Exploring Ethical Perceptions of AI Among University Students in Urban Areas

Thesis Abstract

The rapid integration of artificial intelligence (AI) technologies into daily life has raised pressing ethical concerns, particularly among young adults in urban settings who are digital natives and primary consumers of technological innovations. This study investigates the ethical perceptions of AI among university students in metropolitan areas, aiming to elucidate how young adults understand, interpret, and evaluate the ethical implications of AI deployment across various domains such as healthcare, education, privacy, and employment. The specific objectives include (1) identifying the key ethical issues related to AI perceived by students, (2) examining differences in ethical perceptions based on demographic variables such as gender, academic discipline, and technological proficiency, and (3) exploring the influence of ethical beliefs on students' attitudes towards AI adoption and regulation. Employing a mixed-methods research design, the study integrates quantitative surveys with qualitative focus group discussions to provide a comprehensive understanding of students’ perceptions. The quantitative component involves a structured questionnaire guided by the Theory of Planned Behavior and the Ethical Decision-Making Model, administered to a sample of 400 university students selected through stratified random sampling to ensure representation across faculties and academic years within a major metropolitan university. Data collection instruments include a validated Likert-scale survey measuring perceptions of AI ethics, confidence in AI decision-making, and support for regulatory frameworks. For qualitative insights, eight focus groups comprising 6–8 students each are conducted, with discussions transcribed and analyzed thematically to identify recurring ethical themes and narratives. Data analysis involves descriptive statistics and inferential techniques such as multiple regression analysis to determine predictors of ethical perceptions and Analysis of Variance (ANOVA) to identify differences across demographic groups. Thematic analysis, following Braun and Clarke’s methodology, is applied to qualitative data to extract emergent themes. The reliability of instruments is established via Cronbach’s alpha coefficients above 0.80, and validity is supported through expert review and pilot testing. Expected findings suggest that students perceive ethics in AI primarily through concerns about privacy, bias, transparency, and accountability, with significant variation observed based on demographic factors. It is anticipated that higher technological literacy correlates positively with nuanced ethical awareness, while gender differences may influence priorities and perceptions of risks. The integration of quantitative and qualitative insights aims to reveal the complex interplay between ethical knowledge and attitudes toward AI regulation, highlighting areas of ethical ambivalence and acceptance among urban youth. This research contributes to knowledge by providing empirical evidence on the ethical framing of AI among a key demographic segment—university students—and advancing understanding of how ethical perceptions shape attitudes toward emerging technologies. It offers theoretical enhancements by testing the applicability of the Theory of Planned Behavior and Ethical Decision-Making models within the context of AI ethics. In conclusion, the study emphasizes the importance of integrating ethical literacy into technology education to foster responsible AI development and use. Recommendations include developing targeted awareness campaigns, curricula incorporation of AI ethics, and policy dialogues involving youth perspectives. The findings also suggest avenues for further research into longitudinal changes in perceptions as AI technology evolves and matures, and comparative studies across different cultural and socio-economic contexts. The insights from this study aim to inform policymakers, educators, and technologists striving to align AI innovations with societal ethical standards.