Post-secondary institutions are being challenged to rethink long-standing approaches to assessments now that AI tools are readily available and can be easily used to complete tasks without revealing genuine student learning. At RRC Polytech, this reality sparked an important question: How can we ensure assessments continue to provide meaningful evidence of student learning in an AI-abundant context? 

This became a key focus of the Teaching and Learning AI Working Group, one of the cross-functional groups established through RRC Polytech’s AI Strategy. The result was the AI Assessment (Re)Design Lab, which helps instructors redesign assessments so students can demonstrate their learning authentically, and with integrity, in an AI-enabled world. 

Why Assessment Redesign Matters 

Authentic assessment, rather than AI detection, should be the primary focus of institutional responses to generative AI to provide evidence of student learning. At the University of Sydney, when describing their two-lane approach to assessment in the age of AI and later expanding this in the Sydney Assessment Framework, Liu and Bridgeman state that human-AI collaboration is becoming a regular part of learning. They emphasize that institutions must both support productive AI use and ensure students demonstrate achievement of learning outcomes while instructors evaluate both learning processes and final products. 

Similarly, Sarah Elaine Eaton’s work on assessment in the “post-plagiarism” era argues that institutions should move beyond approaches focused primarily on detecting academic integrity breaches. Instead, the focus should be on designing assessments that require students to evaluate AI outputs critically, identify limitations and bias, preserve their own voice, and demonstrate higher-order thinking, judgment, and synthesis to ensure students can use AI effectively while upholding academic integrity standards. 

Research also highlights the limitations of relying on AI detection tools. Lee and Palmer found that AI-generated writing could be modified in ways that significantly reduced detection results, raising concerns about the dependability of detection software. Meanwhile, Bridgeman, Liu, Brockley, and Bassett warn that AI detection tools can undermine trust between students and institutions caused by both false positives and false negatives. These findings suggest that surveillance-based approaches are unlikely to provide a sustainable solution to authentic assessment in an environment where students increasingly work with AI rather than without it. 

From Research to Practice: The AI Assessment (Re)Design Lab 

Taken together, this research highlights that as students increasingly use AI to generate text, solve problems, summarize information, and create content, assessment products may no longer provide sufficient evidence of what learners know and can do. Assessments may need to be redesigned to generate clear evidence of learning, support transparent AI use, strengthen assessment validity, and avoid detection-based approaches. The AI Assessment (Re)Design Lab was developed to help instructors respond to these challenges in practical and discipline-specific ways. The Lab encourages instructors to ask: 

• What evidence of learning am I collecting? 

• Are expectations of AI use clear and transparent? 

• Can students demonstrate evidence of understanding, critical thinking, reflection, process, and growth? 

• Do students understand how to act responsibly with sacred or protected knowledge in AI systems? 

• Does this assessment remain valid when AI is available? 

In practice, this often means redesigning assessments, so students demonstrate evidence of learning through multiple forms, such as oral explanations, debates, practical demonstrations, reflections, mini thesis defenses, or multiple revisions of written work.  

Drawing on their disciplinary expertise, instructors can select and adapt assessment approaches that best fit their learning outcomes and program context, while enhancing opportunities for students to demonstrate learning in authentic and meaningful ways. Using a variety of assessment methods helps support accessibility, reduce barriers, and provide richer evidence of student learning than any single assessment format alone. 

Lab Structure and Principles 

The AI Assessment (Re)Design Lab is a five-hour blended learning experience combining concise learning modules, facilitator-led discussions, small-group activities, and peer feedback. To deepen understanding of assessment best practices, participants review principles of constructive alignment and assessment design. Drawing on their expertise and the diverse needs of Polytech programs, instructors explore how to redesign assessments so students can demonstrate their learning when AI is readily available to strengthen evidence of learning and reduce academic integrity concerns. 

Early Momentum Across the Polytechnic 

Interest in the Lab has exceeded expectations. During Spring and Summer 2026, approximately 133 instructors participated in the first eight deliveries of the AI Assessment (Re)Design Lab. The impact, however, extends far beyond the number of participants. As instructors redesign assessments, students encounter clearer expectations of AI use, well-designed learning activities, and more meaningful opportunities to demonstrate what they know. These improvements influence courses and programs, helping build long-term institutional capacity to respond to the changing educational landscape. 

Expanding lab opportunities in Fall 2026 

The success of the initial offerings has generated significant interest across academic areas. Many Chairs and Associate Deans have requested cohort-based deliveries that allow groups of instructors to work through the redesign process in a shared context to identify common assessment challenges and develop more consistent approaches with expectations around AI use and evidence of learning. Additionally, in many cases, Chairs are also participating in the Labs alongside instructors, reinforcing the importance of this work. To meet this growing demand, eight additional Lab deliveries are planned for Fall 2026, expanding opportunities for participation across the Polytech. 

Ultimately, this Lab is not about responding to AI as a single technology. It is about strengthening all assessments for the future and ensuring that, as learning environments continue to evolve, students remain at the centre of the educational experience. 

RRC Polytech’s AI Strategy is led by Rebecca Molly Hiebert. For more information visit rr.ca/artificial-intelligence.