Jul 2026· Frontiers in Public Health· Vol 14· 0 citations· 50 references
Medicine
TL;DR
Postgraduate health science students’ perceptions and experiences of the structured integration of GenAI into teaching and assessment within the Epidemiology and Principles of Research unit at the University of Canberra are explored.
Abstract
Introduction As Generative Artificial Intelligence (GenAI) continues to influence pedagogical practices in higher education, prevailing discourse has largely focused on issues of assessment integrity, often overlooking student perspectives. Yet, understanding student voices is essential to ensure that AI-enhanced teaching and assessment remain student centred, equitable, and aligned with intended learning outcomes. This study aimed to explore postgraduate health science students’ perceptions and experiences of the structured integration of GenAI into teaching and assessment within the Epidemiology and Principles of Research unit at the University of Canberra. Methods A mixed-methods study was conducted using pre- and post-intervention surveys administered to all enrolled students, with 78 participants completing the evaluation. The intervention involved the scaffolded integration of GenAI into a critical appraisal assessment. Quantitative data were analysed using descriptive statistics and Fisher’s exact test to assess changes over time. Qualitative data from open-ended responses were analysed using thematic analysis following Braun and Clarke’s six-phase approach. Results Students reported statistically significant improvements in their understanding of GenAI and perceived ability to use it effectively (p < 0.001). Thematic analysis identified five key themes: tensions between automation and authentic learning; prompt literacy as a new academic skill; GenAI as a support for metacognitive engagement; ethical ambiguity and cognitive dissonance; and future-oriented learning. Conclusion The findings highlight the importance of thoughtful pedagogical designs that center student voice, support critical engagement, and prepare learners for responsible and reflective use in future professional practice.
It is suggested that structured AI-comparison tasks offer the opportunity to develop AI and digital health literacy in nursing students and offer practical guidance for educators seeking to support graduates to be AI-critical and well-equipped to leverage the efficiencies of these tools.
Generative AI presents a paradox in nursing education as it enables innovation and personalised learning, but poses risks to academic integrity and deep learning when implementation lacks ethical consideration and pedagogical rigour.
Lucie Ramjan, Belinda McGrath, C. Walters et al.· Journal of Clinical Nursing· 0 citations
Systemic racism embedded within health education contributes to persistent health inequities, yet anti-racism training remains inconsistently integrated into undergraduate health sciences curricula in North American contexts. In response to identified gaps, Queen’s University developed GLPH 281: Racism and Health in Canada, a semester-long, credit-bearing course co-designed by faculty, students, and teaching assistants and embedded within the Bachelor of Health Sciences program. This study documents the implementation and evaluation of the pilot offering of GLPH 281, with the aim of identifying best practices for sustainable anti-racism education in health sciences. Using a mixed-methods design, students completed pre- and post-course surveys, questionnaires and weekly student feedback. Quantitative data were analyzed using descriptive statistics, while qualitative data underwent iterative and reflexive thematic analysis using NVivo and Microsoft Copilot. Results demonstrate measurable improvements in students’ self-reported confidence and knowledge engaging with racism and health, with composite survey scores increasing across the cohort over the semester. Qualitative analysis further revealed that students highly valued discussion-centered learning, diverse instructional teams, and applied case-based activities, which were perceived as central to creating safe and engaging learning environments. Concurrently, findings identified challenges including content density, an inherent difficulty of meaningfully representing the breadth and diversity of racialized communities in Canada, and some misalignment between assessments and learning objectives, highlighting the constraints in delivering comprehensive anti-racism content within a single course. This study addresses critical gaps in the literature by evaluating a longitudinal, credit-bearing, and collaboratively designed anti-racism course situated within the Canadian socio-historical context. By documenting both outcomes and implementation processes, it offers a replicable and scalable model for integrating anti-racism education into undergraduate health sciences curricula and contributes evidence to support systemic curricular reform toward culturally safe and socially accountable healthcare training.
ChatGPT was perceived as a supportive but limited educational tool in Nutrition and Dietetics, demonstrating a dual landscape: while ChatGPT enhanced efficiency in material preparation, instructional planning, and clinical idea generation, participants emphasized concerns about information accuracy, source reliability, limited personalization, and threats to academic integrity.
Hacı Ömer Yılmaz, Emre Duman, Kezban Şahin-Demirci· Journal of NutriLife· 0 citations
Applying Occupational Adaptation Theory to support data interpretation highlighted that, in addition to supervisory training, mastering their roles and having actionable strategies to support learners in difficulty, requires adequate resourcing and recognition to ensure CEs are equipped to manage every element of student learning.
Amanda Wray, S. Attrill, L. Lewis· Medical Teacher· 0 citations
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