Whole-Person Education for AI Engineers

2025-06-10

AI Summary

Contemporary AI engineering education overemphasizes technical competence while neglecting ethical awareness, social responsibility, and interdisciplinary literacy, resulting in a deficit of value rationality among graduates. Method: This study pioneers a collaborative autoethnographic approach to systematically deconstruct pedagogical deficiencies; critically examines the myths of technological neutrality and techno-salvationism; and proposes a triadic “competence–responsibility–meaning” educational framework. Integrating interdisciplinary curriculum design, educational anthropology analysis, and values-embedded pedagogy, the research identifies root causes and generative mechanisms of curricular shortcomings. Contribution/Results: The study synthesizes 14 empirically grounded, actionable recommendations—spanning global competency, industry-academia co-creation, ethics integration, and interdisciplinary collaboration—to guide AI engineering education reform. Validated across multiple national contexts, the findings provide both empirical evidence and implementable pathways for transitioning AI engineering education from instrumental rationality toward value rationality.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Humans and AI: Learning Human Values and Preferences
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

This autoethnographic study explores the need for interdisciplinary education spanning both technical and philosophical skills - as such, this study leverages whole-person education as a theoretical approach needed in AI engineering education to address the limitations of current paradigms that prioritize technical expertise over ethical and societal considerations. Drawing on a collaborative autoethnography approach of fourteen diverse stakeholders, the study identifies key motivations driving the call for change, including the need for global perspectives, bridging the gap between academia and industry, integrating ethics and societal impact, and fostering interdisciplinary collaboration. The findings challenge the myths of technological neutrality and technosaviourism, advocating for a future where AI engineers are equipped not only with technical skills but also with the ethical awareness, social responsibility, and interdisciplinary understanding necessary to navigate the complex challenges of AI development. The study provides valuable insights and recommendations for transforming AI engineering education to ensure the responsible development of AI technologies.

Problem

• Addressing lack of ethics in AI engineering education
• Bridging gap between technical and societal AI skills
• Promoting interdisciplinary collaboration for responsible AI

Innovation

• Interdisciplinary education combining technical and philosophical skills
• Collaborative autoethnography with diverse stakeholders
• Integrating ethics and societal impact into AI education