions of property found in your readings and the different arguments for and against the distribution of property and the various justifications of, and attacks on, ownership. Which of these arguments has any merits? What is the role of property in the various political systems discussed? The essay should concentrate on Hobbes, Locke, and Marx.

“How am I supposed to structure the essay?” the student asked. “Address the first question, comparing the three guys? Address the second question, doing the same, etc.? … Do I talk about each author separately in terms of their conceptions of the nation, and then have a section that compares their arguments, or do I have a 4 part essay which is really 4 essays (two pages each) answering each question? What am I going to put in the intro, and the conclusion?” Given the tangle of ideas presented in the assignment, the student’s panic and confusion are understandable.

A better-formulated assignment poses significant challenges, but one of them is not wondering what the instructor secretly wants. Here’s a possible revision, which follows the guidelines suggested above:

[Course Name and Title]

[Instructor’s Name]

Due date: Thursday, February 24, at 11:10am in section

Length: 5-6pp. double-spaced

Limiting your reading to the sourcebook, write a comparative analysis of Hobbes’s, Locke’s, and Marx’s conceptions of property.

The purpose of this assignment is to help you synthesize some difficult political theory and identify the profound differences among some key theorists.

The best papers will focus on a single shared aspect of the theorists’ respective political ideologies, such as how property is distributed, whether it should be owned, or what role it serves politically. The best papers will not only focus on a specific topic, but will state a clear and arguable thesis about it (“the three authors have differing conceptions of property” is neither) and go on to describe and assess the authors’ viewpoints clearly and concisely.

Note that this revised assignment is now not only clearer than the original; it also requires less regurgitation and more sustained thought.

For more information about crafting and staging your assignments, see “ The Papers We Want to Read ” by Linda Simon, Social Studies; Jan/Feb90, Vol. 81 Issue 1, p37, 3p. (The link to Simon’s article will only work if your computer is on the Yale campus.) See also the discussion of Revising Assignments in the section of this website on Addressing Plagiarism .


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Designing Engaging Assignments

Three tips for coming up with work that sparks real engagement in your students.

Teacher helping a group of teenage students in the classroom.

As I contemplated the writing assignment for our required reading of The Odyssey , I was filled with dread at the thought of reading 120 uninspired essays in which my ninth graders would dutifully recount details from the epic. Essay grading is tedious work, and I’m convinced that the hours we spend grading papers are the least effective way to positively impact students.

So I considered a framing question from the first edition of Grant Wiggins and Jay McTighe’s Understanding by Design : “To what extent does the idea, topic, or process represent a ‘big idea’ having enduring value beyond the classroom?”

I decided that the enduring value students should get from the assignment was not directly about The Odyssey at all—I wanted them to recognize how the literary archetypes we had studied in connection with the epic are found in other stories. From there, I wanted them to realize that understanding how archetypes work could help them form a deeper appreciation of the people around them.

I challenged myself with the questions: “How do I design a meaningful assignment around this big idea that engages students?” and “How do I get kids to understand the value of the work we do?”

Designing an Engaging Assignment

1. Provide choices: According to teacher and blogger  Larry Ferlazzo , “Teachers in the real world recognize that although personalization has the potential to improve learning, our first job in applying any approach is to engage students in the learning process.... It’s about helping students find their spark and make their own fire.” 

In order to ignite students’ interest, I asked them to identify a familiar archetype from a book, movie, or television show of their choice. They would then determine how the character breaks the archetypal mold, and write a response to analyze and explain their findings.

The students were initially skeptical and asked questions like, “So we’re just doing this to brainstorm, and then we’re writing our papers about The Odyssey , right?” I had to work to convince them that they really could write the whole paper about a story of their choosing.

The students’ ability to make their own choices generated an explosion of ideas. A few of my Harry Potter aficionados stopped in excitedly at lunch to share their insights, and there were heated discussions about the myriad ways in which Harry Potter breaks the mold of the hero archetype: “He can’t make a rational decision, so Hermione has to tell him what to do!”

A student who used to love The Magic School Bus series questioned Miss Frizzle’s role as a “teacher as a heroic figure”: “Doesn’t she actually repeatedly risk students’ lives?”

When we paid closer attention to television and films, students noticed a high school chemistry teacher who cooked meth, and how Superman, in the movie Man of Steel , was bullied as a child and felt isolated as he tried to suppress his powers.

2. Offer a challenge: Students persevere when an assignment is not only interesting, but intellectually demanding. When John Hattie lists factors that relate to student achievement, he indicates the importance of ensuring that each assignment has the optimal level of challenge—not too hard, not too easy—because “the effect size of this so-called ‘ Goldilocks ’ level of challenge... nearly doubles the speed of learning.”

Our study of archetypes introduced students to a concept they hadn’t before considered. They were generally able to identify a character’s archetype, but struggled to articulate the specific ways their character broke the mold.

One student was perplexed by the conflicting realities about Betty Cooper, the “good girl” from the series Riverdale . She ultimately realized that despite Betty’s good intentions, her impulsive and often careless behavior reveals that she is much more complex than her blond hair and seemingly perfect exterior lead viewers to expect.

