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.
BACKGROUND
The rapid integration of generative artificial intelligence (GenAI) into nursing education presents both opportunities and challenges, yet empirical evidence on students' critical engagement with AI-generated content within assessment contexts remains limited.
AIM
To examine undergraduate nursing students' reflections when comparing their own evidence-based summaries with AI-generated outputs in response to the same clinical research questions.
METHODS
A qualitative descriptive design was employed using retrospective analysis of 497 assessment submissions from an undergraduate nursing cohort at an Australian university. Students formulated a research question, synthesised peer-reviewed evidence, submitted the same question to an AI tool, and critically reflected on the comparison. Data were analysed using qualitative content analysis and thematic analysis.
RESULTS
Four themes were identified: 1. Credibility, quality of evidence and academic rigour. Students identified fabricated references, outdated information, and absence of peer-reviewed sourcing as key limitations. Additionally, students reflected on algorithmic limitations and the challenge of verifying AI outputs without prior topic knowledge; 2. Critical thinking, depth of analysis, and human intelligence. AI was perceived as unable to replicate contextual reasoning or multi-source synthesis; 3. Efficiency, accessibility, and practical utility. AI's speed and clarity were valued for brainstorming and initial scoping; and 4. Student identity, learning, and professional development were shaped by the view that engaging in manual, hands-on research was integral to forming a safe, evidence-informed nursing identity.
CONCLUSION
This study suggests that structured AI-comparison tasks offer the opportunity to develop AI and digital health literacy in nursing students. Students are neither naively accepting of AI nor reflexively dismissive but are actively working to understand its place within the ethical frameworks of nursing education. These findings contribute to AI integration in nursing education 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, Clare 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.
BACKGROUND
Artificial intelligence (AI) tools such as ChatGPT are increasingly influencing health sciences education, prompting renewed attention to their pedagogical, ethical, and professional implications.
OBJECTIVES
This qualitative study explored the experiences and perceptions of academics in Nutrition and Dietetics regarding the integration of ChatGPT into teaching, assessment, and clinical education.
METHODS
This qualitative study involved in-depth interviews with 15 academics teaching in Nutrition and Dietetics in Türkiye. Reflexive thematic analysis was used to analyze the data. The data were coded, and the codes organized into themes and subthemes in MAXQDA (version 2020.2.2).
RESULTS
Five major themes were identified. Findings demonstrated 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. Educators also highlighted the need for digital literacy training, ethical guidelines, and strong professional oversight to ensure safe and responsible use.
CONCLUSIONS
ChatGPT was perceived as a supportive but limited educational tool in Nutrition and Dietetics. Effective integration requires critical evaluation, ethical guidance, and continued human oversight to ensure safe and evidence-based educational practice.
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