Computing Students’ Perceptions of Ethical Principles in AI and Curricular Coverage

• Felippe Kipman UnB
• Gilmar dos Santos Marques UnB
• Edna Dias Canedo UnB https://orcid.org/0000-0002-2159-339X

DOI: https://doi.org/10.5753/sbqs.2025.13611

Abstract

Context: As artificial intelligence (AI) systems become increasingly integrated into society, ensuring that computing professionals are equipped with ethical competencies is a growing concern. While many international guidelines emphasize ethical principles in AI development, their integration into computing curricula remains inconsistent, especially in the Brazilian context. Goal: This study investigates computing students’ perceptions of ethical principles in AI, their self-assessed preparedness to apply them, and the presence of ethics-focused disciplines in undergraduate curricula. Method: We conducted a survey with 56 undergraduate computing students at the University of Brasília (UnB). The questionnaire included Likert-scale items on students’ familiarity with 21 AI ethical principles, their perceived importance, and whether these topics were covered during their studies. Descriptive statistics were used to analyze the results, and we discuss them in light of related literature. Results: Students overwhelmingly perceive ethical principles as important and report high agreement with their relevance to professional practice. However, a significant gap exists between perceived importance and curricular exposure: although most students agree on the need to learn ethical principles during their studies, a substantial number report not having had a dedicated course on the topic. Some principles, such as Privacy, Justice, and Transparency, were rated both highly important and well-understood, while others like Prosperity and Solidarity showed lower familiarity. Conclusions: The findings highlight the urgent need to integrate dedicated ethics courses into computing curricula. Ethical literacy is important not only for technical competency but also for responsible decision-making in AI development. Future work includes extending the study to other institutions and exploring pedagogical strategies for teaching AI ethics in computing programs.

Keywords: ethics education, embedded ethics, ethics course, higher education, interdisciplinary teaching and learning

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