Revisiting Assessment Paradigms
The emergence of advanced language models introduces a fundamental challenge to traditional assessment paradigms, where the distinction between human-authored and machine-generated content becomes increasingly blurred [6]. While some institutional responses focus on restrictive measures, a more sustainable approach involves the redesign of evaluation metrics to emphasize critical thinking and process-based learning over final product outcomes [3][5]. The analytical part is framed around explicit comparison criteria rather than descriptive retelling of sources on Artificial intelligence in education and academic integrity: an analytical perspective on current developments in Canada. The preview thesis suggests that the rapid integration of artificial intelligence into higher education necessitates a critical re-evaluation of established assessment paradigms and institutional policies. This analysis explores the tension between leveraging technological advancements for personalized learning and maintaining the integrity of academic standards within the Canadian educational landscape.. A strong final section is expected to identify concrete findings, compare positions or cases, explain the drivers behind those differences, and state what can be concluded without overclaiming. To evaluate the impact of AI on academic integrity and propose strategies for pedagogical adaptation.