Aug 2026· Behavioral Science· Vol 16, pp. 1458· 0 citations· 61 references
TL;DR
High perceived AI literacy was associated with multiple combinations of value orientations rather than with a single value dimension, suggesting context-bound associations may inform future research on whether medical AI curricula can integrate technical training with ethical reflection and public-oriented professional values.
Abstract
Artificial intelligence (AI) is increasingly integrated into healthcare education, clinical decision-making, and future practice. For medical students, AI literacy entails technical understanding, practical competence, ethical awareness, value-based judgment, and responsible engagement. This study examines how culturally embedded value orientations are associated with Chinese medical students’ perceived AI literacy, as assessed using a self-report instrument. In a cross-sectional sample of 1500 medical students enrolled at a comprehensive university in Henan Province, China, AI literacy was assessed using the 12-item Artificial Intelligence Literacy Scale (AILS), a self-report measure whose scores represented perceived AI literacy. Value orientations were measured using the 32-item Chinese Values Questionnaire (CVQ), comprising eight value dimensions. NCA and fsQCA were conducted to examine necessary conditions and configurational associations with membership in the high perceived AI literacy set. No single value dimension met the criterion for set-theoretic necessity with respect to membership in the high self-reported AI literacy set, and no individual condition met the fsQCA necessity consistency threshold of 0.90. Four sufficient configurations associated with high perceived AI literacy were identified, with an overall solution consistency of 0.867 and coverage of 0.383. Moral Self-Discipline and Public Interest repeatedly appeared as core or peripheral conditions. These results suggest that high perceived AI literacy was associated with multiple combinations of value orientations rather than with a single value dimension. High perceived AI literacy was associated with multiple value configurations rather than one dominant value orientation. Empirically, this study applies configurational analysis to understand how value orientations are associated with AI literacy, complementing existing research on knowledge, attitudes, and readiness. These context-bound associations may inform future research on whether medical AI curricula can integrate technical training with ethical reflection and public-oriented professional values.
Understanding nurses’ AI literacy and its relationship with thriving at work may help hospitals design more targeted support strategies to support nurses’ AI literacy and thriving at work in similar clinical contexts.
Artificial intelligence (AI) is becoming increasingly relevant to teacher education, yet evidence remains limited on how pre-service teachers’ AI literacy, risk perception, and academic confidence can be assessed within a coherent but multidimensional framework. This study examined the psychometric properties of the AI Literacy, Risk Perception, and Academic Confidence Questionnaire (AIRPAC-Q) among Chinese pre-service teachers. A cross-sectional survey was conducted with 528 participants recruited from teacher education programmes in China. The sample was randomly divided into an exploratory factor analysis (EFA) subsample (n = 258) and a confirmatory factor analysis (CFA) subsample (n = 270). Psychometric evaluation included expert-based content validation, pilot refinement, EFA, CFA, reliability testing, convergent and discriminant validity, concurrent validity, and known-group validity analyses. The final questionnaire retained 14 items across three complementary dimensions: AI literacy, risk perception, and academic confidence. Expert ratings showed acceptable content validity (I-CVI = 0.83–1.00; S-CVI/Ave = 0.94). The hypothesized three-factor model showed an acceptable fit to the data (χ² = 146.32, df = 74, χ²/df = 1.98, CFI = 0.952, TLI = 0.941, RMSEA = 0.060, SRMR = 0.047) and outperformed two-factor and one-factor alternatives. Cronbach’s α values ranged from 0.82 to 0.88, composite reliability values ranged from 0.83 to 0.89, and average variance extracted values ranged from 0.56 to 0.59. Participants with prior AI use experience scored higher on AI literacy and academic confidence but slightly lower on risk perception than those without such experience. These findings support the AIRPAC-Q as a context-specific multidimensional tool for assessing competence, caution, and confidence in AI-supported teacher education.
Undergraduate medical students show moderate awareness of AI in healthcare but lack formal training and in-depth understanding, highlighting 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: Artificial intelligence (AI), often termed the "fourth industrial revolution," is transforming healthcare by improving decision-making, diagnosis, and patient management. Despite its potential, understanding and integration of AI into medical education and practice remain limited, especially in resource-constrained regions. This study explores medical students' knowledge, attitudes, and practices concerning AI in a rural medical college in Haryana, India. Material and Methods: A cross-sectional online survey was conducted among 189 medical students at BhagatPhool Singh Government Medical College for Women, Haryana. Data were collected using a 13-item questionnaire on demographic details, knowledge, attitudes, and practices related to AI. Convenience sampling was employed, and data analysis was performed using SPSS v26.0. Frequency tables were used to summarize the findings. Results: Among respondents, 91.5% were aware of AI, yet only 53.4% knew about its medical applications. Most participants (81.5%) reported not being taught about AI during medical school, but 79.9% expressed an interest in learning more. While 76.7% agreed AI aids in early diagnosis, only 20.6% believed it could replace doctors. Regarding AI's potential burden, 18.5% agreed, while 45.5% remained neutral. For future practices, 51.9% showed willingness to work with AI, 66.7% believed it could aid in information gathering, and 78.8% thought AI would revolutionize medical teaching and research. Conclusion: The majority of medical students demonstrated a positive attitude toward AI and recognized its potential in healthcare, despite limited knowledge of its applications. Specific training and curriculum integration of AI in medical education are essential to prepare future doctors for the evolving digital landscape. Keywords: Artificial intelligence, medical education, medical students, rural healthcare, AI applications.
Introduction: Radiology stands at the forefront of artificial intelligence integration in medicine, with artificial intelligence algorithms demonstrating performance comparable to experienced radiologists in diagnostic imaging tasks. Medical students, as future clinicians and referring physicians, require adequate knowledge of and positive attitudes towards artificial intelligence-driven radiology. Despite growing global interest, data from South Asian medical education contexts remain critically scarce.
Aims: To assess technology readiness, digital literacy, and ethical awareness regarding radiological artificial intelligence among medical students at a tertiary teaching hospital in Nepal.
Methods: A cross-sectional survey was conducted among 227 consenting medical students using a structured, self-administered 41-item questionnaire with five-point Likert scale responses. Domain scores for knowledge, attitudes, and ethical perspectives were computed. Independent-samples t-tests and one-way ANOVA were used for group comparisons.
Results: Of 227 participants (mean age 21.3±1.8 years; 55.9% male), 56.8% demonstrated adequate artificial intelligence knowledge. Mean domain scores were: knowledge 3.21±0.62, attitude 3.46±0.59, and ethical awareness 4.06±0.60 (out of 5). Among knowledge items, recognition of artificial intelligence assistance in radiological image interpretation was the highest-rated, with 57.2% of students in agreement. Students with formal artificial intelligence teaching had significantly higher attitude scores (3.64±0.68 vs 3.40±0.54; p=0.008). Only 25.1% had received formal artificial intelligence education.
Conclusion: Medical students in Nepal show moderate artificial intelligence knowledge, positive attitudes, and high ethical awareness, with the strongest clinical artificial intelligence recognition centered on radiological applications. Radiology departments should lead the integration of structured artificial intelligence literacy programs into undergraduate medical curricula across South Asia, where ethical awareness is high but formal artificial intelligence education remains critically insufficient.
Prasanna Ghimire, Sakar Sharma, P. Ghimire· Journal of Nepalgunj Medical...· 0 citations
Positive correlations were identified among all measured dimensions, with the strongest association observed between hope and adaptation, and the findings indicate that students’ evaluations of artificial intelligence involve interrelated perceptions of knowledge, anxiety, positive expectations, and educational preparation needs.
Özlem Gök· VITALIS JOURNAL· 0 citations
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