Exploring Knowledge, Perceptions, and Preparedness for Artificial Intelligence among Health Informatics Students at the University of Hail: Qualitative Study
The research identifies widespread familiarity and generally positive attitudes toward AI tools such as ChatGPT and Google Gemini, which recognize AI as a supportive technology that enhances decision-making and precision medicine while emphasizing the continued importance of human oversight.
Abstract
: This qualitative study explores the knowledge, perceptions, and preparedness of health informatics students at the University of Hail regarding artificial intelligence (AI) in healthcare. Through semi-structured interviews with 15 undergraduate students, the research identifies widespread familiarity and generally positive attitudes toward AI tools such as ChatGPT and Google Gemini. Participants recognize AI as a supportive technology that enhances decision-making and precision medicine while emphasizing the continued importance of human oversight. Despite readiness to engage with AI, significant gaps exist in curriculum design, particularly the lack of practical, simulation-based training and ethical education. The study highlights challenges, including limited specialized courses, technical barriers, and ethical concerns, and presents student-driven recommendations for curriculum reform focused on experiential learning and tiered competency development. These findings contribute empirical evidence to the limited literature on AI preparedness in health informatics education and underscore the need for targeted educational reforms to equip future professionals to integrate AI responsibly and effectively in healthcare.
Objective: Artificial intelligence (AI) is rapidly transforming healthcare, offering opportunities to improve diagnostics, optimize workflows, and reduce medical errors. Effective integration requires not only technological innovation but also clinician engagement and education. This study explores the perceptions, knowledge, and experiences of Spanish medical professionals and students regarding AI in healthcare.
Methods: A cross-sectional online survey was conducted between October and December 2024, yielding 167 valid responses from diverse medical backgrounds.
Results: Participants reported higher familiarity with general technology (mean = 6.6/10) than with AI (5.0) or AI in medicine (4.2). Younger respondents demonstrated greater AI literacy but less experience with medical software. Gender disparities were evident, with males reporting significantly higher knowledge and engagement across all domains. Although 95.8% of participants recognized ChatGPT, familiarity with medical AI tools was minimal. Respondents expressed limited awareness of AI regulation (mean = 2.2/10) and uncertainty about physicians’ roles in policy-making, despite broad support for ethical oversight.
Conclusions: These findings reveal significant educational, generational, and gender gaps that may hinder AI adoption in clinical practice. Strengthening interdisciplinary collaboration, promoting inclusive AI education, and involving clinicians in regulatory processes are essential to ensure responsible, equitable, and effective integration of AI in healthcare.
Jorge García Condado, E. Cristòbal Cóppulo, Mireia Gamundi et al.· Journal of Scientific Innova...· 0 citations
The rapid expansion of digital health demands a transformation in health workforce education, yet the mapping of Artificial Intelligence (AI) modalities and their structural integration in nursing curricula remains fragmented. To address this gap, this scoping review aimed to systematically map global AI applications in nursing education from 2020 to 2026, offering a distinct contribution by synthesizing pedagogical innovations and structural implementation barriers to guide future curriculum design. Guided by the PRISMA-ScR framework, a systematic screening was conducted across Scopus, PubMed, and CINAHL databases. Results mapped five core AI technologies, including intelligent tutoring systems, virtual patient simulations, adaptive platforms, natural language processing, and predictive analytics, which significantly enhance students' clinical reasoning, critical thinking, and professional competence without compromising patient safety. However, global adoption is geographically skewed and heavily hindered by deficient technological infrastructure, high financial costs, ethical data privacy issues, and a pronounced gap in faculty digital readiness. This study concludes that successful AI integration must shift from ad-hoc usage toward structured, policy-driven curricular frameworks. Ultimately, this review provides a critical strategic benchmark for educational administrators and policy makers to standardize digital health competencies, mitigate regional educational disparities, and safely future-proof the next generation of the healthcare workforce.
