What if, as educators, we take as much interest and care in designing assessments as we do with preparing for classroom teaching? Can we aim to create assessments that are equally valuable opportunities for students to learn?
Assessments that students are intrinsically motivated to complete are less susceptible to academic dishonesty of any kind (Krou et al., 2021). Creating an assessment that is purposefully designed to engage students in a meaningful learning experience – rather than a competition between students or a tedious task – can similarly encourage students to undertake it with integrity.
Take a moment to reflect on what an assessment-as-learning-experience might look like in your discipline: how might students develop their knowledge, skills and/or attitudes through completing the assessment? Or, consider what students learn from the assessment you would like to redesign: if they are only recalling what they already know (which is also important, of course!), can your redesigned assessment incorporate elements that allow them to learn something new?
The ubiquitous presence of generative AI has amplified structural tensions in postsecondary education (i.e. problems with grading and the transactional views of assessment it engenders) and compelled many of us to reckon with our assessment practices. It has also complicated the implementation of various well-designed assessments.
Note: susceptibility to AI misuse is not an indicator of a poorly designed assessment! But the ample availability of AI applications is a reality we must acknowledge in our assessment design practice.
Most current approaches to preventing inappropriate AI use in assessment involve direct supervision of some kind: in-class activities observed by the teaching team, oral assessments or invigilated exams. But many instructors acknowledge the limitations of such forms of assessment, like the difficulties in scaling to large classes, or the constraints on creativity in assessment design, or the barriers to accessibility. Ultimately, assessment design alone is unlikely to wholly address the misuse of AI; clear syllabus statements, conversations with students, documentation tools and other supports also play a role in guiding students towards responsible AI use.
At the same time, completely prohibiting the use of AI may not adequately prepare students to make ethical and well-considered decisions about their use of the technology in the present and future. Course activities or assessments that acknowledge the nuances of navigating the realities of generative AI can help guide students in determining how AI may support a task or hinder their learning, and how to properly document their use.
If you would like some inspiration for assessment designs that either interrogate or incorporate AI thoughtfully, refer to the assignments collected by Harvard’s AI Pedagogy Project (integrating AI use) and the University of Toronto (examples of both integrating and preventing AI use).
Corbin et al. (2025) propose another reframing of assessment in the era of generative AI, as a wicked problem:
Unlike their counterpart ‘tame’ problems, which have clear definitions and measurable solutions, wicked problems lack definitive formulations, and their solutions are not true or false but rather better or worse, requiring judgment, compromise, and adaptation. This distinction is key because it disrupts the assumption that there is a ‘correct’ policy, assessment method, or institutional response waiting to be discovered. Instead, every approach carries trade-offs, is shaped by context, and must be continually reassessed in response to evolving conditions (737-738).
They also note the significant personal toll on instructors pressured to find the “right” solution, who bear the full consequences of their choices (738, 742). As we engage in our assessment design or re-design process, thinking of assessment after generative AI as a “wicked problem” may remind us that there is no ideal solution eluding our grasp but rather, trade-offs we must make in our given context.
Many of the educators we work with at the MacPherson Institute tell us how grades and grading have come to dominate students’ thinking about the practice of assessment, and their experience of university learning more generally. Grades are also a major source of tension between educators and students, often putting educators in the position of negotiating an incremental change or justifying a particular mark. Furthermore, the overemphasis on grades is likely to be a driving factor in students’ misuse of generative AI, if getting a good mark is prioritized above learning and growth.
While the “Rethinking Assessment” module is intended to support the design or redesign of a specific assessment, we also recognize that no assessment exists in a vacuum. As you think through the particulars of your assessment, you may also wish to consider how it fits into the bigger picture of your course.
The term “ungrading” is a bit misleading: the ungrading movement in higher education encompasses a range of alternative grading strategies, and not necessarily foregoing grades altogether. For many educators, doing away with grades may seem impossible given constraints like class size, student expectations and accreditation requirements.
One of the key proponents of ungrading, progressive educator Jesse Stommel, clarifies:
“Ungrading’ means [questioning] grades as a systemic practice, distinct from simply not grading…. Too many of our approaches to grades treat students like they’re interchangeable and fail to recognize their complexity. Can we imagine flexible approaches to assessment, pedagogies which center intrinsic more than extrinsic motivation, encouraging and supporting learning, rather than policing behavior?” (Stommel, n.d.)
The ungrading movement, then, seeks to take practical steps towards better and more equitable grading strategies, but also towards an eventual future where grades have a minimal role – if any – in the student learning experience.
Stommel has a growing list of ideas to introduce a culture of ungrading into the classroom:
Are any of the ungrading strategies listed above consistent with your teaching values and beliefs? If you have a specific assessment design or need in mind, could aspects of the ungrading strategies discussed above be built in?
* By structured activity, we mean a carefully thought-out and clearly communicated process, which frames self-reflection or peer feedback as a meaningful and respectful practice.
** We acknowledge that not every educator has the privilege of taking risks in their teaching, which can be contingent on factors like employment precarity, race, gender, perceived language abilities, and more.
These ungrading strategies can be used in combination, and not every strategy will be realistic for every teaching context. If you would like to learn more about a particular ungrading approach, the best way to get started is to talk with colleagues who have tried these approaches in their own teaching. Though every offering of every course will have unique opportunities and challenges, your fellow educators can share the lessons they have learned to help you navigate your own ungrading journey.
Corbin, T., Bearman, M., Boud, D., & Dawson, P. (2026). The wicked problem of AI and assessment. Assessment & Evaluation in Higher Education, 51(4), 736–752. https://doi.org/10.1080/02602938.2025.2553340.
Krou, M. R., Fong, C. J., & Hoff, M. A. (2021). “Achievement Motivation and Academic Dishonesty: A Meta-Analytic Investigation.” Educational Psychology Review, 33(2), 427–458. https://doi.org/10.1007/s10648-020-09557-7.
Stommel, J. (n.d.). Ungrading and Alternative Assessment, http://bit.ly/ungrading.