Jul 2026· Journal of Patan Academy of Health Sciences· 0 citations· 12 references
TL;DR
Qualitative results revealed that students heavily utilize GenAI as cognitive support to simplify complex medical concepts; they remain deeply concerned regarding intellectual laziness, cognitive decline, and AI hallucinations.
Abstract
Introduction: While Generative AI (GenAI) is rapidly influencing higher education, data on its integration within medical education remains limited. This study aimed to explore the perceptions, utilization patterns, and underlying concerns regarding GenAI technologies among undergraduate medical and nursing students.
Method: A cross-sectional, mixed-methods descriptive study was conducted from August 2025 to March 2026 after ethical clearance from (Ref No). Total enumerative sampling targeted all enrolled MBBS and nursing cohorts. Data were collected online. The instrument featured 18 quantitative items assessing knowledge, willingness, and concerns via a 5-point Likert scale, alongside four open-ended questions evaluating uses, concerns, and integration suggestions in medical education. Quantitative data were analyzed using descriptive statistics, while qualitative responses underwent inductive thematic analysis.
Result: There were 356(50.92%) respondents to the questionnaire. GenAI adoption was highly prevalent, with 300(84.27%) of students reporting “often” or “always” using the technology. Students exhibited the highest willingness toward GenAI integration, reporting agree/strongly agree, primarily to save time 290(81.46%) and access round-the-clock, 269(75.56%) personalized support. Students expressed the highest concern focused on over-reliance 183(51.41%) and the hindrance of transferable skills 172(48.32%). Qualitative results revealed that students heavily utilize AI as cognitive support to simplify complex medical concepts; they remain deeply concerned regarding intellectual laziness, cognitive decline, and AI hallucinations.
Conclusion: Health sciences students leverage GenAI for academic efficiency but remain acutely aware of risks to critical thinking. Restricting the technology is unsupported by the data; rather, students advocate for proactive curriculum integration and targeted AI literacy training to ensure responsible, effective use.
Background: Artificial intelligence (AI) is revolutionising healthcare, but its integration into Indian undergraduate medical curricula remains nascent. Understanding student perspectives is critical for effective pedagogical reform.
Objectives: This study aimed to assess AI awareness and knowledge, evaluate perceptions toward its integration in medical education, and identify perceived barriers to adoption among undergraduate medical students.
Methods: A cross-sectional, questionnaire-based study was conducted (January-March 2026) among 324 MBBS students (2nd to final year) at an Indian medical college. A structured, pre-validated questionnaire was used to evaluate awareness, usage patterns, perceptions, and barriers (5-point Likert scale). Descriptive statistics and chi-square tests were used for the statistical analysis, and the level of significance was set at p < 0.05.
Results: Participants (mean age 22.71 ± 1.59 years; 55.6% male) reported 77.5% prior AI exposure, though only 12.04% claimed high familiarity. AI chatbots were the primary tools used (92.3%). Students demonstrated favourable perceptions regarding AI’s utility in clinical decision-making and learning while largely rejecting the notion that it would replace educators. Key barriers included data privacy concerns and output reliability. Prior AI exposure significantly correlated with positive perceptions (x² = 11.948, p = 0.018). Most students advocated for formal AI training and ethical guidelines within the medical curriculum.
Conclusion: Indian medical students show positive attitudes toward AI but lack deep technical familiarity. To address ethical and infrastructural barriers, structured curricular integration focusing on foundational AI competencies is essential for preparing future physicians.
Key words: Artificial intelligence, medical education, students, attitude, technology adoption
Anjani Kumar Srivastava, Anjali Singh, Aparnesh Pandey et al.· International Journal of Hea...· 0 citations
Background: Artificial intelligence (AI) is increasingly integrated into healthcare, creating a demand for AI technology literacy among healthcare professionals. This study aimed to investigate undergraduate medical students’ awareness, concerns, and perceptions of AI in healthcare. Methodology: This cross-sectional study was conducted on 356 undergraduate medical students at a constituent medical college of a health university in India. Data were collected using various sections of a structured questionnaire (Google Forms). The questionnaire was validated to ensure its authenticity and alignment with the study's aim. To assess reliability and readability, a pilot study was conducted. Before the study, ethical approval was obtained, and each participant gave their informed consent. SPSS-26 was used to analyze the data. Results: There was a 41.88% valid response rate, but the overall response rate 45.05%, and a fairly balanced distribution of sexes. Across AI domains, combined awareness ranged from 48.6% to 58.1%, whereas combined disagreement ranged from 22.8% to 32.9%. Most students did not receive formal training in AI. The major concerns noted were job security (43.8%), patient data privacy (47.2%), and errors in medical diagnosis and treatment (44.1%). However, 53.8% of the students perceived AI as a valuable tool for improving healthcare delivery and patient outcomes. Conclusion: Undergraduate medical students show moderate awareness of AI in healthcare but lack formal training and in-depth understanding. Concerns persist regarding ethics, job displacement, loss of empathy, and clinical errors, despite recognition of AI’s potential to improve healthcare outcomes. These findings highlight the need for structured AI education within medical curricula and further research on its long-term impact.