3. Tell them why: According to best-selling author Daniel Pink , “research has shown that people do better at a task—whether that task is spelling, hitting a curveball, or playing the viola—if they know why they’re doing it in the first place. School is often all about how—here’s how you do a quadratic equation, here’s how you write a five-paragraph essay, here’s how you do a paper chromatography experiment in chemistry. The fact is, we often give short shrift to why.”

I explained to students that people, like archetypal characters, are complex. One purpose of studying archetypes is to remind us that our tendency to generalize and stereotype is often incorrect. We may think, “Oh, that person is a jock—he couldn’t possibly be good at math” or “She’s so pretty and popular—life must be so easy for her.”

Noticing how superheroes suffer with feelings of isolation and so-called good girls really aren’t so perfect after all reinforces the fact that we cheat ourselves and others when we label people based on appearances and false assumptions. My students know that we studied literary archetypes to gain a deeper understanding of why we must not judge people based on appearances.

Preparing Students for the Real World

Classroom assignments should provide students with the opportunity to make informed choices, struggle through challenging tasks, and draw their own conclusions so they can transfer these essential skills to their lives outside of school. We can foster our students’ independence in these areas by designing assignments that matter.

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(Re-)Designing Assignments where Students Collaborate with Artificial Intelligence

To help you prepare for teaching and learning in the age of artificial intelligence (AI), here is a pedagogical approach, plus some extra resources, to help you tackle assignment design. These ideas aim to help you reinforce your learning goals and prepare students for the future, which I believe, involves collaboration with AI.

Why Collaborate with AI?

I heard at a conference that AI should actually stand for Assisted or Augmented Intelligence, which I think makes more sense. AI is certainly powerful, but it requires, at minimum, prompting to start a task and a lot of humans to program it in the first place. Below is one illustration of tasks where the AI and human contributions may vary. Human contribution is most useful for multi-dimensional, complex tasks with unexpected events, but much of our everyday work can be handled by technology.

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AI is infiltrating everyday life, almost every profession, and the academic fields of the future. In business, marketers are using AI to write content and format it for specific consumer goals. In engineering, coders are using AI to generate initial code and get feedback to expedite their creative processes. Researchers of various fields are using AI to run simulations, analyze data, and polish up papers.

Now, AI is embedded in many tools we commonly use on our campus, including those supported by WashU (Microsoft Word, Gmail, Padlet, Gradescope, etc.). Most of us will use these tools without knowing it, and many of us will deliberately seek out these tools to help complete a task, solve a problem, begin brainstorming, reduce administrative work, get help, and more. Hence, using AI is not only inevitable, but valued in most aspects of life. You can learn more at Inside Higher Ed Emerging Technology and AI report (June 2023) .

Knowing how to use AI well for specific goals will certainly play a role in the future workplace. To prepare students, we need to recognize AI’s role and, if possible, leverage it so that it enhances teaching and learning. This means a high level of both human + AI contribution in a way that helps both students and technologies grow. At times, this may mean that you keep AI contribution low so that students build essential skills, and at other times you keep human contribution low so that AI can flourish in what it does best and humans can learn how to use it effectively. The method below will help you consider how to create with AI as depicted in the top right quadrant in this figure:

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Spectrum Method

The spectrum method considers human to AI collaboration as a range to which students and AI can contribute to different degrees to individual assignments . For instance, consider what contribution may look like for an essay assignment either created completely by AI or by a student:

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In between the extremes of only AI or only human contribution are forms that will likely become more common, particularly human essay with AI review , which has been generally accepted as tools like Grammarly have come on the scene to help students check essays for grammar and style improvements. Even AI-generated essays with human refining could become commonplace in specific contexts where it makes sense to have AI generate a generic idea or general framework for humans to then tweak (think: anything that may already have a template or standard language, e.g., emails, contracts, slogans).

articles on designing assignments

The value of outlining these different types along the spectrum is that it helps you see where AI may contribute and decide what level of student and AI contribution makes sense. In my workshops, I ask participants to copy/modify a table or create their own using AI prompts, all provided in this open, collaborative document that I invite you to contribute to.

Short link: https:// tinyurl.com/HumanAISpectrum

In the collaborative document, you can also find additional tables for different assignments from coding to independent research. Feel free to copy and build on these tables.

Once you have a table that clearly outlines where humans and AI can contribute, it can also serve as a communication tool specifying what the boundaries are for your class assignment. See the figure below for an example where human essay with AI review is allowed, but additional AI contribution is not permitted (i.e., students can use Grammarly or similar tools).

articles on designing assignments

No matter what you choose, I suggest walking students through the table, your boundary, and specifying why that boundary is drawn where it is. There are great reasons from “no AI is allowed in this intro language course so that you learn to construct grammar yourself” to “it’s not a problem to use AI to generate code, but you need to adjust it to fit into the rest of the code that a company may be using.”

