Transitioning from a Traditional to an AI-Aware Assignment Rubric in a Health and Medicine Course

Assessment is a powerful driver of student learning, shaping not only what students learn but also how they engage with course content. In higher education, particularly in disciplines like physiology, rubrics serve as both instructional tools and evaluative frameworks, ensuring transparency, consistency, and alignment with learning outcomes. However, traditional assessment rubrics often fail to account for emerging challenges, such as the role of artificial intelligence (AI) in academic work. To address this, Estaphan and colleagues (Estaphan et al., 2025) proposed a framework for adapting assessments in the AI era. One of the key recommendations was the integration of metacognition and reflective assessment, encouraging students to critically engage with AI-generated outputs while maintaining academic integrity. As part of this publication, a revised rubric was introduced as an example of how structured assessment criteria could mitigate AI-related risks while promoting deeper learning. This study serves as an empirical follow-up to assess the impact of implementing the AI-aware rubric in a pathophysiology course, replacing a previous version that had been in use for several iterations. The change was not merely a revision of assessment criteria but a shift in pedagogy, as the new rubric influenced instructional practices, student engagement, and learning behaviours. Given that assessment steers learning (Biggs & Tang, 2011), our study explores how the new rubric shaped both educators’ teaching approaches and students’ learning strategies. To evaluate these changes, we conducted in-depth interviews with three educators who had previously used the old rubric and later implemented the revised version. As these educators were the only instructors engaged in the course, the sample represents the full set of teaching perspectives available. Their insights provide a comprehensive understanding of how the new rubric influenced grading practices, instructional strategies, and student engagement. Our research aimed to answer the following questions: How did the revised rubric influence educators’ grading practices and expectations for student performance?Did the rubric lead to any shifts in teaching strategies or instructional approaches?How did educators perceive student responses to the new assessment framework?What broader implications does the rubric’s implementation have for AI-aware pedagogy and curriculum design? Results The thematic analysis of the interviews revealed four overarching themes regarding the implementation of the revised assessment rubric 1. Transformed Student Learning, Engagement Patterns and Critical Thinking; 2. Refinement of Educators’ Assessment Practices and Expectations; 3. Expansion of teaching strategies into AI Literacy; 4. Broader Pedagogical Implications for AI-Aware Curriculum Design and Adaptation; 5. Implementation Challenges and system adaptation barriers; 6. Navigating Assessment Authenticity in AI-Enhanced Learning Environments; 7. Institutional Support and Professional Development Needs. Overall, the revised rubric was perceived as a valuable enhancement to the assessment process. It facilitated more critical engagement with AI, refined educators’ grading confidence, and encouraged shifts in instructional approaches. However, concerns remained about students’ potential overreliance on AI-generated content and the necessity for structured guidance on AI literacy. These findings underscore the importance of AI-aware pedagogy in preparing students for an evolving academic and professional landscape. This abstract was presented at the American Physiology Summit 2026 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.