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Ethical Use of Artificial Intelligence in Engineering Education: A Systematic Review

● high · 2026-08-17 · engineering-education, ethics, academic-integrity, bias-mitigation, equity-in-ai-education, faculty-development, higher-ed, generative-ai

Synthesis: Osunbunmi and colleagues (2026) present a PRISMA-guided systematic review of empirical studies on the ethical use of AI in undergraduate engineering education, moving beyond conceptual discussions of AI ethics to examine how ethical guidance is actually enacted in instructional practice. From 2,158 records they retained 99 empirical studies, coding them for core ethical principles and identifying seven recurring forms of ethical guidance: transparency and disclosure of AI use; faculty–student accountability and human oversight; designing for student independence and agency; privacy and data protection; Academic Integrity and authorship; fairness, equity, and bias mitigation; and beneficence. The review finds a consistent pattern — ethical AI guidance in engineering education is predominantly student-facing and compliance-oriented (centered on academic integrity and disclosure), while reciprocal accountability for faculty AI use and institutional responsibility remains comparatively underdeveloped — and calls for a more balanced, practice-oriented approach to responsible AI integration.

From policy to practice

The review responds to a shift in conversations about AI in engineering education from how AI can improve educational processes to how ethical governance shapes its responsible use. Prior reviews often synthesized policy documents, conceptual frameworks, or literacy interventions; this study instead focuses specifically on empirical studies of AI use in undergraduate engineering classrooms, using deductive coding of core ethical principles combined with inductive thematic analysis.

Method

Following PRISMA 2020, the authors searched eight databases (APA PsycInfo, ProQuest, ERIC, Engineering Village, Education Research Complete, ScienceDirect, Scopus, Web of Science). Inclusion criteria required empirical, peer-reviewed studies of AI use in undergraduate engineering education (2000–2025) that addressed ethical considerations explicitly or implicitly. The database search yielded 2,158 records; after deduplication and blind screening, 99 studies were retained for ethical analysis.

Seven recurring forms of ethical guidance

Thematic analysis identified seven recurring forms of ethical guidance in engineering education:

  1. Transparency and disclosure of AI use — openly communicating when and how AI is used.
  2. Faculty–student accountability and human oversight — taking responsibility for decisions and outcomes.
  3. Designing for student independence and agency — preserving students' own problem-solving and critical thinking.
  4. Privacy and data protection — safeguarding sensitive educational data.
  5. Academic integrity and authorship — ensuring authenticity of student work.
  6. Fairness, equity, and bias mitigation — treating learners equitably and avoiding AI bias.
  7. Beneficence — acting to do good and avoid harm.

Across the 99 studies, accountability (n = 47) and transparency (n = 34) were the most emphasized core aspects, while autonomy (n = 12) and beneficence (n = 10) were the least emphasized; integrity (n = 49) was prominent enough to be treated as a distinct theme.

A student-facing, compliance-oriented imbalance

The central finding is that ethical AI guidance in engineering education is predominantly student-facing and compliance-oriented — often centered on academic integrity and disclosure requirements — while reciprocal accountability for faculty AI use and institutional responsibility remains underdeveloped. The authors interpret this as a limitation: because engineering decisions directly affect public safety, infrastructure reliability, environmental sustainability, and societal wellbeing, ethical AI use in engineering classrooms is a matter of professional formation and societal responsibility, not merely academic policy. They document the need to make ethical guidance more balanced across students, faculty, and institutions, and to support Faculty Development so educators can guide responsible AI integration.

Implications

  • Ethical guidance must cover faculty and institutions, not only students: integrity-focused, student-facing rules leave faculty AI use and institutional responsibility unaddressed.
  • Preserve human agency and independence: designing for student independence and agency is the least-emphasized ethical dimension but is central to protecting core engineering skills (problem-solving, critical thinking).
  • Ground ethics in engineering's professional stakes: the review frames ethical AI use in engineering as professional formation tied to public safety and societal impact.
  • Balance principle with practice: the field needs more empirical attention to how ethical principles are operationalized in classrooms, not just articulated in policy.

Connected Concepts

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Citation

Osunbunmi, I. S., Moyaki, D., Fakiyesi, V. O., Dunmoye, I. D., Nwanua, M. I., Oloyede, M., Bamidele, B. R., Feyijimi, T. R., & Hunsu, N. (2026). Ethical Use of Artificial Intelligence in Engineering Education: A Systematic Review. ASEE Annual Conference & Exposition, Paper ID #52865.