Use this chance to reinforce your learning objectives for students and connect the assignment to a larger why. Here are some questions that you can consider as you go through the process of modifying/building your table and drawing your boundary:

  • What does the future of work look like in your discipline? How will your academic and professional contexts leverage AI to enhance its work?
  • Where will students need to know how to use AI (and how to not use AI) to do their work?
  • Where does your class fit into students’ academic experience? What human-specific skills should they learn now? What AI-related skills should they learn now?
  • What are your learning goals for students? What should they be able to do/know on their own and thus need to practice/apply? What should they be able to do/know with the help of AI?

Extra Resources

Want some more basic information about generative AI tools, particularly ChatGPT? See our CTL resources with recommendations for ChatGPT and AI Composition Tools or Incorporating ChatGPT into Your Assignments . You can also contact us to discuss your teaching goals or learn more at [email protected] .

Don’t like this spectrum approach, particularly for assessments? Consider the two-lane method from Professor Danny Kim at the University of Sydney, which balances AI contribution with student assessments within a controlled environment . You can learn more about this method in a session on Harnessing the Power of AI: Transforming Assignments and Assessments in Higher Education ( YouTube video ), where I got the wonderful images in the “Why Collaborate with AI?” section above.

Learn more about how AI will be used by your students in academic and professional contexts. Current and future usage in various disciplines range from political campaigns writing emails to a changing landscape of software development . Students may even be recruited by companies that provide ChatGPT Plus as a work perk. Talk with your colleagues (and students!) about what they see in your field. These approaches work best in conjunction with wider departmental discussions about how students should (or should not) be collaborating with AI as they move from introductory to advanced courses in your discipline. Perhaps boundaries move up or down to align with AI collaboration skills essential to their future as professionals, scholars, and citizens of the world.

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Instructional Design: Designing Courses and Assignments That Promote Deep Understanding of Essential Concepts

  • September 18, 2008
  • Jayne Zanglein

Our college is in the midst of a curricular project that aims to transform courses so that they promote a deeper understanding of core concepts through carefully designed assignments. The college hired Grant Wiggins, co-author of Understanding by Design (Jay McTighe and Grant Wiggins) to assist faculty in making these changes, and I’d like to report on my experiences redesigning a course I teach called The Legal Environment of Business.

As a new teacher of this course, I was dismayed when I discovered that virtually all of the texts condense three years of law school courses into a one-semester course. It seemed to me that this was an unrealistic amount of material to teach undergraduate business students. I opted to redesign the course to promote deeper understanding of a few topics. But which topics should be covered?

The hardest part of transforming the course involved determining its core concepts, what Wiggins and Tighe call the essential questions. A colleague recommended I think about it this way: “If a student visited you five years after graduation and said ‘I remember your class! I learned that _______’, how would you like the student to fill in the blank?” After many revisions, I decided on four questions that serve as the foundation of my course:

1.Why does the government regulate certain activities?

2.Who are the stakeholders involved in governmental policy-making, and from what power base are they operating?

3.How is governmental regulation enforced?

4.To what extent do laws and judicial opinions interpreting laws reflect the policy underlying governmental regulation?

With Dr. Wiggins help, I created assignments that allow students to wrestle with the answers to the essential questions and develop a deep understanding of contemporary legal issues. Instead of taking the three-years-of-law-school-in-one-semester approach, I helped students master the essentials of legal methods (how to read a court case, the trial process, the court system, judicial and statutory interpretation, and basic legal research) and then let the students do a group research project, which, by its very structure, would require them to answer the essential questions. It worked like this.

Students could choose from one of seven pre-selected research topics based on bills currently pending in Congress (for example, medical malpractice, human cloning, and Social Security privatization) or one of several pending Supreme Court cases (the Nike free speech case and the University of Michigan affirmative action cases). Some students picked their own topics such as the reexamination of Title IX and SUV safety. Students worked in groups of three or four.

Additionally, each student was required to complete five assignments on the group topic: a book report, an editorial, Congressional testimony, a class presentation, and a group report. Students were assigned to represent a stakeholder in the group’s topic. For example, members of the Social Security privatization group chose to represent one of the following stakeholders: young Americans, women, African-Americans, and older workers. Each student read a book on how Social Security privatization would affect the selected stakeholder group and wrote an editorial on why the stakeholder group supported or opposed privatization. Each student found a Congressional bill to research and wrote Congressional testimony analyzing the bill and its potential impact on stakeholders. For the class presentation, group members introduced the class to two bills — one pro-privatization and one anti-privatization — gave Congressional testimony for and against each of the bills, and asked the class to vote on which bill should be enacted. For the final paper, the group members wrote an objective paper, outlining the pending bills and advising me (as a Senator) how each bill would impact my constituents.

