Preliminary findings suggest AI-enhanced case-based platforms can engage students and support applied ethics learning but are best positioned to complement rather than replace traditional instruction.
Abstract
Introduction: Generative artificial intelligence (AI) can produce realistic clinical scenarios on demand and deliver immediate, individualized feedback, yet its use to teach ethical reasoning, rather than to address the ethics of AI itself, remains underexplored in interprofessional healthcare education. Aim: This pilot study examined how interprofessional healthcare students perceived an AI-enhanced, case-based platform designed to support ethical decision-making across physical therapy, occupational therapy, speech-language pathology, and audiology. Methods: Students enrolled in an interprofessional education course completed an online module of 20 instructor-vetted, AI-generated ethics cases and an optional post-activity survey of Likert-scale and open-ended items. Quantitative data were analyzed descriptively and qualitative responses were analyzed through content analysis. Results: Ten students responded. Within this small sample, perceptions of platform utility and usability were strongly positive, with all respondents agreeing that immediate feedback and scenario variety supported learning. Perceptions were more divided when the platform was compared directly with traditional classroom learning, and respondents identified pacing and auto-scrolling as usability concerns. Conclusions: These preliminary findings suggest AI-enhanced case-based platforms can engage students and support applied ethics learning but are best positioned to complement rather than replace traditional instruction. Findings are exploratory given the small, demographically limited sample.
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.
Introduction: The integration of Evidence-Based Practice (EBP) into nursing education faces challenges in linking theory to clinical application in complex family health contexts. Students struggle with efficiently accessing, appraising, and applying evidence influenced by sociocultural factors. Artificial intelligence (AI) offers transformative potential but requires pedagogical design to foster critical thinking and ethical use beyond technical skills.Method: An action research with mixed methods was conducted with 100 nursing students. The intervention had four phases: participatory family diagnosis, AI-assisted evidence retrieval and validation, community educational workshops design and execution, and multi-level evaluation.Results: A significant shift in AI use from basic to strategic, with a 40% reduction in literature search time. Qualitative data revealed enhanced critical awareness and ethical reasoning, while quantitative results indicated 90% of students improved critical appraisal skills and 70% felt more confident in evidence-based decisions. The project impacted 100 families, with 90% trusting evidence-based recommendations.Conclusions: Integrating AI in experiential pedagogies like Design Thinking and Service-Learning effectively develops nursing competencies, ensuring technology adoption supports context-sensitive family health learning outcomes.
Maria Graciela Villalba-Condori, Carla Cuya-Zevallos· Publicaciones· 0 citations
The impact of transitioning from a traditional to an AI-aware rubric in a Health and Medicine course is evaluated, examining how this shift influenced educators’ grading practices, instructional strategies, and perceptions of student engagement.
Suzanne Estaphan, Tehzeeb Zulfiqar· Frontiers in Education· 0 citations
INTRODUCTION
Clerkship students encounter ethically significant dilemmas but have limited opportunities to rehearse ethically grounded decision-making and difficult conversations in realistic clinical contexts. We evaluated an immersive, sequential simulation-based clinical ethics education program.
METHODS
We conducted an explanatory sequential mixed-methods pre-post study with clerkship students at Kaohsiung Medical University from September 1, 2022, to May 31, 2023. Participants completed a core e-learning curriculum (digital modules/videos) before a two-day immersive simulation. The simulation comprised six longitudinal standardized patient cases, each with four sequential scenarios. Quantitative outcomes included ethical knowledge, ethical sensitivity, vignette-based ethical decision-making, and confidence in managing clinical ethical dilemmas; satisfaction was measured post-intervention. Pre-post comparisons were made using Wilcoxon signed-rank tests. Focus groups were audio-recorded, transcribed verbatim, and analyzed using reflexive thematic analysis. Findings were integrated using a joint display.