Sonali Sharma, Smriti Kayat, N. Saboo et al.· Nigerian Medical Journal· 0 citations
Background and Objectives This study aimed to explore nursing students’ artificial intelligence literacy in terms of knowledge, attitudes, and practices and its impact on their educational input in Iraq. Methods A descriptive cross-sectional survey conducted across 100 nursing students from the College of Nursing, University of Baghdad, Iraq in 2025. The study utilized the Artificial Intelligence knowledge, attitudes, and practices scale. The Statistical Package for Social Sciences (SPSS), version 26 was used to examine the associations among the measured variables in the study. Results The study findings show that more than three quarter of nursing students (80%) possessed a high level of knowledge regarding artificial intelligence, whereas 12% demonstrated a moderate level and only 18% exhibited a low level of knowledge. Findings indicate a low prevalence of negative attitudes, with 59% of students demonstrating positive attitudes. Findings reveals that just over one-third (34%) of students reported practicing often based on the artificial intelligence. Correlational analyses revealed that nursing students age and the study academic year were significantly associated with their total score of artificial intelligence literacy in terms of knowledge, attitude, and practice (p < 0.001). Conclusion The study revealed that nursing students had a wavier level of artificial intelligence literacy of knowledge, attitude and practice and its application in nursing learning. Prioritize strategies should be applied in the nursing curricula to enhance students’ knowledge and translate positive attitudes into consistent practice, ultimately fostering effective integration of artificial intelligence in nursing education.
Haider Mohammed Majeed, Ali Hussein Alek Al-Ganmi, Ali Dhahir Abdulyemmah et al.· F1000Research· 0 citations
OBJECTIVE
Aim: To analyze the key aspects of artificial intelligence (AI) application in medical education and to assess related psychological attitudes among students and academic staff.
PATIENTS AND METHODS
Materials and Methods: A cross-sectional study was conducted using an anonymous online survey. A total of 727 respondents participated, the majority of whom were involved in medical education. The questionnaire included demographic items, questions on frequency and purposes of AI use, self-assessment of AI proficiency, and a 40-item scale evaluating psychological attitudes. Data were analyzed using descriptive statistics and presented as relative frequencies (%).
RESULTS
Results: The findings demonstrated significant integration of АІ into the medical education environment: 41.4% of respondents reported daily use, while 46.6% reported using these tools several times per week. Primary applications included learning support (79.6%), self-education (78%), and preparation of educational materials (30.1%). While 61.8% of participants rated their proficiency as high, the study raised concerns about the rapid pace of technological development and potential cognitive dependence.
CONCLUSION
Conclusions: The normalisation of АІ in academic practice indicates its role as a cognitive extension. While it enhances efficiency and creativity, the risk of reduced autonomy and ethical challenges necessitates the development of structured educational strategies and methodological guidelines for its responsible use in medical training.
Inna I. Kucherenko, A. O. Burdeinyi, L. Lymar et al.· Polski merkuriusz lekarski :...· 0 citations
BACKGROUND
Generative artificial intelligence is increasingly embedded in graduate nursing research environments. Within this context, patterns of reliance on these tools, extending in some cases to dependency, have emerged among nursing graduate students, raising concerns about the impact on the development of foundational research competencies. Yet, limited qualitative evidence exists regarding how such reliance manifests, evolves, and is experienced by students at different training stages.
OBJECTIVES
To explore how nursing graduate students in China experience and navigate generative artificial intelligence use in their research practice, including the conditions under which such use becomes dependency.
DESIGN
Qualitative descriptive study.
SETTING
Data were collected from nursing graduate programs at 10 universities across China.
PARTICIPANTS
Seventeen nursing graduate students (9 doctoral, 8 master's) were recruited using purposive sampling supplemented by snowball sampling. All participants had documented use of generative artificial intelligence tools for research-related tasks for a minimum of three months.
METHODS
Semi-structured in-depth interviews were conducted between March and April 2026. Data were analyzed using Braun and Clarke's six-phase reflexive thematic analysis, supported by NVivo 12.0 software, and interpreted through cognitive offloading theory.
RESULTS
Data analysis identified four themes: (1) pervasive generative artificial intelligence integration across research workflows; (2) how generative artificial intelligence simultaneously augments output and erodes capability; (3) emotional and evaluative responses to generative artificial intelligence dependency; and (4) self-regulation strategies and structural gaps. Cross-case analysis yielded a typology of four user profiles: tool-rational, collaborative self-regulating, efficiency-oriented explorer, and deeply dependent user. Doctoral students predominantly occupied profiles characterized by higher metacognitive control, suggesting an "experience-buffering effect" from prior research training.
CONCLUSIONS
Generative artificial intelligence integration among nursing graduate students simultaneously enhances research productivity while risking the covert erosion of foundational competencies. The central challenge shifts from prohibiting artificial intelligence use to ensuring that metacognitive control keeps pace with integration depth. These findings advocate for the implementation of differentiated, stage-sensitive artificial intelligence governance frameworks tailored to nursing graduate education.
Furong Chen, Jingjing Cai, Shaoxue Li et al.· International Journal of Nur...· 0 citations
Background: This study explores how health science educators perceive Artificial Intelligence (AI) in the teaching/learning process, specifically looking at their opinions, concerns, and hopes for how it can help students learn better.
Methods: This cross-sectional study occurred in the Marrakech-Safi region of Morocco. A structured, validated questionnaire with 24 questions was used to gather numerical data from n=192 educators at the Faculty of Medicine and Pharmacy, as well as the Higher Institute of Nursing and Health Techniques. The scale reliability was acceptable with a Cronbach's alpha of 0.707.
Results: 50 educators responded to the questionnaire. Descriptive statistics show that while a vast majority (96.0\%) believe AI can improve learning conditions, a high percentage (90.0\%) have never used AI in their teaching practices. A significant positive correlation (rs ≈55) was found between the belief that 'AI will assist me in decision-making' and 'AI will aid in the remediation stage' (p = 0.000412).
Conclusion: Based on the study's results, it is crucial to consider educators' perceptions and the potential outcomes when using AI in health sciences education. Addressing ethical concerns and providing appropriate training are crucial for ensuring successful and equitable AI integration in the curriculum.
N. Hachoumi, Mohamed Eddabbah, A. E. El Adib· Health Sciences Investigatio...· 0 citations