The Future Doctor Profile: Assessing AI Literacy, Mental Resilience, and Ethical Competence Among Medical Students in Southwest Nigeria
Abstract
An assessment of the technological, psychological and ethical competencies required of future physicians in an increasingly AI-enabled healthcare environment.
The rapid integration of artificial intelligence into healthcare is reshaping the competencies required of modern physicians. Beyond technological literacy, doctors must demonstrate mental resilience and ethical competence to ensure safe, patient-centred care. Limited empirical evidence has examined these competencies together among Nigerian medical students.
To assess AI literacy, mental resilience and ethical competence among clinical medical students in Southwestern Nigeria and examine their interrelationships within the “Future Doctor Profile” framework.
A multi-institutional cross-sectional study was conducted among 256 clinical medical students across accredited medical schools in Southwestern Nigeria. Data were collected using a structured questionnaire assessing AI literacy, mental resilience using the CD-RISC-10, and AI-related ethical competence. Descriptive statistics, correlation analyses and multivariable regression were performed.
The mean age was 22.5 ± 2.2 years and 63.3% of participants were female. Only 16.0% reported formal AI teaching despite high AI tool usage. Mean AI literacy was 22.3 ± 3.9, mean resilience was 27.3 ± 6.9 and mean ethical competence was 32.0 ± 4.2. Moderate positive correlations were observed between AI literacy and resilience (ρ = 0.409), AI literacy and ethical competence (ρ = 0.385), and resilience and ethical competence (ρ = 0.325), all p < 0.001.
AI literacy, resilience and ethical competence were significantly interrelated among Nigerian medical students, supporting an integrated multidimensional “Future Doctor Profile.” Structured AI education, resilience support and strengthened digital ethics training are necessary.
Clinical-phase medical students.
Accredited institutions in Southwestern Nigeria.
Students reporting structured AI education.
Mean frequency on a 1–5 scale.
66.4% Demonstrated High AI Literacy
The mean AI literacy score was 22.3 ± 3.9. Overall, 66.4% of participants were classified as having high AI literacy, 31.3% moderate literacy and 2.3% low literacy.
50.4% Demonstrated High Resilience
The mean CD-RISC-10 score was 27.3 ± 6.9. High resilience was observed in 50.4% of participants, moderate resilience in 43.4% and low resilience in 6.3%.
73.0% Demonstrated High Ethical Competence
Mean ethical competence was 32.0 ± 4.2. High ethical competence was observed among 73.0% of participants, while 26.6% had moderate competence and 0.4% had low competence.
Only 16% Had Received Formal AI Teaching
Despite widespread AI tool use among respondents, only 41 of the 256 participants reported having received formal teaching on artificial intelligence in medicine.
54.7% Confident in Evaluating AI Information
Although overall AI literacy was relatively high, only 54.7% agreed that they could critically evaluate AI-generated information.
75% Would Verify AI Suggestions
Three-quarters of participants indicated that they would trust AI-generated suggestions only after clinical verification.
94.1% Supported Strong Data Protection
Protection of patient information attracted particularly strong agreement, with 94.1% indicating that patient data used in AI systems must be strongly protected.
85.5% Would Override Conflicting AI Advice
Most participants reported that they would override an AI recommendation where it conflicted with their clinical judgment.
ρ = 0.409
AI literacy and mental resilience demonstrated a moderate positive correlation, which was statistically significant at p < 0.001.
ρ = 0.385
AI literacy was positively correlated with ethical competence, supporting the interconnected nature of technological and ethical preparedness.
ρ = 0.325
Mental resilience and ethical competence also demonstrated a significant moderate positive relationship.
Three Interconnected Competencies
The proposed framework integrates technological intelligence through AI literacy, psychological adaptability through mental resilience, and digital moral agency through ethical competence.
Multicentre Cross-Sectional Analytical Study
The study assessed AI literacy, mental resilience and ethical competence and examined their interrelationships among clinical-phase medical students.
Southwestern Nigeria
Participants were drawn from 13 universities across the Southwest geopolitical zone of Nigeria, representing federal, state-owned and private medical schools.
Clinical Years 4–6
Eligible participants were undergraduate medical students in Years 4 to 6 who had commenced clinical rotations and patient-care activities.
256 Medical Students
A total of 256 clinical-phase medical students participated in the study.
Convenience & Voluntary Response
A non-probability sampling approach combining convenience and voluntary response sampling was used.
Cronbach’s α = 0.822
The final six-item AI literacy scale demonstrated good internal consistency following pre-testing and exploratory factor analysis.
CD-RISC-10
Mental resilience was measured using the validated 10-item Connor-Davidson Resilience Scale. Reliability in the study sample was excellent, with Cronbach’s α = 0.905.
Cronbach’s α = 0.669
The AI-related ethical competence scale addressed accountability, patient consent and autonomy, data privacy, and integration of algorithmic recommendations with clinical judgment.
SPSS Version 27
Descriptive statistics, group comparisons, correlation analyses and multiple linear regression were used. Statistical significance was set at p < 0.05.
Institutional Ethical Approval
Ethical approval was obtained from the Institutional Research Ethics Committee of the University of Medical Sciences Teaching Hospital, Ondo, and relevant ethics boards of participating institutions. Informed consent was obtained from participants, and anonymity and confidentiality were maintained.
Contributor Roles
EFO conceived the study. OOO developed the methodology and drafted the introduction. OTO and IO conducted the literature review. OEA performed the statistical analysis. EFO synthesized the findings and drafted the discussion and conclusion. IDO supervised the study.
Conflict of Interest & Funding
Conflict of Interest: No conflict of interest.
Funding: No funding received.
Editorial Timeline
CC BY 4.0
This article is published under the Creative Commons
Attribution 4.0 International licence.
DOI: 10.64573/torgj2605002
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