Dr. S. Lakshmi· International Journal of Nur...· 0 citations
Background The growing integration of artificial intelligence (AI) into healthcare and medical education has created an urgent need to evaluate how prepared undergraduate students are to engage with these technologies. Medical graduates will increasingly encounter AI-driven tools across clinical and educational settings, yet systematic assessment of their readiness and perceptions remains limited, particularly in India. This study aimed to assess AI readiness and perceptions among undergraduate medical students and to examine how readiness varied in relation to sociodemographic characteristics, prior AI training, and patterns of AI tool utilization. Methods This cross-sectional study enrolled 310 undergraduate Bachelor of Medicine, Bachelor of Surgery (MBBS) students at a tertiary care teaching institution in Surendranagar, Gujarat, between September and November 2025. AI readiness and perception were assessed using the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) and a separately developed, validated 10-item questionnaire, respectively. Descriptive statistics were summarized as means, standard deviations, frequencies, and percentages. Participants were categorized as having poor (≤66), average (67-78), or good (≥79) readiness using cut-offs derived from the 33.33rd and 66.67th percentiles of the observed MAIRS-MS score distribution. The same percentile-based approach was applied to each MAIRS-MS domain score. Pearson's chi-square test or Fisher's exact test, as appropriate, was used to examine associations between categorical variables, while Spearman's rank correlation coefficient was used to assess relationships between readiness scores and selected variables. A two-sided p-value of less than 0.05 was considered statistically significant. Results The mean age of the study participants was 20.03 ± 1.65 years, and 182 (58.71%) were female subjects. Using the 33.33rd and 66.67th percentiles of the observed MAIRS-MS score distribution, 169 of 238 participants (71.0%) were classified as having average AI readiness. Previous exposure to AI training (χ² =6.33, p=0.042) was significantly associated with the ethics domain of AI readiness. Total AI readiness showed extremely weak positive correlations with age (r=0.087, p=0.181) and academic year of study (r=0.057, p=0.381), with neither relationship reaching statistical significance. Among all 310 participants, more than half of the students perceived AI as useful for several educational purposes, including teaching (n=165, 53.23%), assignment preparation (n=162, 52.26%), self-learning (n=166, 53.55%), understanding complex concepts (n=170, 54.84%), and clinical case scenarios (n=161, 51.93%). Substantial proportions also expressed concerns regarding misleading information (n=151, 48.70%), potential effects on clinical skills and critical thinking (n=151, 48.71%), and data privacy (n=133, 42.90%). Conclusion Overall, undergraduate medical students demonstrated an average level of AI readiness and generally mixed-to-positive perceptions toward artificial intelligence, with a considerable proportion of students remaining neutral across several items. Previous AI training or exposure was significantly associated with the ethics domain of AI readiness. An extremely weak positive correlation was observed between the total readiness score and both age and academic year of study.
Kumarjiv K. Shreshthi, Jay H. Nimavat, Milind Makwana et al.· Cureus· 0 citations
Artificial intelligence (AI) is rapidly transforming healthcare, medical education, and scientific research. As AI integration into medical education becomes inevitable, faculty development programs are needed to equip medical educators with the necessary knowledge, skills, and attitudes. This study aimed to analyze the AI training needs of faculty members and teaching assistants at the Alexandria Faculty of Medicine (AFM) as a crucial step in planning an AI faculty development program. A web-based cross-sectional survey was conducted to assess respondents’ current knowledge, attitudes and practices regarding the use of AI in their professional practice, along with their learning preferences. A total of 336 faculty members and teaching assistants completed the survey. Quantitative data were analyzed using descriptive statistics, while qualitative responses were summarized via content analysis. While 52.1% of respondents expressed a high interest in integrating AI into their professional practice and 55.6% considered learning about AI highly important, about 56.0% were unaware of AI uses in medicine, and 67.0% had never used AI applications in their work. Key barriers included limited access to AI tools (72.9%), insufficient knowledge (64%), and a lack of training opportunities (60.7%). Most respondents (84.2%) preferred workshop-based training. This study highlights the need for a faculty development program to develop AI competencies of faculty members to fully leverage AI tools and mitigate their limitations at AFM. The findings provide initial guidance for the planning of context‑appropriate AI faculty development initiatives for medical educators.