This series of assignments required students to complete successive drafts of essentially the same document. For example, students used the book report to gain an overview of their topic. For the editorial, students simply had to redraft a portion of their book report, do additional research, and add evidence to support their opinion. For those who wrote an excellent editorial, the Congressional testimony was simply a longer version of the editorial in a different format. Those who had not written a strong editorial used the feedback from the editorial to strengthen their arguments and add more persuasive supporting evidence. The final presentation was a logical extension of the group work so far: it allowed students to present their previous work in front of the class and motivated them to develop a strong introduction so that the class would understand the issues involved. Finally, the final report required students to transform their persuasive arguments into a strong objective research paper. Through this sequence of interconnected, cumulative assignments, students answered three of the four essential questions.

Some professors might worry that this teaching method is more time-consuming than the traditional method. Actually it was coming up with the essential questions and developing the series of assignments that took weeks. As for extra time commitments when I implemented it, I met with each group at least once out of class to help them with their projects. But, on the other hand, I had two classes free while the librarian taught research, and at the end of the semester, I enjoyed several weeks of class presentations and witnessed the metamorphosis of college students into confident legislative interns, making any extra time well worth it.

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Designing Research Assignments: Assignment Ideas

  • Student Research Needs
  • Assignment Guidelines
  • Assignment Ideas
  • Scaffolding Research Assignments
  • BEAM Method

Assignment Templates

Research diaries offer students an opportunity to reflect on the research process, think about how they will address challenges they encounter, and encourage students to think about and adjust their strategies. 

  • Research Diary Template
  • Research Diary Instructions

Alternative Assignments

There are many different types of assignments that can help your students develop their information literacy and research skills. 

The assignments listed below target different skills, and some may be more suitable for certain courses than others.

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Designing Jobs Right

  • Roger L. Martin

articles on designing assignments

It’s a given of human nature that whenever people get an assignment that they can’t or don’t want to do, they’ll make up a different one and do that instead. If a job is unchallenging, they’ll redefine it to be more interesting, and if it’s not doable, they’ll turn it into something that they can accomplish. Sometimes that works out, but mostly it doesn’t, because the job doesn’t fulfill its intended function.

Managers will be far more effective if they take time to sit down regularly with employees and explore what their job preferences are and how their tasks can be both achievable and engaging. But it’s a two-way street: Subordinates must also help design the tasks their bosses will do. If those responsibilities aren’t interesting or value-adding, the bosses will make up their own tasks—with results the subordinates may not like.

Make them challenging—but don’t overdo it.

One of my favorite Star Trek story lines is about the Kobayashi Maru training simulation for Starfleet Academy students. It was first featured in the second Star Trek movie, in 1982, and then when the movie series was rebooted, in 2009.


The all-too-common mistakes businesses make with recruiting, hiring, benefits, and job design—and how to avoid them

  • Roger L. Martin is a former dean of the Rotman School of Management, an adviser to CEOs, and the author of A New Way to Think (Harvard Business Review Press, 2022).

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Generative AI fuels creative physical product design but is no magic wand

Although generative AI (gen AI) is in its infancy, the technology is already leaving an indelible mark on how physical products and packaging are conceived, innovated, and designed.

About QuantumBlack, AI by McKinsey

QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.

From product packaging to car components and retail displays, gen AI  enables industrial designers to explore more ideas and product experiences, including previously unimagined ones, and develop initial design concepts significantly faster than with traditional methods.

Additionally, with the ability to visualize concepts in high fidelity much earlier in the design process, companies can elicit more precise feedback from consumers as they work to fine-tune every element of the user experience (see images below). In product research and design alone, McKinsey estimates gen AI could unlock $60 billion in productivity . 1 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023.

A comparison of six modern welding helmets rendered with generative AI. Each helmet shows a sleek, sporty aesthetic, with different design variations and transparent displays that enable welders to view key metrics and adjust light sensitivity as they work.

While gen AI tools can bring about extraordinary outputs, they cannot replace human expertise. Just as the industry saw with the arrival of computer-aided design (CAD) and later advancements such as 3-D printing and augmented and virtual reality, while the methods for designing physical products may change, design experts are needed to ensure the meaningful use of the technology and delivery of business value.

In the case of industrial design, experts conducting consumer research often unearth important insights that inspire pivotal design choices. Their skill in identifying the best concepts from the dozens of AI-generated images—assessing outputs with an eye for aesthetics and manufacturability and manipulating images based on user research and their design sense—is crucial in providing a final design that will resonate with users.

Although these tools are relatively new, our teams continue to see their significant impact on productivity. When they are used properly throughout the product development life cycle, we sometimes see a reduction upward of 70 percent in product development cycle times, providing teams with the opportunity to spend more time conducting consumer testing, refining designs, vetting suppliers, and optimizing designs for manufacturability and sustainability. These tools and processes are ultimately a vehicle for growth and innovation, enabling faster development of far better products.

But while R&D and product development leaders can use the technology today to propel innovation, they will need to understand and prepare for the technology’s limitations. In this article, we share ways gen AI can unlock creativity and productivity across the product development life cycle, examine crucial considerations for business leaders trying to create business value, and suggest steps for getting started based on our design work and the use of gen AI tools in our creative process.

