Integrating Ethical, Human-Centered, and Responsible AI in Outcome-Based Engineering Education: Ethics and Environmental Protection Course

Abstract

The fast adaptation of Artificial Intelligence (AI) in the education system places significant weight on ethical, human-centered, and accountable procedures in engineering program courses. This article reports an Outcome-Based Education (OBE)-associated pedagogical context for embedding ethical AI principles into the Engineering Ethics and Environmental Protection course of an undergraduate electrical and electronic engineering program. The suggested methodically combines cognitive and affective domain Course Outcomes (COs) with Program Outcome (PO) 8 requirements; mainly, this PO is suggested by the Board of Accreditation for Engineering and Technical Education (BAETE), focusing on engineering ethics, public safety, and social duty. The course is mainly designed to integrate case study-based student learning and structured presentations driven by worldwide documented professional and ethical codes of conduct, viz. IEEE, NSPE, and IEB. Students engage in both real-life and orchestrated ethical cases related to environmental compliance, public safety, public health, and sustainability issues, involving them in investigating and resolving those cases. Such activities substitute a human-centered perspective by emphasizing the societal, environmental, and cultural implications of engineering verdicts, mainly in AI-driven systems where accountability and transparency are critical concerns. Assessment tactics are aligned with COs, evaluating both cognitive insight and affective characteristics, such as ethical rationalizing, professional liability, and worthy decision-making. Tools that are used for evaluating student learning outcomes include rubric-based evaluation of case studies, oral presentations targeting affective domain assessment, and systematic reports that quantify students’ aptitude towards the application of ethical codes, assessing risks, and proposing sustainable engineering solutions. The incorporation of open-ended problem-solving further enhances critical thinking and reflective judgment, essential for responsible AI development and deployment. The framework highlights the importance of environmental ethics, linking AI applications with sustainability goals and emphasizing engineers’ responsibility toward ecological preservation and public welfare. By combining theory, practice, and ethical reflection, the model ensures that graduates are not only technically competent but also socially responsible with ethics. This study demonstrates that integrating ethical AI concepts through structured OBE approaches and case-based learning significantly enhances learners’ preparedness to address complex and real-world challenges. The approach is scalable and adaptable across engineering disciplines, contributing to the development of ethical standards in the AI-driven transformation era. At the end of a semester, all OBE data are compiled by collecting them from all course teachers and then evaluated to compute the attainment of COs.