N. Elnemr, S. R. Aref, Aly Abdelmohsen et al.· Discover Education· 0 citations
Background: The emergence of generative artificial intelligence (AI), particularly with platforms like OpenAI, has brought about a paradigm shift in problem-solving and decision-making approaches. One sector that has notably embraced AI is education, where its integration has revolutionized traditional teaching and learning methods. While prior reports have highlighted the opportunities AI presents in education, they also emphasize the associated risks. Despite these concerns, proponents argue that incorporating AI into education could potentially better prepare students for evolving industry demands.
Objective: The objective of this study is to explore the perceptions of faculty and students at the University of [blinded] regarding AI usage in education.
Methods: The study, approved by the [blinded] Institutional Review Board, employed a mixed-methods design. Quantitative data were collected through a cross-sectional survey distributed to faculty and students. The survey included questions on demographics and perceptions of AI in education. Participants were also given the option to express interest in follow-up interviews. Interviews were conducted with willing participants to further explore their views on AI in education.
Results: The quantitative data revealed that the majority of the faculty and students were aware of and somewhat familiar with AI tools. They generally perceived AI as a beneficial addition to education. However, concerns about AI included potential dependency and the ethical implications of AI-generated content. The follow-up interviews provided deeper insights, with participants expressing optimism about AI's potential to transform education while emphasizing the need for robust ethical guidelines and training to maximize its benefits.
Conclusion: The findings suggest a positive perception of AI among both faculty and students at [blinded], highlighting its potential to enhance personalized learning and better prepare students for future industry demands. However, there is a clear need for comprehensive ethical guidelines and training to address concerns about dependency and the ethical use of AI. The study emphasizes the importance of balancing the benefits of AI with the associated risks to ensure its effective and responsible integration into educational practices. Continued research and dialogue are essential to navigate the evolving landscape of AI in education, ultimately aiming to enhance the learning experience and outcomes for students in healthcare education.
Keywords: Artificial intelligence, perceptions, education, survey, interview
X. Gordy· The Journal of Scholarship o...· 0 citations
Background: Artificial intelligence (AI) is increasingly being used in medical education, offering opportunities for personalized learning, rapid access to information, and support for academic activities. However, concerns regarding accuracy, overdependence, and reduced critical thinking remain.
Objectives: To assess the perceptions, attitudes, perceived benefits and concerns regarding the use of AI tools among undergraduate medical students.
Methods: A cross-sectional study was conducted among 767 undergraduate medical students at a tertiary care teaching institution in Bharuch, Gujarat, India, from January to March 2026. Students from all MBBS years who provided informed consent were included. Data were collected using a pre-tested, semi-structured questionnaire administered through Google Forms. Descriptive statistics were used to summarize the findings as frequencies and percentages.
Results: Among 767 participants, AI use was reported daily by 207 (27.0%) students, on a few days per week by 282 (36.8%), and occasionally or rarely by 278 (36.2%). ChatGPT was the most commonly used AI tool reported by 698 (91.0%) students, followed by Google Gemini 398 (51.9%). The most common academic purpose of AI use was clarifying difficult concepts, reported by 561 (73.1%) students, followed by obtaining quick summaries or notes 460 (60.0%). AI was considered very helpful for understanding concepts by 445 (58.0%) students, while 515 (67.1%) reported faster learning. Nearly half 367 (47.8%) supported integrating AI into medical teaching. However, inaccurate information 334 (43.4%), reduced critical thinking 327 (42.6%), and overdependence 252 (32.8%) were major concerns.
Conclusions: AI tools are widely used and positively perceived by medical students. Structured and responsible integration of AI into medical education may maximize its benefits while minimizing potential risks.
Keywords: Artificial Intelligence, ChatGPT, Medical Education, Medical Students
Vaishali Patel, Vallari Jadav, Kuntal Patel· International journal of sci...· 0 citations
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