Ethical Literacy in Computing Education under the Pressure of Generative AI: A Mixed-Methods Study of Students and Educators in Brazil
DOI:
https://doi.org/10.5753/jserd.2026.7709Keywords:
Ethical literacy, Generative AI, AI ethics, computing education, mixed-methods study, curriculum integration, Software Engineering educationAbstract
Context The rapid expansion of Artificial Intelligence (AI), particularly Generative AI, has intensified ethical challenges in computing education, exposing tensions between technical training and ethical responsibility. While ethical principles are widely endorsed in international AI governance frameworks, their effective integration into computing curricula remains uneven, especially in Global South contexts. Goal This study investigates the ethical literacy of undergraduate computing students and examines how educators perceive their role, institutional constraints, and pedagogical challenges in fostering ethics education under the pressure of Generative AI. Method We adopted a sequential explanatory mixed-methods design. First, a survey with 56 undergraduate computing students at the University of Brasília (UnB) examined familiarity with 21 AI ethical principles, perceived relevance, and curricular exposure. Second, semi-structured interviews with 31 computing educators explored how ethics integration has evolved, the impact of Generative AI on teaching practices, perceived student ethical maturity at graduation, and structural barriers to effective ethics education. Quantitative data were analyzed descriptively, and qualitative data were examined through iterative coding procedures. \textbf{Results}: Students demonstrated strong agreement regarding the importance of ethical principles in computing and reported high self-perceived preparedness to assess social impacts. However, a clear gap emerged between perceived importance and formal curricular exposure. Educators, in turn, identified Generative AI as an operational accelerator that intensified assessment reconfiguration, academic integrity concerns, and accountability tensions. Interviews also revealed a dominant perception of low ethical maturity at the transition to professional practice, alongside systemic barriers including curricular overload, limited faculty preparation, and institutional governance gaps. Conclusions: The findings suggest the existence of a structural ethical literacy gap in computing education, reinforced by curricular fragmentation and insufficient institutional support, and amplified by the rapid integration of Generative AI. Addressing this gap requires coordinated curricular reform, faculty development, and institutional governance strategies that integrate ethical reasoning as a core technical competency rather than a peripheral concern. This competency is particularly important for future software engineers who must evaluate AI-generated artifacts, assume accountability for AI-supported engineering decisions, and negotiate socio-technical trade-offs throughout the software lifecycle.
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