Unlocking creativity and productivity across the design life cycle

When industrial designers create concepts or redesign packaging, consumer durables, experiences, spaces, or just about anything else, their creative processes generally go through a few essential phases: market and user research, concept development, and concept testing and refinement. Gen AI technology can provide tremendous value at each stage, enabling designers to iterate faster, connect the dots in new ways, and catalyze divergent thinking to create products that transform users’ everyday experiences (exhibit).

Market and user research

Almost all good physical product design starts with market research. What features or qualities are most important to potential consumers? How are consumer preferences and tastes evolving and how are our competitors responding? What gaps exist for creating a new category of offerings?

Using gen AI tools trained on large language models—such as ChatGPT, Bard, and others—designers can gather, synthesize, and make sense of existing market and consumer data far more expediently than previously possible. Moreover, because the tools draw insights from many more diverse data sources than humans alone could analyze, they can reveal untapped market opportunities and overlooked consumer needs or expectations. That enables industrial designers to build a much richer baseline of knowledge for stakeholder discussions and consumer interviews. One consumer packaged goods company augmented its market and user research with new insights from gen AI tools about consumer sentiment and how it might use its brand equity to expand into high-growth markets. With this knowledge, the design team broadened the scope of its ethnographic interviews, gaining feedback on important design elements that informed its subsequent work to develop and refine new concepts.

Concept development

As industrial designers and engineers create new product designs or iterate on the next generation of an existing product or engineering component, text-to-image gen AI tools provide a powerful medium for inspiration and innovation.

The technology’s ability to generate novel, lifelike images based on expert prompts can inspire bolder exploration and bring forward distinctive and potentially first-of-their-kind ideas. These visualizations, data, and other outputs that emerge as designers input rough sketches, ethnographic research insights, and features based on consumer sentiment into a gen AI tool can be a great starting point, drastically accelerating the concept development phase. That said, human intervention by an expert designer is still needed to validate, test, and refine outputs to make them meaningful, manufacturable, and impactful, as the images generated typically can’t be used in their initial state (for instance, some may not align with the company’s vision, others may not reflect the designer’s prompt in any meaningful way, and others still may be completely unmanufacturable).

As with previous technological evolutions, such as the emergence of CAD and 3-D printing, gen AI frees design experts from mundane and time-consuming tasks when preparing concept images, mood boards, and storyboards. By inputting iterative prompts about target performance goals and new specifications, for example, industrial designers can arrive at the “best answer” faster than if they tested different theories individually and then conducted highly manual due diligence (see images below).

Initial prompt

A generative AI rendering of a titanium bicycle pedal following an initial prompt by designer. The pedal displays numerous irregularities in the placement and number of studs, an uneven distribution and variety of structural supports connecting the top and bottom plates, and unintelligible text and logo.

Prompt progression

A series of four images depicting ten titanium bicycle pedals developed by iteratively prompting generative AI. The ten pedals display numerous design variations and flaws including inconsistencies in the size and shape of studs and the structural supports connecting the top and bottom plates, and, in one case, an axle housing that ends midway across the pedal.

Final, refined, and manufacturable

An image comparing the final raw output of a titanium bicycle pedal from generative AI following iterative prompting and the same image after it has been refined using image-editing software.  The raw image shows studs and structural supports inconsistently placed and grooves marked on inside surfaces. After refinement by the designer, the studs are uniformly positioned, the interior surfaces are smooth, and the structural supports are precisely aligned with the corners and center of the pedal for improved strength.

Industrial designers at an automotive OEM needed just two hours with the help of gen AI to create the initial design concepts for 25 variations of a next-gen car dashboard with a touch screen interface, charging surfaces, instrument panel, and other components. These concepts were then further refined by the design team using an image-editing software to provide stakeholders with a clearer picture of where the industry was going and how to evolve component interfaces, form factor, color, material, finish, and more for the latest models of electric vehicles (see images below). Without gen AI, creating images with similar detail and quality would have taken at least a week with many more iterations. This process empowered designers to bring a product experience to life in a far more tangible manner and in a fraction of the time.

Side-by-side images of a traditional car interior and a generative AI rendering. The AI rending has futuristic lighting a polished interior and larger digital display screens.

Given that gen AI outputs currently require significant manipulation, the creation of these images typically happens in the studio. But as the technology develops and its outputs become more refined, industrial designers and engineers are increasingly sitting in meetings with business leaders and conducting consumer research sessions while using gen AI tools to create inspirational images in real time based on live feedback.

Concept testing and refinement

With the ability to elevate a conceptual napkin sketch or rough design idea to an immersive visual, industrial designers can also bring new concepts and experiences to life. This can enable more meaningful discussions with business leaders and consumers as they seek feedback on potential opportunity areas, concepts, and future visions.

Executives at a preeminent museum, for instance, could better visualize opportunities to increase accessibility of museum exhibits when industrial designers edited and combined AI-generated images with supplementary visual content (sketches, graphics, and so on) to create storyboards that illustrated novel formats, products, services, and experiences (see image below).