RESULTS
Thirty-eight clerkship students participated. Post-intervention ethical knowledge showed gains across all seven assessed domains. Ethical sensitivity increased across three issues, and ethical decision-making improved across all six vignettes. Confidence increased in nine of 11 items, including dilemma awareness, clarifying ethical problems, ethical reasoning, communication to reach consensus, and confidence in ethical decision-making. Satisfaction with simulation education was high. Qualitative integration identified three themes explaining these gains: confidence through enactment, structured ethical decision-making, and voice and consensus under pressure.
DISCUSSION
The program was associated with short-term gains in ethics knowledge, ethical sensitivity, vignette-based decision-making, and confidence. Qualitative findings suggested that enactment, feedback, and reflection made ethics learning more practical and increased learners' perceived readiness for challenging clinical encounters. Sequential simulation may help clerkship students connect principle-based ethics teaching with real-time reasoning and communication demands. Controlled longitudinal studies are needed to examine durability, transfer, and workplace-based performance.
CLINICAL TRIAL REGISTRATION
ClinicalTrials.gov identifier: NCT05547893.
Yen-Ko Lin, Hsin-Liang Liu, Po-Chih Chang et al.· Medical Teacher· 0 citations
BACKGROUND
Artificial intelligence (AI) is increasingly integrated into higher education. However, evidence on theory-informed AI interventions supporting nursing research training remains limited.
PURPOSE
To evaluate an AI-facilitated teaching assistant (INSPIRE-AI) on final-year undergraduate nursing honors students' research self-efficacy, motivation, and research interest, and explore students' experiences of using INSPIRE-AI.
METHODS
An embedded mixed-methods study comprising a one-group quasi-experimental pretest/posttest design with postintervention semistructured qualitative interviews. Nursing students received access to INSPIRE-AI throughout the honors year. Quantitative survey data (N = 146) were analyzed using paired t-tests and repeated-measures general linear models.
RESULTS
Research self-efficacy improved significantly (P < .001). Students reported that INSPIRE-AI supported structured thinking and reduced uncertainty, though engagement varied due to trust concerns, perceived surveillance, and preference for familiar AI tools.
CONCLUSIONS
Together, these findings suggest that INSPIRE-AI has the potential to support research self-efficacy through structured scaffolding; however, this interpretation should be considered alongside the broader educational support that students received throughout the honors program.
J. Ng, Jia-Ning Chew, T. Akkadechanunt et al.· Nurse Educator· 0 citations
Educational approaches to teaching medical ethics are often successful provided that they engage students actively in class. The present study set out to develop, implement, and evaluate an instructional approach to teaching medical ethics using brief dramatized videos to present common clinical ethical dilemmas in culturally respectful, psychologically safe formats. Five short educational videos were created based on the general medical ethics curriculum in Iran, each depicting a specific ethical issue (informed consent, truth-telling, patient privacy, medical errors, and conflicts of interest). The videos featured child actors and were used in interactive classroom sessions over two academic semesters. This educational approach was evaluated by conducting a cross-sectional survey based on the CIPP (Context, Input, Process, Product) model. The evaluation showed high levels of student satisfaction across all four CIPP domains, with particularly strong scores in student engagement, video content quality, and application of ethical principles. Students reported that the video-based sessions helped them better understand ethical challenges and facilitated deeper reflection on professional behavior. *Corresponding Author Mobasher Mina Address: Department of Medical Ethics, Afzalipour School of Medicine, Kerman University of Medical Sciences, Kerman, Iran. Tel:(+98) 34 31 32 05 00 Email: m.mobasher@kmu.ac.ir Received: 23 Feb 2026 Accepted: 30 Jun 2026 Published: 18 Aug 2026 Citation to this article: Noori Hekmat S, Mobasher M, Ahmadipour H, Malekpour Afshar R, Pardakhty A. How Utilizing short videos as a reflective method for teaching medical ethics concepts: An exciting experience for both students and their teacher. J Med Ethics Hist Med. 2026;19:12. It appears that short, culturally sensitive educational videos have the potential to serve as an effective tool for teaching medical ethics. We therefore recommend future implementation across diverse educational contexts and studies.
S. Hekmat, Mina Mobasher, Habibeh Ahmadipour et al.· Journal of Medical Ethics an...· 0 citations