Sex and Prior AI Knowledge as Determinants of University Students' Perceptions, Attitudes, and Career Aspirations Regarding Artificial Intelligence: A Comparative Cross-Sectional Analysis
Keywords:
artificial intelligence, gender differences, prior knowledge, university students, AI literacy, comparative study, career aspirations, AI ethicsAbstract
Background: Artificial intelligence (AI) is rapidly reshaping higher education, yet how individual characteristics — particularly gender and prior AI knowledge — influence students' perceptions, attitudes, and career intentions remains poorly understood, especially in resource-constrained educational contexts.
Objectives: To examine whether gender, prior AI knowledge, and age group are independently associated with differences in students' perceived impact of AI across societal domains, their overall attitudes, curriculum integration preferences, and ethical concerns.
Methods: A cross-sectional comparative survey was administered to 1,122 undergraduate students. Perceived AI impact across six Likert-scaled domains was compared by gender (Mann-Whitney U test) and prior knowledge (Mann-Whitney U). Categorical outcomes (attitudes, career intentions, ethical awareness) were compared using Pearson chi-square tests. Age-group differences in Likert domains were examined with the Kruskal-Wallis test. Statistical significance was set at p < 0.05.
Results: No significant gender differences were observed in any of the six Likert-scaled AI impact domains (all p > 0.05). However, male students were significantly more likely to consider an AI career (44.4% vs. 30.4%; p < 0.001), believe AI can replace human jobs (49.7% vs. 37.0%; p < 0.001), and acknowledge AI ethical challenges (50.1% vs. 42.7%; p = 0.018). Prior AI knowledge exerted a broader and more consistent influence: students with prior knowledge rated AI impact on education and healthcare significantly higher (both p < 0.001), held more positive overall attitudes (57.9% vs. 42.4%; p < 0.001), and more strongly supported curriculum integration (74.8% vs. 60.7%; p < 0.001). Notably, prior knowledge did not influence ethical awareness or perceptions of AI-related inequality. Age group was associated with perceived AI impact only on social media (H = 17.54; p < 0.001) and data privacy (H = 10.68; p = 0.005).
Conclusions: Gender and prior AI knowledge shape distinct, non-overlapping facets of students' AI perceptions. Gender influences career intentions and ethical acknowledgement but not domain-specific impact ratings. Prior knowledge broadly enhances AI literacy, positive attitudes, and educational support but leaves ethical consciousness unchanged — underscoring the need for dedicated AI ethics education independent of technical knowledge acquisition.
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