Our Epistemic Boundaries between Learning and Assessment in a Postplagiarism Era

The session began with an introduction of the 2025-2026 postplagiarism speaker series by Research Professor of Education Dr. Sarah Elaine Eaton, who designed and offered this series through CAIELI at the University of Calgary. It was followed by the land acknowledgement that added relational tribute to the traditional territories of the people of the Treaty 7. Dr. Eaton, then introduced speaker of the session Dr. Soroush Sabbaghan, Associate Professor of Education and Educational Leader in Residence for generative Artificial Intelligence (AI) at the University of Calgary, providing a brief introduction about his recently published book entitled Navigating Generative AI in Higher Education and his recognition as an AI education leader within and beyond Canada. Dr. Sabbaghan, in his insightful presentation, provided various vintages to reflect on our limitless potentials of learning in postplagiarism era, and at the meantime, he emphasized the nuanced challenges in our existing assessment practices.

AI Augmentation, But Not Automation

Dr. Sabbaghan’s session helped in illuminating how boundaries between student learning and assessment have become blurred in the postplagiarism era. Providing example of a lecture hall of 400 students, he shed light on how the pressing reality of generative AI in education created profound distance between how humans actually learn and what existing artifacts-based assessments can realistically capture. Learning could remain invisible in postplagiarism pedagogical spaces, where students’ engagement, belongingness, and sense-making processes could be impossible to observe as they unfold through artificial simulation. Such invisibility extends to assessment itself – when “judgements are indivisible, academic integrity becomes invisible right along with it”. Learning and assessment in postplagiarism era should exist in a dialogic relationship rather than assessment as a means of policing artifacts for the absence of errors. The existing artefacts-based assessment practices separated students’ cognitive engagement epistemically differentiating assessment time from their learning time. Such differentiation between learning and assessment with respect to time remains learning invisible. With AI augmentation, but not automation, the procedural intersection between student learning and assessment becomes visible than ever before, shifting the boundaries from product to process.

In the postplagiarism era, assessment practice should prioritize making learning process as much as visible rather than just measuring artifacts-based outcomes. Most importantly, assessment design should resist potential losses in generative AI integration, prioritizing on collective identity, relationality, belongingness, and shared speciality. In this perspective, the postplagiarism framework demonstrates integrity as a means of learning visibility, protecting the value of human judgement, relationship, and epistemic agency that guide meaningful transformation.

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Bibek Dahal is a PhD candidate at the Werklund School of Education, University of Calgary.