A generative AI image of an illustration of a modern museum exhibit. People are seen looking at artwork and the image is overlayed with digital popups indicating where a viewer can click for more information or engagement.

Following the testing of initial concepts with stakeholders, designers can then use the technology to refine product style, apply finishing touches, and map future concepts to inform product road maps—sometimes in hours instead of weeks—before moving to the subsequent phases of design detailing, refinement, engineering concepts, and design for manufacturing.

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Beyond design.

Leaders seeking to further use the technology in product simulation and testing should watch the gen AI space closely. The technology is rapidly evolving, and as it does, we anticipate even more capabilities will become available to simplify the handoff between design and engineering and dramatically accelerate engineering processes. We’re already seeing the market launch of gen AI software solutions that enable industrial designers and engineers to rapidly turn product concepts into CAD models. That allows them to model products far faster and begin the engineering process more expediently. While the tools are still nascent, we can imagine in the not-too-distant future that these tools will drastically improve and accelerate design-to-engineering handovers.

We also expect to see new tools capable of rapidly analyzing designs for manufacturability and serviceability—for example, to confirm whether a product can be manufactured using a facility’s existing injection molding tools. From an engineering perspective, gen AI is already revolutionizing the way experts approach long-established simulation engineering problems, such as how to optimize the structural performance of products. One gen AI tool for finite element analysis and topological optimization—cornerstone techniques for understanding how a part performs under different conditions and how to produce lightweight yet strong structures—can generate hundreds of improved-design options for parts based on identified criteria, such as forces, pressures, and environmental conditions. In the future, we can expect an even more comprehensive range of capabilities from such tools, including the abilities to transform rough sketches into detailed engineering part models, facilitate material selection and optimization, and identify ways to enhance manufacturability, optimize components for assembly, and reduce costs.

Crucial considerations for achieving business value

Without a doubt, gen AI outputs can be impressive. However, producing meaningful outputs and turning them into a desirable, user-centric, manufacturable product that matches user preferences, pain points, and expectations doesn’t happen by just pressing a button. To achieve business value, industrial design and engineering expertise are crucial in the following areas:

  • Conducting consumer research. Consumer research gleaned from gen AI tools may seem comprehensive; however, these tools can provide incorrect information (often called hallucinations). Additionally, even when the insights provided are accurate, they can offer only a baseline of knowledge, as consumer trends and behavior often change faster than training data sets. As a result, design teams must still verify hypotheses and investigate emerging trends through primary research. By combining gen-AI-produced insights and ethnographic interviews, design teams can obtain a much richer understanding of user preferences than either can provide on their own in the same period.
  • Developing effective prompts. Highly iterative prompting is required to produce something close to what designers envision, consumers want, and companies can manufacture. A simple sentence may generate an interesting image, but the output won’t necessarily be accurate, feasible, or relevant (see images below). Ultimately, design experts must provide context for the overall concept, including subject, medium, environment, lighting, color, mood, and composition. They need to determine how much detail to include (for instance, less detail might produce more variety but result in concepts that don’t have the specific features needed). What’s more, they need to consider prompt length and how to separate complex prompts (having fewer words in a prompt means each word has more influence, which can affect outputs).

Two side-by-side illustrations of a girl painting a flower made by generative AI. The image on the left looks pretty at first glance, but upon inspection proves inconsistent with reality while the right image does not include these errors.

  • Refining gen AI outputs. Oftentimes, text-to-image tools generate flawed images: a rogue plant grows out of the top of a television, or an unflyable drone is created (see image below). Organizations should expect to perform substantial postproduction editing—for instance, by using image-editing software to fine-tune the colors, fonts, and patterns used in the final concepts—to achieve a meaningful result. Even when initial outputs look as though they could be on store shelves today, closer inspection typically finds they are a far cry from a manufacturable product. Today, designers and engineers must still create their refined version of a concept in CAD to ensure the product accounts for manufacturing specifications, requirements, and constraints.

A generative AI rendering of a passenger drone with elements insufficient for safety and manufacturing.

  • Curating the best concepts. Gen AI can produce dozens of concepts quickly, but as the famous “jam experiment” study showed, too many choices can overwhelm both important stakeholders and consumers. As a result, organizations will need design experts to identify the best ideas from the large number of images produced and refine them based on aesthetics, feasibility, fit for use, and more to ensure concept testing with users yields valuable feedback.
  • Adding a good dose of human empathy. AI tools are only as good as the data they are trained on. And given the “averaging” that may occur with aggregated inputs, they can perpetuate historical biases, oversimplify solutions, and gloss over insightful bits of nuanced human behavior that can provide the seeds for innovation. Industrial designers and engineers, therefore, must provide ongoing oversight of the design, making certain that all facets of product use are considered—from the aesthetics (whether the design is aligned with regional and cultural preferences) to ergonomics (whether the gen AI output is too large or unwieldy for the target audience) and usability (for instance, whether the product is accessible for individuals with disabilities).

