Background <p>Artificial intelligence is increasingly embedded in clinical practice and medical education, yet the psychological determinants of students’ readiness remain poorly understood. We are aware of no study that has simultaneously examined personality traits, technology affinity and AI readiness in a single cohort.</p> Methods <p>In a cross-sectional online survey using convenience and snowball sampling, medical students from six continents completed three self-report instruments: the Medical Artificial Intelligence Readiness Scale (MAIRS-MS), the Big Five Inventory–10 and the Affinity for Technology Interaction scale. Pearson correlations, multiple linear regression and ANOVA were applied. The protocol was prospectively registered (OSF: osf.io/7s89a).</p> Results <p>Of 1,920 respondents, 1,278 (66.5%) completed all instruments and constituted the analytic sample, whilst 642 (33.5%) with incomplete data were excluded by listwise deletion (54.8% female; mean age 19.9 ± 1.56 years; 65.9% European). Openness correlated with the Vision subscale (<i>r</i> = 0.669) and agreeableness with the Ethics subscale (<i>r</i> = 0.602); technology affinity was associated with overall readiness (<i>r</i> = 0.231; all <i>p</i> &lt; 0.001). Male gender (B = 6.698), openness (B = 1.772) and agreeableness (B = 1.518) were independent predictors of overall readiness, whereas technology affinity did not retain an independent effect once personality and gender were accounted for. Men reported higher overall readiness (η² = 0.143); women scored marginally higher on ethical awareness.</p> Conclusions <p>Personality traits were independently associated with AI readiness, whereas technology affinity was associated with overall readiness only at the bivariate level. Given the cross-sectional design, these relationships denote associations rather than causal effects. Medical AI curricula should adopt differentiated instructional approaches informed by students’ psychological profiles.</p>

错误:搜索内容不能为空,请输入英文关键词
错误:关键词超出字数限制,请精简
高级检索

Personality traits, technology affinity, and artificial intelligence readiness in medical students: a multinational cross-sectional study

  • Helmar Bornemann-Cimenti

摘要

Background

Artificial intelligence is increasingly embedded in clinical practice and medical education, yet the psychological determinants of students’ readiness remain poorly understood. We are aware of no study that has simultaneously examined personality traits, technology affinity and AI readiness in a single cohort.

Methods

In a cross-sectional online survey using convenience and snowball sampling, medical students from six continents completed three self-report instruments: the Medical Artificial Intelligence Readiness Scale (MAIRS-MS), the Big Five Inventory–10 and the Affinity for Technology Interaction scale. Pearson correlations, multiple linear regression and ANOVA were applied. The protocol was prospectively registered (OSF: osf.io/7s89a).

Results

Of 1,920 respondents, 1,278 (66.5%) completed all instruments and constituted the analytic sample, whilst 642 (33.5%) with incomplete data were excluded by listwise deletion (54.8% female; mean age 19.9 ± 1.56 years; 65.9% European). Openness correlated with the Vision subscale (r = 0.669) and agreeableness with the Ethics subscale (r = 0.602); technology affinity was associated with overall readiness (r = 0.231; all p < 0.001). Male gender (B = 6.698), openness (B = 1.772) and agreeableness (B = 1.518) were independent predictors of overall readiness, whereas technology affinity did not retain an independent effect once personality and gender were accounted for. Men reported higher overall readiness (η² = 0.143); women scored marginally higher on ethical awareness.

Conclusions

Personality traits were independently associated with AI readiness, whereas technology affinity was associated with overall readiness only at the bivariate level. Given the cross-sectional design, these relationships denote associations rather than causal effects. Medical AI curricula should adopt differentiated instructional approaches informed by students’ psychological profiles.