AI literacy and AI anxiety in emergency and first aid students: a cross-sectional study

This study was conducted to determine Artificial Intelligence (AI) literacy and AI anxiety levels in students attending the First Aid and Emergency Program, to analyze the relationship between these variables, and to identify significant predictors of AI literacy and AI anxiety. This descriptive and cross-sectional study was conducted with 430 students enrolled in the Emergency and First Aid Program in Konya province. Data were collected using the Personal Information Form, Artificial Intelligence Literacy Scale (AILS), and Artificial Intelligence Anxiety Scale (AIAS). Descriptive statistics, independent samples t-test, Pearson correlation analysis, and multiple linear regression analysis were performed to analyze the data. The mean scores the participating students obtained from the overall AILS and AIAS were 3.96 ± 0.60 and 3.00 ± 0.76, respectively. There was a significant, weak, and negative correlation between the AILS and AIAS scores ( r = − .24, p < .001). There was a negative correlation between the scores obtained from the overall AILS and the scores obtained from the learning anxiety ( r = − .38, p < .001) and AI configuration anxiety ( r = − .18, p = .004) dimensions of the AIAS. However, there was no significant correlation between the scores obtained from the AILS, and the scores obtained from the job replacement anxiety and sociotechnical blindness dimensions of the AIAS. The results of the multiple linear regression analysis demonstrated that of the participating students, those who did not follow AI-related news obtained lower scores from the AILS (β = − 0.19, p < .001). Variables such as being a Female (β = 0.22, p < .001), not following AI news (β = 0.11, p = .028), and not supporting the inclusion of AI courses in the curriculum (β = 0.10, p = .042) were associated with higher levels of AI anxiety. The weak negative correlation between AI literacy and AI anxiety suggests that higher levels of AI literacy may be associated with lower levels of AI anxiety. This finding suggests that strengthening content to support artificial intelligence literacy in the Emergency and First Aid Program curriculum could be considered. Specifically, it is considered that integrating educational content aimed at reducing learning anxiety in students and increasing their awareness of the functioning, limitations, and conscious use of AI systems into the curriculum might play a supportive role in managing students’ anxiety levels. Therefore, further longitudinal and quasi-experimental studies should be conducted to understand the nature of this relationship and the potential effects of AI training better.

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Publication Details

Journal
BMC Medical Education
Published
2026-08-14
DOI
https://doi.org/10.1186/s12909-026-10095-z
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
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article

AI literacy and AI anxiety in emergency and first aid students: a cross-sectional study

Saliha Özpınar, Derya KARABULUT, Mustafa Karabulut
BMC Medical Education
Artificial Intelligence in Healthcare and Education
article

AI literacy and AI anxiety in emergency and first aid students: a cross-sectional study

Saliha Özpınar, Derya KARABULUT, Mustafa Karabulut
article en

Abstract

This study was conducted to determine Artificial Intelligence (AI) literacy and AI anxiety levels in students attending the First Aid and Emergency Program, to analyze the relationship between these variables, and to identify significant predictors of AI literacy and AI anxiety. This descriptive and cross-sectional study was conducted with 430 students enrolled in the Emergency and First Aid Program in Konya province. Data were collected using the Personal Information Form, Artificial Intelligence Literacy Scale (AILS), and Artificial Intelligence Anxiety Scale (AIAS). Descriptive statistics, independent samples t-test, Pearson correlation analysis, and multiple linear regression analysis were performed to analyze the data. The mean scores the participating students obtained from the overall AILS and AIAS were 3.96 ± 0.60 and 3.00 ± 0.76, respectively. There was a significant, weak, and negative correlation between the AILS and AIAS scores ( r = − .24, p < .001). There was a negative correlation between the scores obtained from the overall AILS and the scores obtained from the learning anxiety ( r = − .38, p < .001) and AI configuration anxiety ( r = − .18, p = .004) dimensions of the AIAS. However, there was no significant correlation between the scores obtained from the AILS, and the scores obtained from the job replacement anxiety and sociotechnical blindness dimensions of the AIAS. The results of the multiple linear regression analysis demonstrated that of the participating students, those who did not follow AI-related news obtained lower scores from the AILS (β = − 0.19, p < .001). Variables such as being a Female (β = 0.22, p < .001), not following AI news (β = 0.11, p = .028), and not supporting the inclusion of AI courses in the curriculum (β = 0.10, p = .042) were associated with higher levels of AI anxiety. The weak negative correlation between AI literacy and AI anxiety suggests that higher levels of AI literacy may be associated with lower levels of AI anxiety. This finding suggests that strengthening content to support artificial intelligence literacy in the Emergency and First Aid Program curriculum could be considered. Specifically, it is considered that integrating educational content aimed at reducing learning anxiety in students and increasing their awareness of the functioning, limitations, and conscious use of AI systems into the curriculum might play a supportive role in managing students’ anxiety levels. Therefore, further longitudinal and quasi-experimental studies should be conducted to understand the nature of this relationship and the potential effects of AI training better.

BMC Medical Education
Selçuk University (TR), Necmettin Erbakan University (TR), Alanya University (TR)
Quality Education
Openalex Percentile: Top 14%
Artificial Intelligence in Healthcare and Education
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