Getting started

Adding gen AI to the physical product design tool kit can accelerate and advance product design innovation, but only if teams can effectively use the technology. Based on our work and experience using the tools, we recommend R&D and product leaders consider the following actions to begin building their gen AI capabilities:

  • Set aside time for learning and exploration. This action can involve empowering teams to test the technology in commonplace activities, such as iterating on new product features for an existing offering. It should also involve providing opportunities, such as a dedicated messaging channel or team meetings, to share successes and challenges. In other areas, such as software development, McKinsey research has found that the more practitioners use the tools and share their learnings with others, the better they get . We find the same is true in physical product design.
  • Identify and launch a pilot in high-value domains. While it can be tempting to apply the technology to every project under way, leaders are best served by identifying a pilot project where there’s potential to generate considerable value. A pilot project could use gen AI across the design life cycle for a signature product, or it could focus on streamlining one process, such as research, across its entire flagship product line.

Evaluate risks and institute guardrails. Gen AI introduces new legal, ethical, and reputational risks that leaders must carefully consider and manage. These include concerns about data security (whether confidential information is being exposed when prompting the tool), intellectual property (whether the model outputs infringe on copyrighted, trademarked, patented, or otherwise legally protected material), and reliability (whether the tools are hallucinating and providing inaccurate responses to prompts), among others. In certain instances, such as gen AI’s capacity to hallucinate, the risks may be limited, as design experts typically vet and verify information provided by the tools and marry it with additional primary data sources. Furthermore, any surreal and fictitious image generated by the tools during concept development may be an asset, inspiring greater creativity and originality.

In other instances, especially those related to intellectual property rights and data security, action is required to ensure the responsible use of the technology. Leaders should review their legal processes and design standards to confirm they have the necessary diligence measures in place to ensure a final product doesn’t improperly reproduce third-party intellectual property, regardless of where their teams draw inspiration from—be it gen AI tools or their own research on- and offline. (In cases where teams wish to share AI-generated images they produce as is, leaders should ensure they understand intellectual property and ownership terms put forth by different tooling vendors as well as any relevant local laws that may govern ownership of an AI-generated output.)

Leaders should also implement policies that guide teams on what information can and cannot be used in gen AI prompts. Some best practices include understanding the terms of service for the given gen AI tool and refraining from using third-party intellectual property, proprietary insights, or other sensitive information in prompts.

  • Educate business stakeholders on new processes. The level of detail and refinement of AI-generated images can create the impression that a product is much closer to completion than it is. As a result, as R&D organizations adopt these tools, they should be transparent about their use and provide stakeholders with a clear understanding of what the images represent, their use, and their limitations. Regular updates about the actual progress of a project can also ensure that the highly realistic visual representations don’t lead to overoptimistic expectations.
  • Upskill industrial designers for future roles. Using gen AI in physical product design will invariably create new roles wherein design experts become “curators of creativity,” linking, manipulating, and drawing inspiration from the technology’s outputs to solve product challenges. This role requires storytelling and human-centered design skills, manufacturing know-how, competencies in other digital tools (such as CAD, illustration, sketching, and rendering software), a deep understanding of the use of different materials in design, and so on. It can take years to master these skills and understand how and when to pair with gen AI tools; as such, leaders should begin upskilling their teams today.

Gen AI has begun to reshape physical product design, enabling industrial designers to be more productive, creative, and strategic in building products that solve user needs. While the technology’s outputs can be dazzling, its ability to create business value becomes apparent only when combined with the skilled hands and discerning eyes of design experts. As adoption gains speed and as more designers and engineers integrate this technology into their workflows, we could see some genuinely revolutionary design and engineering solutions blossom. This will potentially lead to an entirely new aesthetic era with ingenious form factors, greater efficiency in material usage and manufacturability, and improved product life spans—benefiting both the companies that create these products and the people who use them.

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Seattle University to Receive $300 Million Art Collection

The hotel developer Richard Hedreen is donating more than 200 artworks, and $25 million in seed money, in honor of his wife, Betty, an alumna.

A painting of a girl in a green dress with yellow, orange and pink flowers against an orange background.

By Christopher Kuo

Seattle University is making plans for a new art museum, thanks to a gift of a $300 million art collection and $25 million in seed money from a donor, the university trustees announced on Wednesday.

The donation — by Richard Hedreen, a real estate developer — is the largest gift in the history of the university, a Jesuit institution founded in 1891, the trustees said in a statement.

Hedreen is donating his entire collection, which has more than 200 works of art dating from the 15th century to today, including art by Thomas Gainsborough, Lucian Freud and Amy Sherald.

“It’s a remarkable teaching collection,” the university’s president, Eduardo Peñalver, said in a phone interview, adding that “we look forward to having that on our campus and have our faculty, our students be able to use that across the entire curriculum in sparking their own learning and discussion.”

Hedreen said in a statement that he was donating the collection in honor of his wife, Betty, who was a Seattle University alumna and served on the Seattle Art Museum board of trustees. She died in 2022.

“I’m confident that a Jesuit university, which focuses on teaching and the visual arts, poetry, literature, history, education is the right place to have a museum that can teach art history,” Hedreen said in a phone interview.

The new museum will be open to students and the public, Peñalver said, adding that it would be “a bridge between our campus and the city.”

Edgar Gonzalez, the vice president for university advancement, wrote in an email that the university has already begun discussions with an architect related to the planned museum and that the project would take about three to five years to complete. “The seed funding will allow us to begin moving forward with the project immediately,” Gonzalez wrote.

Chris Kuo covers arts and culture as a member of the 2023-24 Times Fellowship class. More about Christopher Kuo

U.S. courts require random judge assignments to avoid ‘judge shopping’

Federal judiciary leaders on Tuesday announced a policy that requires assigning judges at random in civil cases that have statewide or national implications, an effort to address widespread concerns about “judge shopping” in single-judge divisions.

The Judicial Conference of the United States, the policymaking body for the federal courts, said district courts may continue to assign cases to a single-judge division if those cases don’t seek to bar or mandate state or federal actions through declaratory judgment or injunctive relief.

When random assignments are required, the case will be assigned to a judge within the same judicial district.

“The random case-assignment policy deters judge-shopping and the assignment of cases based on the perceived merits or abilities of a particular judge,” Judge Robert J. Conrad Jr., secretary of the conference, said in a statement. “It promotes the impartiality of proceedings and bolsters public confidence in the federal Judiciary.”

The issue of “judge shopping” gained national attention after anti abortion activists filed a lawsuit seeking to revoke federal approval of the abortion medication mifepristone in a division with just one judge: Matthew J. Kacsmaryk, known for his long-held antiabortion beliefs.

In Texas, the attorney general’s office and conservative groups also have looked to single-judge divisions as the places to challenge President Biden’s policies on immigration and the environment, among other issues.

How Texas is challenging the Biden administration on border policy

The Biden administration and organizations such as the American Bar Association have raised concerns about judge shopping in the past, and Chief Justice John G. Roberts Jr. also highlighted in the issue in his 2021 Year End Report on the Federal Judiciary .

Bruce Green, a professor at Fordham Law School, welcomed the amended policy. “I think that it’s deeply problematic to have a party be able to choose the single judge that they want to preside in the case,” he said. “Adopting a policy that makes that more difficult is a good thing. There’s a reason why courts, in general, have the practice of randomly assigning cases within the court, and this will promote that practice.”

Green said single-judge divisions made geographical sense in some ways when judicial districts are very rural. But, he said, judges can still drive long distances to hear cases in different courthouses when necessary, and also have the option of holding hearings online. “The justification for having a single-judge division may not be that compelling anymore, if it ever was,” he said.

But Josh Blackman, a professor at South Texas College of Law, questioned the Judicial Conference’s authority to create the policy and said the issue should be decided by elected lawmakers. “I think the solutions come from Congress,” Blackman said. “I don’t know that this policymaking body has the authority to do what it did — even if they did, I think it’s better coming from the legislature.”

Judicial Conference officials also said Tuesday that they have not completed their review of Supreme Court Justice Clarence Thomas’s financial reporting practices , nearly one year after Democratic lawmakers accused the justice of violating federal ethics laws by failing to report years of lavish travel and gifts from wealthy friends.

Last April, Sen. Sheldon Whitehouse (D-R.I.) and Rep. Hank Johnson (D-Ga.) asked the conference to investigate what they said was Thomas’s failure to report on his annual disclosure forms his travel and real estate deals with friend and benefactor Harlan Crow.

The lawmakers said the conference, which is overseen by Roberts, should refer the matter to Attorney General Merrick Garland to consider whether Thomas had violated the Ethics in Government Act.

The request was sent to the conference’s Committee on Financial Disclosure for consideration. On Tuesday, after the conference’s semiannual meeting, Judge Jeffrey S. Sutton said the committee is still looking into the allegations from lawmakers.

“That was not discussed by the judges at the conference, and they did not have an action item on the point in front of us so it’s still pending, but it’s in front of them,” said Sutton, chief judge of the U.S. Court of Appeals for the 6th Circuit.

Johnson said in a statement that the conference should move quickly. “ Time is of the essence,” Johnson said. “To restore Americans’ trust, the Judicial Conference must act swiftly to show that Supreme Court Justices are not above the law.”

Whitehouse, in his own statement, said he hoped that members of the conference will decide soon how to move forward. “A cloud will hang over the Court as long [as] these questions go unanswered,” he said.

  • Senate Dems postpone vote to subpoena allies of Justices Thomas, Alito November 9, 2023 Senate Dems postpone vote to subpoena allies of Justices Thomas, Alito November 9, 2023
  • Influential activist Leonard Leo helped fund media campaign lionizing Clarence Thomas July 20, 2023 Influential activist Leonard Leo helped fund media campaign lionizing Clarence Thomas July 20, 2023
  • Justice Thomas details jet travel, property deal with billionaire August 31, 2023 Justice Thomas details jet travel, property deal with billionaire August 31, 2023

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